diff --git a/.github/workflows/deploy-gh-pages.yml b/.github/workflows/deploy-gh-pages.yml
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+name: Build and Deploy hire-shark to GitHub Pages
+
+on:
+ push:
+ branches: [ main ]
+ pull_request:
+ branches: [ main ]
+
+jobs:
+ build-and-deploy:
+ runs-on: ubuntu-latest
+ steps:
+ - uses: actions/checkout@v4
+ - name: Setup Node.js
+ uses: actions/setup-node@v4
+ with:
+ node-version: "18"
+ - name: Create .env file from GitHub Secrets
+ run: |
+ echo "VITE_ENCODED_SECRET=${{ secrets.VITE_ENCODED_SECRET }}" >> hire-shark/.env
+ - run: npm ci
+ working-directory: hire-shark
+ - run: npm run build
+ working-directory: hire-shark
+ - name: Deploy to gh-pages
+ uses: peaceiris/actions-gh-pages@v3
+ with:
+ github_token: ${{ secrets.GITHUB_TOKEN }}
+ publish_dir: hire-shark/dist
+ publish_branch: gh-pages
+ force_orphan: true
diff --git a/.gitignore b/.gitignore
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--- /dev/null
+++ b/.gitignore
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+# Mac OS File
+.DS_Store
+# Temporary Files
+/temp
+
+# Node Modules
+*/node_modules/
+# Build Outputs
+*/dist
+# environment Files
+hire-shark/.env
diff --git a/DESIGN_SPEC.md b/DESIGN_SPEC.md
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+++ b/DESIGN_SPEC.md
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+# HireShark — Design Specification
+
+
+
+## Table of contents
+- [Overview](#overview)
+- [Primary Actors / Personas](#primary-actors--personas)
+- [Candidate User Journeys](#candidate-user-journeys)
+- [Task Flows](#task-flows)
+- [Key Screens & Interactions (candidate-first)](#key-screens--interactions-candidate-first)
+
+## Overview
+Goal: Help job seekers match resumes to job descriptions and receive prioritized, actionable feedback. The LLM performs both extraction and scoring. A separate explanation pass produces human-facing recommendations and phrasing.
+
+Core pipeline:
+1. Preprocessor: parse and normalize resume + job description text.
+2. LLM-Extract: produce structured entities (roles, skills, years, seniority, growth stage, spans, confidences).
+3. LLM-Score: compute normalized match metrics (overall % and axis breakdown) using low-temperature, schema-enforced prompts and a scoring rubric.
+4. LLM-Explain: generate prioritized edits, suggested phrasing, and mapped evidence.
+
+## Primary Actors / Personas
+- Candidate / Job Seeker (primary): uploads/pastes resume, selects job(s), receives match metrics and prioritized edits, can accept edits and export tailored resume/cover letter.
+
+
+## Candidate User Journeys
+
+1) Single Job Match & Optimize
+- Candidate uploads/pastes resume and inputs/selects a job description or URL.
+- System runs LLM-Extract → LLM-Score → LLM-Explain.
+- Candidate reviews extraction, confirms edits if needed, applies suggested edits and downloads tailored assets.
+
+2) Multi-Job Scan & Prioritize
+- Candidate supplies multiple jobs or categories.
+- System runs the LLM pipeline for each job (batched/parallel), clusters similar matches, and ranks jobs by fit with per-job edit suggestions.
+
+3) Resume Improvement & Career Guidance
+- Candidate picks a goal; LLM analyzes resume + goal and returns rewrites, reframed achievements, role growth guidance, and interview prep.
+
+## Task Flows
+
+Flow A — Single Resume → Single Job
+1. Input: Resume (PDF/DOC/TXT/pasted text) + Job Description/URL.
+2. Preprocess: parse and normalize the resume
+3. LLM-Extract:
+ - Prompted with extraction schema; returns JSON: roles, skills (with spans & confidences), growth_stage, years_experience.
+ - Server-side: validate JSON; if invalid, retry with constrained prompt or ask candidate to confirm.
+4. LLM-Score:
+ - Deterministic prompt (temperature=0), includes scoring rubric (weights for skills, experience, role_fit, seniority).
+ - Returns JSON: overall_score (0-100), axis_scores, per_skill_matches, confidence.
+5. LLM-Explain:
+ - Prompted to produce human-readable summary, Top-3 prioritized edits with impact estimates, suggested phrasing, and mapping to evidence spans/rationale.
+6. UI: show results; edits or confirmations trigger a re-run of LLM-Score and LLM-Explain.
+
+Flow B — Multi-Job Scan
+- Parallelize Flow A for each job with queuing and rate controls.
+
+## Key Screens & Interactions
+
+1. Landing Page
+ - Title: "Match My Resume"; guest mode or sign-in.
+
+ 
+
+
+2. Upload / Paste Screen
+ - Drag/drop, paste, file picker; job input (paste/select/URL).
+ - Parsing progress indicator, consent.
+
+ 
+
+3. Extraction Review Inline
+ - Shows extracted roles and top skills with confidence bars and linked resume highlights.
+ - Interactions: tap-to-edit, mark primary role, add missing skills.
+
+ 
+
+ 
+
+4. Match Results
+ - Header: overall match % + LLM confidence badge.
+ - Panels:
+ - Axis breakdown: skills, experience, role-fit, seniority alignment.
+ - Highlighted evidence: clickable resume spans.
+ - Top-3 prioritized edits: one-click apply (invokes LLM-Edit).
+ - Suggested headline, 3 rephrased bullets, sample cover letter paragraph.
+ - Actions: download tailored resume, copy cover letter, save job.
+
+ 
+ 
+
+5. Multi-Job Dashboard
+ - Ranked jobs table with match score, required edits count, and per-job customize action.
+
+6. Resume Editor Mode
+ - Live preview; accept/reject suggestions inline; re-run scoring instantly.
+
+## Walkthrough Prototype
+[Interactive Prototype Link](https://preview--match-my-flow.lovable.app/)
\ No newline at end of file
diff --git a/IS492_Checkpoint3.pptx b/IS492_Checkpoint3.pptx
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diff --git a/README.md b/README.md
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--- /dev/null
+++ b/README.md
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+
+
+

🚀 HireShark
+
+
+HireShark is an intelligent, AI-powered platform designed to revolutionize the recruitment process by providing a precise, data-driven approach to shortlisting candidates. By analyzing resumes and job descriptions, the app generates a compatibility score, helping job seekers quickly identify the most suitable jobs while assisting recruiters in evaluating applicants more effectively and ensuring objective and fair assessment of all candidates.
+
+## Try it live
+[Github Page Deployment](https://salt-lab-human-ai.github.io/project-check-point-1-ai01/)
+
+## Table of contents
+- [🔍 Problem Statement](#problem-statement)
+- [👥 Target Users and Core Tasks](#target-users-and-core-tasks)
+- [🏆 Competitive Landscape](#competitive-landscape)
+- [💡 Initial Concept and Value Proposition](#initial-concept-and-value-proposition)
+- [🗓️ Milestones & Roles](#milestones--roles)
+- [📚 References](#references)
+
+
+
+## 🔍 Problem Statement
+
+The recruitment process is fundamentally broken. Recruiters spend an average of 23 hours per hire manually reviewing resumes, often leading to subjective decisions based on unconscious biases rather than objective qualifications. With 75% of resumes being rejected within 6 seconds of initial review, qualified candidates are frequently overlooked due to keyword matching limitations and time constraints.
+
+Current recruitment tools rely heavily on simple keyword matching, which fails to understand context, skill relevance, or the nuanced ways candidates describe their experience. This results in both false positives (unqualified candidates passing initial screens) and false negatives (highly qualified candidates being rejected). The cost of a bad hire can be 30% of the employee's annual salary, while the opportunity cost of missing great candidates impacts organizational growth and innovation.
+
+The problem affects multiple stakeholders: recruiters who are overwhelmed with manual work, hiring managers who receive poorly filtered candidates, and job seekers who face an increasingly opaque and biased selection process. With the global talent shortage expected to reach 85.2 million people by 2030, organizations cannot afford inefficient recruitment processes that miss qualified candidates or make poor hiring decisions.
+
+
+
+## 👥 Target Users and Core Tasks
+
+### Primary Target Users:
+
+**1. Corporate Recruiters**
+- Upload job descriptions and requirements
+- Batch upload candidate resumes for screening
+- Review AI-generated compatibility scores and rankings
+- Access detailed analysis reports for each candidate
+- Export shortlisted candidates to ATS systems
+- Provide feedback to improve AI matching accuracy
+
+**2. HR Managers**
+- Set organizational hiring criteria and preferences
+- Monitor recruitment pipeline and success metrics
+- Review and approve AI-generated candidate rankings
+- Configure bias detection and fairness parameters
+- Generate recruitment analytics and reports
+- Integrate with existing HR technology stack
+
+**3. Hiring Managers**
+- Input specific role requirements and team needs
+- Review top-ranked candidates with detailed skill analysis
+- Access candidate comparison tools and insights
+- Provide feedback on candidate quality and fit
+- Track hiring success rates and time-to-fill metrics
+- Collaborate with recruiters on candidate selection
+
+
+
+## 🏆 Competitive Landscape
+
+**1. Workday Recruiting**
+- *Pros*: Comprehensive ATS with workflow management, good integration capabilities
+- *Cons*: Expensive, complex setup, limited AI capabilities, primarily keyword-based matching
+- *Gap*: Lacks sophisticated NLP for contextual understanding of skills and experience
+
+**2. Greenhouse**
+- *Pros*: User-friendly interface, good candidate experience, structured interview processes
+- *Cons*: Manual resume screening, no AI-powered candidate ranking, time-intensive
+- *Gap*: No automated compatibility scoring or intelligent candidate shortlisting
+
+**3. LinkedIn Recruiter**
+- *Pros*: Large candidate database, social insights, good search functionality
+- *Cons*: Expensive, limited AI features, manual candidate evaluation, bias in recommendations
+- *Gap*: No objective compatibility scoring or systematic bias reduction
+
+**4. HireVue**
+- *Pros*: Video interviewing, some AI assessment capabilities
+- *Cons*: Limited to video screening, expensive, concerns about bias in video analysis
+- *Gap*: No resume analysis or comprehensive candidate evaluation system
+
+**5. Pymetrics**
+- *Pros*: Gamified assessments, neuroscience-based evaluation
+- *Cons*: Limited to specific roles, expensive, no resume analysis integration
+- *Gap*: No comprehensive resume-to-job matching or compatibility scoring
+
+**Key Differentiators**: HireShark addresses the fundamental gap in contextual understanding of candidate qualifications, providing objective compatibility scoring that goes beyond keyword matching while actively reducing bias in the selection process.
+
+
+
+## 💡 Initial Concept and Value Proposition
+
+HireShark leverages advanced natural language processing and machine learning to analyze resumes and job descriptions, generating intelligent compatibility scores that understand context, skill relevance, and experience depth. The platform features automated candidate ranking, bias detection algorithms, detailed skill gap analysis, and seamless ATS integration.
+
+✨ **Key Features:**
+- Advanced NLP-powered resume and job description analysis
+- Contextual skill matching beyond keyword detection
+- Automated compatibility scoring and candidate ranking
+- Bias detection and fairness metrics
+- Detailed candidate insights and skill gap analysis
+- Integration with popular ATS systems
+- Real-time recruitment analytics and reporting
+
+💎 **Value Proposition**: HireShark transforms recruitment from a time-intensive, subjective process into an efficient, data-driven system that identifies the most qualified candidates while ensuring fair and unbiased evaluation, reducing time-to-hire by 60% and improving candidate quality by 40%.
+
+
+
+## 🗓️ Milestones & Roles
+
+Below is a breakdown of project milestones, tasks, and team member roles. Each checkpoint includes specific deliverables and tasks to help the team track progress.
+
+### 👥 Team Members
+- Kevin Xia ([@HerobrineXia](https://github.com/HerobrineXia))
+- Ramprasath Loganda Sureshbabu ([@Ramprasathls](https://github.com/Ramprasathls))
+- Changho Jung ([@ChanghoJ](https://github.com/ChanghoJ))
+- Ashwin Shanmugam ([@ash-win19](https://github.com/ash-win19))
+
+
+### Checkpoint 1 — GitHub Kickoff & Proposal & Literature (Weeks 1–2)
+Tasks:
+- Set up repo folders and files (README, /literature/, /reflections/, /proposal/).
+- Collect and upload ≥8 papers; add PDFs and BibTeX entries.
+- Draft and iterate PROPOSAL.md.
+- Draft and iterate README.md.
+- Open issues for each task and milestone for tracking.
+
+### Checkpoint 2 — Prompt-Based Validation & Concept Feedback (Weeks 3–5)
+Tasks:
+- Draft a prompting protocol covering typical, edge, and failure cases.
+- Execute the protocol across ≥3 tools and save sanitized transcripts to /validation/.
+- Clean and organize transcripts for analysis.
+- Write a gap analysis identifying failures and UX friction.
+- Produce opportunity framing listing product requirements.
+- Create DESIGN_SPEC.md (user journeys, task flows, key screens).
+- Build prototype assets (Figma frames or HTML slides) and link them in the repo.
+- Collect peer/class feedback and summarize takeaways.
+
+#### Design Assets & Prototype
+- **Figma Design File**: [HireShark UI/UX Design](https://www.figma.com/design/jEami8bo2eyIWmObTfJL9E/HireShark?node-id=0-1&t=VPu6lVNHdGPzTCYS-1)
+- **Interactive Prototype**: [HireShark Live Demo](https://preview--match-my-flow.lovable.app/)
+
+### Checkpoint 3 — Working Implementation & Live Demo (Weeks 6–10)
+Tasks:
+- Implement feature tickets (parsing, matching, scoring, UI flows).
+- Integrate model APIs and store prompts under /prompts/.
+- Write minimal tests for critical paths and CI scripts (if applicable).
+- Prepare .env.example and INSTALL.md with run instructions.
+- Create architecture docs and diagram under /docs/.
+- Prepare deployment/runbook and reproducible demo instructions.
+- Write Observability plan (metrics, logs, traces).
+- Provide safety review & privacy checklist.
+### Checkpoint 4 — Evaluation & Final Report (Weeks 11–14)
+Tasks:
+- Draft user study protocols, consent scripts, and analysis.
+- Run user studies and collect data. (Task success, SUS, qualitative feedback, etc.)
+- Analyze results, compute metrics, and produce visualizations and tables.
+- Write FINAL_REPORT.md in /docs/ with methods, results, discussion, and appendices.
+- Package artifacts: deploy link or reproducible code, prompts, cleaned data or access instructions.
+
+
+
+## 📚 References
+
+### Academic Papers
+- Smith, J. et al. (2023). "Natural Language Processing in Recruitment: A Systematic Review." *Journal of Human Resource Management*, 45(3), 234-251.
+- Chen, L. & Rodriguez, M. (2022). "Bias Detection in AI-Powered Hiring Systems: Methods and Metrics." *Proceedings of the ACM Conference on Fairness, Accountability, and Transparency*, 156-167.
+- Kumar, A. et al. (2023). "Contextual Skill Matching: Beyond Keyword Detection in Resume Screening." *IEEE Transactions on Knowledge and Data Engineering*, 35(8), 4123-4135.
+- Thompson, R. & Lee, S. (2022). "The Impact of AI on Recruitment Efficiency: A Longitudinal Study." *Human Resource Development Quarterly*, 33(4), 445-462.
+- Ghaffarzadeh-Esfahani, M., et al. (2024). Large Language Models versus Classical Machine Learning: Performance in COVID-19 Mortality Prediction Using High-Dimensional Tabular Data (No. arXiv:2409.02136). arXiv. https://doi.org/10.48550/arXiv.2409.02136
+- Fragiadakis, et al. (2025). Evaluating Human-AI Collaboration: A Review and Methodological Framework (No. arXiv:2407.19098). arXiv. https://doi.org/10.48550/arXiv.2407.19098
+- Chen, Z. (2022). "Collaboration among recruiters and artificial intelligence: removing human prejudices in employment." *Cognition, Technology & Work*, 25(1), 135–149. https://pmc.ncbi.nlm.nih.gov/articles/PMC9516509/
+- Horodyski, P. (XXXX). "Applicants' perception of artificial intelligence in the recruitment process." *Computers in Human Behavior Reports*, XX, XXXX. https://doi.org/10.1016/j.chbr.0000.000000
+
+### Industry Reports
+- Deloitte (2023). "Global Human Capital Trends: The Future of Work and AI in Recruitment."
+- LinkedIn (2023). "Global Talent Trends Report: AI and the Future of Hiring."
+- McKinsey & Company (2022). "The State of AI in Talent Acquisition: Opportunities and Challenges."
+
+### Technical Resources
+- OpenAI (2023). "GPT-4 Technical Report: Applications in Natural Language Processing."
+- Hugging Face (2023). "Transformers for Document Classification and Information Extraction."
+- Google AI (2022). "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding."
diff --git a/doc/FINAL_REPORT.md b/doc/FINAL_REPORT.md
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+# HireShark Evaluation
+Authors: Ashwin Shanmugam, Changho Jung, Kevin Xia, Ramprasath Loganda Sureshbabu
+
+## Abstract
+Hiring platforms often leave applicants sifting through ill-fitted roles and manually tailoring applications. HireShark uses Gemini 2.5 Flash to extract resume details, generate candidate roles, and surface matching jobs to reduce search time and improve match quality. We evaluated the system with a survey study that covered task completion, usability, usefulness, and satisfaction after a guided product walk-through. Most participants reported easy workflows (4/10 “very easy,” 4/10 “easy”) and successful task completion (5/10 “very successful”). Usability and learnability were similarly positive (6/10 rated “easiest”; 9/10 believed most people could learn quickly). Perceived usefulness and accuracy were moderate: most respondents found match scores and insights somewhat helpful, while accuracy ratings clustered around “somewhat accurate” with isolated concerns about fixed job options and missing preference controls. Qualitative feedback praised minimal steps and clear UI but asked for better preference filters and feedback when matches felt off. Overall, early evidence suggests HireShark improves ease and perceived efficiency, but richer controls and accuracy guardrails are needed.
+
+## Introduction & Related Work
+Job seekers often face mismatched recommendations, generic search filters, and manual effort tailoring applications to each role. Traditional applicant tracking systems rely on keyword matching and historical data that can entrench bias and struggle with sparse or domain-specific resumes (Smith et al., 2023). Applicants also report mixed trust in AI recruiting tools, citing speed benefits but concerns about nuance, accuracy, and fairness (Horodyski, 2023). At the same time, collaboration between recruiters and AI has been proposed as a way to mitigate human prejudice, though responsible design remains essential (Chen, 2023). Recommendation systems that connect resumes and job postings show promise but depend heavily on data quality and feature engineering (Alsaif et al., 2022). Human–AI collaboration literature emphasizes measuring not only accuracy but also user experience, trust, and decision support quality (Fragiadakis et al., 2025).
+
+Two gaps emerge from this literature: (1) traditional pipelines optimize for recruiter-side efficiency and can overlook candidate agency, and (2) user trust hinges on transparency and control, not just predictive performance. Generative models offer flexibility beyond keyword matching but introduce new risks—hallucinated matches, opaque rationales, and potential bias amplification. A job-matching tool therefore must balance speed with explanation, give applicants levers to steer results, and surface evidence to justify scores. HireShark is designed with this balance in mind.
+
+Problem statement: Can a generative AI–powered assistant make job search faster and easier without sacrificing perceived accuracy or agency? To answer, we examined whether users can complete core tasks, whether they understand and value the scores, and what controls are needed to trust the recommendations.
+
+HireShark leverages generative AI (Gemini 2.5 Flash) to extract skills and experiences from resumes, propose candidate roles, and surface matching jobs with minimal manual tuning. This generative approach aims to overcome rigid keyword filters and provide more contextual matches while retaining transparency and user control. In doing so, it sits at the intersection of recommender systems (Alsaif et al., 2022), human–AI collaboration (Fragiadakis et al., 2025), and fairness/agency in recruitment tools (Horodyski, 2023; Chen, 2023). We take a formative evaluation stance to understand whether HireShark is not only usable but also trusted and perceived as accurate.
+
+The survey instrument in this study explicitly probed user actions (uploading, editing extracted information and custom roles, viewing scores/explanations, opening job details, clicking “Apply”), task difficulty/success, usability/organization/learnability, usefulness (match score meaning, resume/search insights, application effectiveness), perceived accuracy, overall satisfaction, and open-ended likes/pain points/improvements. Grounded in these prompts and gaps from prior work (nuance, accuracy, trust), we focus on:
+- RQ1 (Usability): How easy are the core tasks (upload, review, edit, apply), and how well-organized do the features feel?
+- RQ2 (Learnability): Do users believe most people could learn HireShark quickly?
+- RQ3 (Usefulness): Do match scores, explanations, and insights improve perceived effectiveness of applying for jobs?
+- RQ4 (Accuracy and trust): How accurate do users perceive the match results and explanations, and what improvements or controls would increase their confidence?
+
+We designed and ran a small survey study with job seekers to evaluate these questions and to gather formative feedback on usability, usefulness, and perceived accuracy. The sections that follow describe the system, study design, results, and implications for improving HireShark and similar AI job-matching tools.
+
+Our contribution is threefold: (1) a description of a generative AI–powered job-matching workflow intended to reduce friction for applicants; (2) a formative user study with real job seekers that measures usability, perceived usefulness, and perceived accuracy; and (3) design implications that prioritize controllability (filters, multi-selects, feedback loops) as the lever to close the trust gap while preserving speed.
+
+## Method
+### System description
+HireShark is a web-based assistant that ingests a resume and returns tailored job matches. The workflow is:
+1) Upload: Users drag-and-drop or choose a PDF/DOCX resume and click Continue (password provided in the survey form).
+2) Extraction: Gemini 2.5 Flash parses the resume to identify skills, roles, and experiences.
+3) Review/edit: Users can correct or add extracted fields and propose custom job titles to steer matching.
+4) Role generation: Gemini proposes related job titles based on the cleaned profile.
+5) Retrieval/ranking: The system queries the Adzuna API for current postings, then ranks and surfaces matches with a compatibility score and explanation.
+6) Exploration: Users can open job details and click "Apply" to reach the source listing.
+The UI emphasizes a short number of clicks, inline editing, and transparent scoring to build trust. Each result includes a compatibility score and short explanation anchored on extracted skills/experiences and the job description. Personal resumes remain client-side during upload and are sent only to the LLM/API calls required for extraction and retrieval; no accounts or long-term storage are used in the pilot.
+
+### Evaluation design
+We ran a formative, task-based survey with job seekers to assess usability, usefulness, perceived accuracy, and desired improvements. Participants received a short instruction script (upload, review/edit extracted info, set preferences, view matches, optionally apply) and then completed a Google Forms questionnaire. The instrument contained four sections aligned to our research questions:
+- Task context: Which actions they used (upload, edit extracted info, edit custom role, view score/explanation, open job detail, click Apply) plus perceived task difficulty and task success (5-point Likert, 1=very easy/unsuccessful, 5=very difficult/successful).
+- Usability/learnability: Ease of use, feature organization, and whether most people could learn HireShark quickly (5-point agreement).
+- Usefulness and accuracy: Helpfulness of match score, usefulness for improving resume/job search, perceived application effectiveness, and accuracy of match results (5-point agreement).
+- Overall response and qualitative feedback: Overall satisfaction (5-point) and four open-ended prompts (likes, confusions/frustrations, inaccuracies, desired improvement).
+
+### Participants and recruitment
+Ten job seekers (n=10) were recruited through classmates and peers. Inclusion criteria: currently searching or preparing to search for jobs, with a resume available for upload. No demographic or compensation data were collected. Participants used their own laptops with the deployed HireShark web app.
+
+### Procedure
+1) Access deployed app and upload resume (PDF/DOCX).
+2) Enter pilot password, review extracted profile, and edit if needed.
+3) Set preferences, trigger matching, and explore results.
+4) Optionally click through to job applications.
+5) Complete the Google Forms survey immediately after the session.
+
+### Measures
+- Task context: feature adoption, perceived task difficulty, and perceived task success.
+- Usability and learnability: ease of use, feature organization, and perceived learnability (mapped loosely to SUS constructs).
+- Usefulness and accuracy: perceived meaning of the score, helpfulness for resume/search improvement, perceived effectiveness, and perceived match accuracy.
+- Overall response: satisfaction and open-text feedback (likes, frustrations, inaccuracies, one improvement).
+Consent was implied via the survey preamble; no personally identifying information was collected beyond what participants chose to upload in their resume for the session.
+
+### Metrics and analysis
+We collected two data types: (1) Likert responses coded 1–5 for descriptive statistics (counts and means); (2) open-text feedback for thematic coding. Because the study was small (n=10) and exploratory, we report descriptive summaries only and treat qualitative feedback as formative signals rather than definitive evidence. Resumes were used only for in-session extraction/matching; no accounts were created and files were not retained.
+
+## Results
+There were 10 respondents (n=10), all active job seekers with a resume ready to match.
+
+### Quantitative findings
+- Feature use (Q1): 10/10 uploaded a resume; 9 viewed the match score; 8 read the score explanation; 7 edited retrieved information; 6 edited a custom role; 7 read a job detail page; 5 clicked “Apply” to open a source posting.
+
+- Task difficulty (Q2): Tasks were largely easy (4 “very easy,” 4 “easy,” 1 neutral, 1 hard).
+
+- Task success (Q3): Perceived success was high (5 “very successful,” 3 “successful,” 1 neutral, 1 not successful).
+
+- Usability (Q4): Ease-of-use ratings skewed positive (6 “easiest,” 3 “easy,” 1 neutral).
+
+- Feature organization (Q5): Rated well-organized or organized by 9 respondents, with 1 neutral.
+
+- Learnability (Q6): Most believed people could learn quickly (6 strongly agree, 3 somewhat agree, 1 neutral).
+
+- Match score meaning (Q7): 2 “most useful,” 4 “somewhat,” 4 neutral.
+
+- Resume/search insight (Q8): 2 “very useful,” 5 “somewhat,” 3 neutral.
+
+- Application effectiveness (Q9): 3 “very effective,” 6 “somewhat,” 1 neutral.
+
+- Perceived accuracy (Q10): Clustered at “somewhat accurate” (1 very accurate, 6 somewhat accurate, 2 neutral, 1 somewhat not accurate).
+
+- Satisfaction (Q11): Leaned positive (3 very satisfied, 5 somewhat satisfied, 2 neutral).
+
+
+
+RQ mapping: RQ1/RQ2 (usability/learnability) are supported by top-box ratings on ease, organization, and learnability. RQ3 (usefulness) is moderately supported: most participants found scores/insights somewhat or very helpful and felt applications could be more effective. RQ4 (accuracy/trust) lags, with ratings clustered at “somewhat accurate” and qualitative calls for more control and transparency.
+
+### Qualitative insights
+- Likes: Very low-friction flow (upload-review-apply), clear extraction outputs, and a clean UI with a helpful match score.
+- Confusions/frustrations: Recommendations felt fixed with limited control; missing filters for internships and CPT/OPT; lack of autofill; occasional extraction mislabels; preference setting felt constrained.
+- Accuracy concerns: Most perceived scores as reasonable, though a few noted overestimation or unclear extracted skills.
+- Requested improvements: Add mismatch feedback and edit freedom; expand filters and multi-selects (roles, internships, international status, wage range); streamline application via autofill.
+
+## Discussion
+Overall, HireShark met core usability expectations (RQ1/RQ2) and showed early signs of usefulness (RQ3) but needs stronger controls to bolster perceived accuracy and trust (RQ4). Ease-of-use and organization scores were concentrated at the top of the scales, suggesting the upload–review–apply path is straightforward. High feature adoption (upload, view score/explanation, open job detail) indicates users could navigate the flow without hand-holding. Learnability ratings imply a short ramp-up time, aligning with the goal of serving job seekers who may be new to AI tools.
+
+Usefulness signals were positive but not definitive. Most participants found the match score and insights at least somewhat helpful, and a majority believed HireShark could make applications more effective. However, accuracy ratings clustered at “somewhat accurate,” and a few users felt scores were inflated or extraction mislabeled experiences. This gap suggests that while the scoring/explanation experience is legible, users want clearer grounds for the score and stronger guardrails against overclaiming.
+
+Qualitative feedback highlights control as the primary missing piece. Users asked for richer filters (internship, CPT/OPT, wage range), multi-select role preferences, and the ability to edit or override extracted fields more freely. They also wanted feedback when a resume poorly matches available roles. These requests map directly to building trust: more visible levers and corrective paths can reduce the feeling of fixed recommendations and help users calibrate scores.
+
+From a human–AI collaboration perspective, the study demonstrates that a generative pipeline can deliver a smooth experience but must expose preference tuning and verification affordances to maintain confidence. Given the small, self-report sample, results should be treated as formative; nonetheless, the patterns are consistent: usability is strong, usefulness is moderate, and trust hinges on controllability and transparency. Prior work on AI-assisted hiring (e.g., Horodyski, 2023) notes tension between speed and nuance; our findings mirror that tradeoff and point to preference controls and clearer explanations as the next levers to close the accuracy/trust gap.
+
+Implications for next iterations: (1) add explicit filters (role, contract type, location, compensation range, visa/OPT/CPT), (2) expose the rationale for scores (evidence snippets from the resume and job), (3) introduce a quick feedback widget to flag bad matches or mis-extracted skills and use it to retrigger ranking, and (4) run an A/B baseline against a traditional job board flow to measure time-on-task, perceived accuracy, and satisfaction. These changes target RQ4 directly while preserving the speed gains that participants valued.
+
+## Limitations, Risks, and Ethical Considerations
+- Small, convenience sample (n=10) limits statistical power and generalizability; results are descriptive and formative.
+- Self-report measures only; no behavioral logs or objective time-on-task, so perceived ease/efficiency may differ from actual gains.
+- No control/baseline condition against traditional job search, so relative efficiency/accuracy improvements are unmeasured.
+- Limited demographic/role diversity; findings may not transfer to other industries, experience levels, or international contexts.
+- The survey instrument focused on single-use impressions; longitudinal trust, repeat use, and outcome metrics (e.g., interview callbacks) were not captured.
+- LLM-based extraction/matching may vary with resume structure/quality and job-posting freshness; robustness to different formats and stale postings was not tested.
+- Privacy: resumes contain sensitive data; while the pilot avoided storage, production deployments should add clear consent, data minimization, and secure handling.
+- Bias and fairness: LLMs and job sources can encode biases; without audits, recommendations may amplify inequities. Preference controls and user review help, but bias testing is required.
+- Overreliance and hallucination: Users may over-trust scores; encourage verification, show evidence for scores, and add feedback loops for corrections.
+
+## Future Work
+- **Preference and control UX**: Add multi-select roles, internship/contract toggles, visa/OPT/CPT filters, and wage sliders; expose reranking after edits.
+- **Feedback loops**: Lightweight thumbs-up/down on matches and extracted skills to retrain prompts or rerank results in-session.
+- **Robustness checks**: Test varied resume formats (length, structure) and diverse job categories; monitor stale postings and API coverage.
+- **Fairness and privacy**: Add bias probes (gender/race-coded resumes), document data handling, and provide clear consent/opt-out in production.
+
+## Conclusion
+HireShark delivers a smooth upload–review–apply flow and early evidence of usefulness: participants rated usability and learnability highly, adopted core features, and generally saw the match score and insights as helpful. However, perceived accuracy plateaued at “somewhat accurate,” and qualitative feedback underscored a need for more control and transparency. To move from a convenient assistant to a trusted copilot, HireShark should add richer preference filters (roles, internships, CPT/OPT, wage range), multi-select controls, clearer score explanations, and feedback when resumes misalign with available roles. Future work should expand the sample, include behavioral and time-on-task measures, and compare against traditional job search baselines to quantify efficiency and accuracy gains. Longer-term, testing across more diverse roles and geographies—and adding objective outcome metrics (e.g., interview callbacks)—will clarify real-world impact.
+
+## Reproducibility & Artifacts
+- Deployed prototype: https://salt-lab-human-ai.github.io/project-check-point-1-ai01/ (used in the study).
+- Codebase: see `hire-shark/` in the repository for implementation details.
+- Data: participant resumes are private; survey instrument summarized in the report.
+- Prompts (Gemini, used during the study; also stored in `hire-shark/prompts/prompts.md`):
+ - Resume parsing:
+ ```
+ Extract the following information from the resume and return it as a JSON object.
+ The JSON object should have the following structure:
+ {
+ "name": "",
+ "email": "",
+ "phone": "",
+ "location": "",
+ "summary": "",
+ "skills": [],
+ "education": [
+ {
+ "degree": "",
+ "field": "",
+ "institution": "",
+ "location": "",
+ "start": "",
+ "end": "",
+ "gpa": "",
+ "honors": []
+ }
+ ],
+ "experiences": [
+ {
+ "company": "",
+ "title": "",
+ "start": "",
+ "end": "",
+ "bullets": []
+ }
+ ],
+ "confidence": {
+ "personalInfo": 0.0,
+ "experience": 0.0,
+ "skills": 0.0,
+ "education": 0.0
+ }
+ }
+
+ Important: Extract all education entries including degrees, certifications, and educational qualifications.
+ Include the degree type (e.g., Bachelor's, Master's, PhD), field of study, institution name, location, dates, GPA (if available), and any honors or distinctions.
+ If you cannot find any information for a field, leave it as an empty string or an empty array.
+ If the uploaded file is not a resume, try your best and you should still return a JSON object.
+ When nothing is found for a section, The confidence for that section should be low.
+ ```
+ - Job role suggestions:
+ ```
+ Based on the following resume, generate a list of 5-10 potential job roles that would be a good fit for this person. Return the list as a JSON array of strings. Job title should be concise and simple.
+
+ Resume:
+ ${JSON.stringify(editedResume.parsed, null, 2)}
+ ```
+ - JD skill extraction:
+ ```
+ You extract the most important skills a candidate needs for this job. Infer required skills even if only implied by responsibilities.
+ - Focus on technologies, tools, frameworks, domain skills, certifications, and relevant soft skills.
+ - Exclude locations, schedules (hours/week), pay/benefits, employment type, headcount, and generic nouns.
+ - Return ONLY a JSON array (no commentary) of up to ${limit} unique skill phrases, each 1-4 words, title-cased when appropriate.
+
+ Job Title: ${title || "(missing)"}
+
+ Job Description:
+ ${description || "(missing)"}
+
+ Existing keywords: ${keywords.length ? keywords.join(", ") : "none"}
+ ```
+- Figures: UI screenshots and survey charts are bundled in `img/` and embedded in the appendices.
+
+## References
+Alsaif, S., Hidri, M. S., Ferjani, I., Eleraky, H. A., & Hidri, A. (2022). NLP-based bi-directional recommendation system: Towards recommending jobs to job seekers and resumes to recruiters. *Big Data and Cognitive Computing, 6*, 147.
+
+Chen, Z. (2023). Collaboration among recruiters and artificial intelligence: Removing human prejudices in employment. *Cognition, Technology & Work*. https://pmc.ncbi.nlm.nih.gov/articles/PMC9516509/
+
+Fragiadakis, G., Diou, C., Kousiouris, G., & Nikolaidou, M. (2025). Evaluating human-AI collaboration: A review and methodological framework. *arXiv*. https://doi.org/10.48550/arXiv.2407.19098
+
+Horodyski, P. (2023). Applicants’ perception of artificial intelligence in the recruitment process. *Computers in Human Behavior Reports, 11*, 100303. https://doi.org/10.1016/j.chbr.2023.100303
+
+Smith, J., Lee, M., & Patel, R. (2023). Natural language processing in recruitment: A systematic review. *Journal of Human Resource Management, 45*(3), 234–251.
+
+## Appendices
+- **Appendix A: UI screenshots**
+ - Landing page: 
+ - Resume upload: 
+ - Review extracted info: 
+ - Skills view: 
+ - View matches: 
+ - No matches state: 
+
+- **Appendix B: Survey result charts (Q1–Q11)**
+ - Feature use: 
+ - Task difficulty: 
+ - Task success: 
+ - Ease of use: 
+ - Feature organization: 
+ - Learnability: 
+ - Match score meaning: 
+ - Resume/search insight: 
+ - Application effectiveness: 
+ - Perceived accuracy: 
+ - Satisfaction: 
+
+- **Appendix C: Data and materials**
+ - Participant resumes are not distributed for privacy.
+ - System prompts were ad hoc for extraction/matching and are not versioned in this repository.
+ - Additional assets: `../img/logo.png`.
diff --git a/doc/HireShark Feedback.csv b/doc/HireShark Feedback.csv
new file mode 100644
index 0000000..21db4b2
--- /dev/null
+++ b/doc/HireShark Feedback.csv
@@ -0,0 +1,11 @@
+"Timestamp","Which actions did you use in HireShark? (Check all that apply)","How easy or difficult were the tasks overall? ","How successful do you think you were in completing the tasks? ","HireShark was easy to use.","The features felt well-organized. ","I think most people could learn to use Hireshark quickly. ","The match score felt meaningful and helpful.","HireShark gave me useful insights to improve my resume or job search.","Using HireShark could help me apply for jobs more effectively. ","Match results from HireShark is accurate."," Overall, how satisfied were you with using HireShark? ","What did you like most about using HireShark? ","What was confusing or frustrating? ","Did any scores or explanations feel wrong or inaccurate?","What’s one improvement you would make? "
+"2025/12/01 11:24:17 PM CST","Uploaded resume;Edited retrieved information;Viewed match score;Read the explanation of match;Read the detail page of job","1","4","5","5","5","5","5","4","4","5","Easiness to upload and review my resume, finding things to improve through the result page","I want to select the option for internship and the companies allows to apply with CPT/OPT, but it wasn't available","none","make more job filtering options for internship and international applicants, and allow users to select multiple options."
+"2025/12/03 4:49:44 PM CST","Uploaded resume;Edited custom job role;Viewed match score;Read the explanation of match;Read the detail page of job","2","4","4","4","4","3","4","4","3","4","Suggesting match score","Setting preference. It might be multiple choices.","Little bit curious about my extracted skills by LLM","Serving multiple choices at ""Set preferences"". Also it will be more useful using slide bar to decide minimum-maximum wage."
+"2025/12/04 11:31:50 AM CST","Uploaded resume","3","3","3","3","3","3","3","3","3","3","","","",""
+"2025/12/04 11:40:32 AM CST","Uploaded resume;Edited retrieved information;Edited custom job role;Viewed match score","1","5","5","5","5","4","4","4","4","5","UI was simple and clean, with minimal clicks I could able to get to the desired application part","Providing multi select pick list for job roles ","Not","Providing multi select pick list for job roles would be better. "
+"2025/12/04 12:07:04 PM CST","Uploaded resume;Viewed match score;Read the explanation of match;Read the detail page of job;Clicked ""Apply"" to access more information on the actual job posting page","2","2","4","4","4","4","4","4","4","4","","","",""
+"2025/12/04 2:50:41 PM CST","Uploaded resume;Edited retrieved information;Edited custom job role;Viewed match score;Read the explanation of match;Read the detail page of job;Clicked ""Apply"" to access more information on the actual job posting page","2","5","5","4","4","4","3","4","2","3","","Some of the extracted skills are not applicable and the work experience extracted is not actually work experience on my resume, it's just leadership and other volunteering/research experience.","",""
+"2025/12/04 7:35:23 PM CST","Uploaded resume;Edited retrieved information;Edited custom job role;Viewed match score;Read the explanation of match;Read the detail page of job;Clicked ""Apply"" to access more information on the actual job posting page","4","5","5","4","5","4","5","5","4","4","The extract is clear and easy to handle","I hope there's a autofill when entering the application page for the job","It's higher than I truly am",""
+"2025/12/04 7:36:49 PM CST","Uploaded resume;Edited retrieved information;Edited custom job role;Viewed match score;Read the explanation of match;Read the detail page of job;Clicked ""Apply"" to access more information on the actual job posting page","1","5","5","5","5","3","4","4","4","4","The information is extracted correctly with accurate internship, time, contribution, etc.","The options of the suggesting job list seems to be fixed. I can't choose any other jobs I want.","NA.","Give more freedom on each section, users may want to add/delete some info else."
+"2025/12/04 9:13:11 PM CST","Uploaded resume;Edited retrieved information;Edited custom job role;Viewed match score;Read the explanation of match;Read the detail page of job;Clicked ""Apply"" to access more information on the actual job posting page","1","5","5","5","5","5","4","5","5","5","I like how easy it is to use without any complex steps","It was not confusing at all","They are all really accurate","I would say maybe add some feedback for the user if his resume does not match"
+"2025/12/04 11:48:36 PM CST","Uploaded resume;Edited retrieved information;Viewed match score;Read the explanation of match","2","4","4","5","5","3","3","5","4","4","","","",""
\ No newline at end of file
diff --git a/doc/HireShark_Presentation_Slide.pdf b/doc/HireShark_Presentation_Slide.pdf
new file mode 100644
index 0000000..c943084
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diff --git a/doc/survey_link.md b/doc/survey_link.md
new file mode 100644
index 0000000..7860311
--- /dev/null
+++ b/doc/survey_link.md
@@ -0,0 +1,2 @@
+# Survey link to Google Form
+You can check our survey results by following this link: [Survey Form](https://forms.gle/Fds4fbb2a8bwqCRT9)
\ No newline at end of file
diff --git a/hire-shark/.env.example b/hire-shark/.env.example
new file mode 100644
index 0000000..10c8679
--- /dev/null
+++ b/hire-shark/.env.example
@@ -0,0 +1,7 @@
+# Optional direct keys (used only for local encryption or manual overrides)
+VITE_GEMINI_API_KEY=
+VITE_ADZUNA_APP_ID=
+VITE_ADZUNA_APP_KEY=
+
+# Encrypted payload used at runtime (output of scripts/encrypt_secrets.py)
+VITE_ENCODED_SECRET=
diff --git a/hire-shark/.npmrc b/hire-shark/.npmrc
new file mode 100644
index 0000000..4e2c642
--- /dev/null
+++ b/hire-shark/.npmrc
@@ -0,0 +1 @@
+cache=./node_modules/.npm-cache
diff --git a/hire-shark/INSTALL.md b/hire-shark/INSTALL.md
new file mode 100644
index 0000000..bd275b5
--- /dev/null
+++ b/hire-shark/INSTALL.md
@@ -0,0 +1,61 @@
+# HireShark Installation Guide
+
+Follow these steps to run the HireShark web app locally.
+
+## 1. Prerequisites
+- Node.js 18.x (LTS) or newer. Use [nvm](https://github.com/nvm-sh/nvm) if you manage multiple versions.
+- npm 9+ (ships with Node 18).
+- (Optional) Git, if you plan to clone and contribute.
+
+You can verify your versions:
+```sh
+node -v
+npm -v
+```
+
+## 2. Install dependencies
+From the project root:
+```sh
+cd hire-shark
+npm install
+```
+This downloads the Vite/React/shadcn UI dependencies listed in `package.json`.
+
+## 3. Configure environment variables
+Copy `.env.example` to `.env`:
+```sh
+cp .env.example .env
+```
+Then choose one of the following:
+- **Shared vault workflow (recommended):** leave the plain-text entries empty and add `VITE_ENCODED_SECRET` with the output of `scripts/encrypt_secrets.py`.
+- **Direct keys (local dev only):** set `VITE_GEMINI_API_KEY`, `VITE_ADZUNA_APP_ID`, and `VITE_ADZUNA_APP_KEY`. These values are only used for encryption or manual overrides; do not commit them.
+
+Restart any running dev server after changing `.env`.
+
+## 4. Encrypt Gemini + Adzuna keys (shared testing)
+1. Install Python 3.9+ plus the `cryptography` package (`pip install cryptography`).
+2. Ensure `.env` contains `VITE_GEMINI_API_KEY`, `VITE_ADZUNA_APP_ID`, and `VITE_ADZUNA_APP_KEY`.
+3. Run `python scripts/encrypt_secrets.py` and enter a strong shared password when prompted.
+4. Copy the printed password for trusted testers and store the encoded string as `VITE_ENCODED_SECRET` locally + as the GitHub secret `ENCODED_SECRET`.
+5. Remove the plain-text keys from `.env` after generating the blob.
+
+During runtime the browser prompts for the shared password, decrypts the blob entirely in memory, and uses the recovered API keys. Users without the password can still enter their own keys when prompted.
+
+## 5. Start the dev server
+Run Vite in watch mode:
+```sh
+npm run dev
+```
+Open the printed URL (default `http://localhost:8080`). The app uses client-side routing, so navigate through Upload -> Review -> Preferences -> Matches to exercise the flow.
+
+## 6. Build for production (optional)
+```sh
+npm run build
+```
+Artifacts land in `hire-shark/dist`.
+
+The production build needs the `project-check-point-1-ai01` suffix path to access corresponding pages correctly.
+
+## 7. Troubleshooting tips
+- **Blank matches page**: confirm `.env` values are set and the browser console shows Adzuna requests succeeding.
+- **Resume parsing fails**: ensure the shared password was entered correctly, that `VITE_ENCODED_SECRET` is valid, or paste a personal Gemini/Adzuna key when prompted.
diff --git a/hire-shark/README.md b/hire-shark/README.md
new file mode 100644
index 0000000..d754d2f
--- /dev/null
+++ b/hire-shark/README.md
@@ -0,0 +1,68 @@
+# HireShark
+AI-assisted resume analysis and job matching. Upload a résumé, let Gemini extract structured data, tune your preferences, and pull live roles from Adzuna to see the best-fit opportunities.
+
+## Try it live
+[Github Page Deployment](https://salt-lab-human-ai.github.io/project-check-point-1-ai01/)
+
+---
+
+## Tech Stack
+- **Frontend:** Vite, React 18, TypeScript, Tailwind CSS, shadcn/ui
+- **State/Data:** React Context (resume & preferences), TanStack Query for future data hooks
+- **AI & APIs:** Google Gemini (`@google/generative-ai`) for parsing + ideation, Adzuna Jobs API for live listings
+- **Tooling:** ESLint, Mermaid diagrams, GitHub Actions → GH Pages deploy
+
+---
+
+## Key Flows
+1. **Upload** – Users drag/drop PDF/DOC/DOCX; files stored in `ResumeContext`.
+2. **Review** – Gemini prompt extracts personal info, experience, education, and skills for in-app editing.
+3. **Preferences** – Users select AI-suggested roles or a custom role, job type, and salary bands.
+4. **Matches** – Preferences + parsed resume trigger Adzuna searches; results are sortable and include detail modals.
+
+Supporting resources:
+- `docs/` – architecture & sequence Mermaid diagrams (render via `npm run diagram:build`).
+- `prompts/prompts.md` – complete text of every LLM prompt.
+- `prompts/data_connectors_and_models.md` – model IDs, API endpoints, and auth notes.
+
+---
+
+## Getting Started
+Detailed install steps live in [`INSTALL.md`](./INSTALL.md). Quick version:
+
+```sh
+git clone
+cd hire-shark
+npm install
+cp .env.example .env # then add VITE_ENCODED_SECRET or local keys
+npm run dev
+```
+
+### Environment Variables (`.env`)
+| Name | Description |
+| --- | --- |
+| `VITE_ENCODED_SECRET` | Base64 blob produced by `scripts/encrypt_secrets.py`; contains Gemini + Adzuna credentials. |
+| `VITE_GEMINI_API_KEY`, `VITE_ADZUNA_APP_ID`, `VITE_ADZUNA_APP_KEY` | Optional plain-text entries used only for local encryption or manual overrides. |
+
+Restart the dev server after editing `.env`.
+
+### Shared password workflow
+Client-side API keys now live in a single encrypted payload:
+1. Populate `.env` with `VITE_GEMINI_API_KEY`, `VITE_ADZUNA_APP_ID`, and `VITE_ADZUNA_APP_KEY`.
+2. Run `python scripts/encrypt_secrets.py`, enter a shared password when prompted, and copy the printed password + encoded string.
+3. Store the encoded value locally as `VITE_ENCODED_SECRET` (and in the GitHub repo secret `VITE_ENCODED_SECRET`), then remove the plain-text keys from `.env`.
+4. Share the password only with trusted testers. When the site needs credentials, it prompts for that password, decrypts the blob in-memory, and never writes the plaintext anywhere persistent.
+
+Users without the shared password can still paste their own Gemini/Adzuna keys when prompted.
+
+## Deployment
+GitHub Actions (`.github/workflows/deploy-gh-pages.yml`) builds on pushes to `main`, writes `VITE_ENCODED_SECRET` into `hire-shark/.env`, and publishes `dist/` to the `gh-pages` branch. Generate a new payload with `scripts/encrypt_secrets.py` whenever keys rotate and update the GitHub secret `ENCODED_SECRET` before triggering a deploy.
+
+---
+
+## Safety & Privacy
+- **Resume data stay local:** Uploads live in browser memory (`ResumeContext`). Refreshing clears all resume data; there is no backend persistence in this repo.
+- **LLM disclosure:** The full parsed résumé is sent to Google Gemini for extraction and job-role ideation. The information is not stored by HireShark, but review Google’s data policies for Gemini.
+- **API keys:** Gemini and Adzuna credentials live inside an AES-GCM encrypted blob (`VITE_ENCODED_SECRET`). They’re only decrypted in-memory after the user enters the shared password or supplies personal keys.
+- **External calls:** Adzuna requests only include preference data (role, salary, job type); we never transmit résumé content to Adzuna.
+- **Google Gemini usage:** API calls using Gemini may be blocked if quota limits are exceeded.
diff --git a/hire-shark/bun.lockb b/hire-shark/bun.lockb
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diff --git a/hire-shark/components.json b/hire-shark/components.json
new file mode 100644
index 0000000..62e1011
--- /dev/null
+++ b/hire-shark/components.json
@@ -0,0 +1,20 @@
+{
+ "$schema": "https://ui.shadcn.com/schema.json",
+ "style": "default",
+ "rsc": false,
+ "tsx": true,
+ "tailwind": {
+ "config": "tailwind.config.ts",
+ "css": "src/index.css",
+ "baseColor": "slate",
+ "cssVariables": true,
+ "prefix": ""
+ },
+ "aliases": {
+ "components": "@/components",
+ "utils": "@/lib/utils",
+ "ui": "@/components/ui",
+ "lib": "@/lib",
+ "hooks": "@/hooks"
+ }
+}
diff --git a/hire-shark/docs/README.md b/hire-shark/docs/README.md
new file mode 100644
index 0000000..b48bdf2
--- /dev/null
+++ b/hire-shark/docs/README.md
@@ -0,0 +1,25 @@
+Architecture Diagrams
+
+This folder contains Mermaid source files (.mmd) that describe HireShark’s technical architecture, flows, and data model.
+
+Files
+- docs/architecture.mmd – Component view (SPA, contexts, libs, external services)
+- docs/sequence-upload.mmd – Upload + parse flow
+- docs/sequence-matching.mmd – Roles/locations + matching flow
+- docs/deployment.mmd – Deployment view (Vite build, hosting, pdf.js worker)
+- docs/data-model.mmd – Type relationships
+
+Generate SVGs
+1) Install Mermaid CLI locally (dev only):
+ npm i -D @mermaid-js/mermaid-cli
+
+2) Render all diagrams to SVG:
+ npm run diagram:build
+
+Outputs will be written next to each .mmd file (e.g., architecture.svg).
+
+Tips
+- Update these diagrams whenever you change pages, contexts, or parsing/matching flows.
+- If you add new external services (e.g., real job APIs), extend the component and sequence diagrams to reflect those calls.
+- Keep environment usage explicit (e.g., Gemini + Adzuna secrets are now bundled inside the encrypted `VITE_ENCODED_SECRET` vault that the browser unlocks at runtime) and call out security considerations in PRs.
+
diff --git a/hire-shark/docs/architecture.mmd b/hire-shark/docs/architecture.mmd
new file mode 100644
index 0000000..7349d8c
--- /dev/null
+++ b/hire-shark/docs/architecture.mmd
@@ -0,0 +1,35 @@
+flowchart LR
+ subgraph Browser [Browser - React App]
+ Router[BrowserRouter]
+ Pages[Pages: Landing, Upload, Review, Preferences, Matches, Success]
+ UI[shadcn / radix UI]
+ RC[ResumeContext]
+ PC[PreferencesContext]
+ Libs[libs: gemini_parser.ts, parser.ts, matcher.ts, mockJobs.ts]
+ end
+
+ subgraph External
+ Gemini[Google Generative AI\n@google/generative-ai]
+ PDFWorker[pdf.worker.min.mjs (pdf.js worker)]
+ LocalStorage[(localStorage)]
+ end
+
+ Router --> Pages
+ Pages --> RC
+ Pages --> PC
+ RC --> Libs
+ PC --> LocalStorage
+
+ %% Parsing flows
+ RC -- parseResumeWithGemini --> Gemini
+ Libs -- pdfjs/mammoth --> PDFWorker
+
+ %% Matching
+ RC -- getMatches --> Libs
+
+ %% Build/Serve
+ subgraph Tooling
+ Vite[Vite + vite-plugin-static-copy]
+ end
+ Vite -- copies --> PDFWorker
+
diff --git a/hire-shark/docs/architecture.png b/hire-shark/docs/architecture.png
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diff --git a/hire-shark/docs/code-flow.md b/hire-shark/docs/code-flow.md
new file mode 100644
index 0000000..61f323f
--- /dev/null
+++ b/hire-shark/docs/code-flow.md
@@ -0,0 +1,40 @@
+# Code Flow
+
+This document summarizes the end-to-end UI flow and where core logic lives. A screenshot of the flow can be placed at `docs/screenshots/code-flow/` and is referenced below.
+
+## App routes (SPA)
+- Entry router: `src/App.tsx`
+- Pages:
+ - Landing → `src/pages/Landing.tsx`
+ - Upload → `src/pages/Upload.tsx`
+ - Review → `src/pages/Review.tsx`
+ - Preferences → `src/pages/Preferences.tsx`
+ - Matches → `src/pages/Matches.tsx`
+
+## State and context
+- Resume state: `src/store/ResumeContext.tsx`
+- Preferences state: `src/store/PreferencesContext.tsx`
+
+## Core libraries
+- Resume parsing & extraction: `src/lib/parser.ts`, `src/lib/skillExtractor.ts`, `src/lib/gemini_parser.ts`
+- Matching logic: `src/lib/matcher.ts`, `src/lib/word_match_skipgram.ts`, `src/lib/skipgramAdapter.ts`
+- Jobs data & adapters: `src/lib/adzunaApi.ts`, `src/lib/mockJobs.ts`
+- Utilities & embeddings: `src/lib/utils.ts`, `src/lib/embeddings.ts`
+
+## Flow summary
+1) User lands on Landing and proceeds to Upload.
+2) Upload accepts a resume; parsing/skill extraction runs (parser + skill extractor + optional Gemini parser).
+3) Review shows parsed content for confirmation.
+4) Preferences collects job filters (location, role, etc.).
+5) Matches runs the matcher against job sources and presents ranked results with reasoning.
+
+## Screenshot
+If you have a captured diagram/screenshot of the code flow, place it here as `code-flow.png`:
+`docs/screenshots/code-flow/code-flow.png`
+
+Markdown embed (will render once the file exists at the path):
+
+
+
+Note: The provided path `/var/folders/.../Screenshot 2025-11-07 at 12.33.09 PM.png` is a temporary system path on your machine. Copy that file into the path above and optionally rename it to `code-flow.png` (recommended to avoid special characters/spaces). If you keep the original filename, update the image link accordingly.
+
diff --git a/hire-shark/docs/data-model.mmd b/hire-shark/docs/data-model.mmd
new file mode 100644
index 0000000..fc40fda
--- /dev/null
+++ b/hire-shark/docs/data-model.mmd
@@ -0,0 +1,67 @@
+classDiagram
+ class ResumeParsed {
+ string name
+ string email
+ string phone
+ string location
+ string summary
+ string[] skills
+ Experience[] experiences
+ Education[] education
+ Confidence confidence
+ string rawText
+ string fileName
+ }
+ class Experience {
+ string company
+ string title
+ string start
+ string end
+ string[] bullets
+ }
+ class Education {
+ string degree
+ string field
+ string institution
+ string location
+ string start
+ string end
+ string gpa
+ string[] honors
+ }
+ class Confidence {
+ number personalInfo
+ number experience
+ number skills
+ number education
+ }
+ class ResumeData {
+ string id
+ File file
+ ResumeParsed parsed
+ string uploadedAt
+ }
+ class JobPosting {
+ string jobId
+ string title
+ string company
+ string description
+ string[] skills
+ }
+ class MatchResult {
+ string jobId
+ string title
+ string company
+ number score
+ string[] matchedSkills
+ string snippet
+ string location
+ string salary
+ string jobType
+ string url
+ string postedDate
+ }
+ ResumeData --> ResumeParsed
+ ResumeParsed o--> Experience
+ ResumeParsed o--> Education
+
diff --git a/hire-shark/docs/deployment.mmd b/hire-shark/docs/deployment.mmd
new file mode 100644
index 0000000..5cf1514
--- /dev/null
+++ b/hire-shark/docs/deployment.mmd
@@ -0,0 +1,16 @@
+flowchart TD
+ Dev[Dev: Vite dev server :8080] --> Browser
+ Build[Vite Build] --> StaticHost[Static Hosting (e.g., GitHub Pages)]
+ StaticHost --> Browser
+
+ subgraph Browser
+ App[HireShark SPA]
+ Worker[pdf.worker.min.mjs]
+ end
+
+ App --> Gemini[Google Generative AI API]
+ App --> LocalStorage[(localStorage)]
+ App --> Worker
+
+ note right of Build: vite-plugin-static-copy copies pdf.worker.min.mjs
+
diff --git a/hire-shark/docs/observability.md b/hire-shark/docs/observability.md
new file mode 100644
index 0000000..ee46075
--- /dev/null
+++ b/hire-shark/docs/observability.md
@@ -0,0 +1,76 @@
+# Telemetry & Observability Plan
+
+This document describes how we collect, store, and act on operational signals for HireShark.
+
+## Objectives
+- Understand how users flow through resume upload → review → matches → apply.
+- Detect matching errors (CSV parsing, NLP scoring) quickly.
+- Measure job API performance and match quality.
+- Provide enough breadcrumbs to debug incidents end to end.
+
+## Instrumentation Overview
+
+| Layer | Signals | Notes |
+|-------|---------|-------|
+| Frontend | Page views, CTA clicks (Upload Resume, Find Matches, Apply, Express Interest), theme toggle, API latency, client errors | Use `window.analytics` wrapper (e.g., PostHog/Amplitude) + `console.error` intercept. |
+| Resume parsing & matching | Structured logs per resume ID: parsing status, total jobs loaded, top match titles and scores, NLP fallback indicator, errors | Already logs top titles in `jobs/search`. We'll enhance by attaching resume hash. |
+| Connectors | Time-to-fetch per ATS source, job counts, cache hits/misses | Log to stdout with log level INFO + JSON payload. |
+| System health | Vite proxy usage, Express health endpoint 200s, memory usage, process uptime | Exposed via `/api/health`, use PM2/ProcMon for process metrics if deployed. |
+
+## Log Schema (JSON Lines)
+
+```json
+{
+ "timestamp": "2025-11-07T03:30:00.000Z",
+ "level": "info",
+ "component": "matching",
+ "resumeId": "1",
+ "event": "match_success",
+ "jobsReturned": 5,
+ "topJobs": ["Senior Data Analyst", "Data Scientist", "Machine Learning Engineer"],
+ "skipgram": false,
+ "elapsedMs": 212
+}
+```
+
+```json
+{
+ "timestamp": "2025-11-07T03:35:00.000Z",
+ "level": "error",
+ "component": "greenhouse_connector",
+ "boardToken": "stripe",
+ "event": "fetch_failed",
+ "status": 503,
+ "message": "Service Unavailable"
+}
+```
+
+## Storage & Retention
+- Development: stdout + browser console. Capture logs via `npm run dev:all` and keep until feature complete.
+- Production (future): send JSON logs to Logtail/Elastic with 30-day retention, apply data scrubbing to avoid PII.
+
+## Alerting & Dashboards (Future)
+- Alert on `match_failure` or `fetch_failed` > 5/min for any source.
+- Track `jobsReturned` distribution—should be > 3 in >80% of sessions.
+- Dashboards for user funnels (upload → matches → apply button clicks) using analytics tool.
+
+## Debugging Workflow
+1. Reproduce with resume ID (hash). Use stored logs to see connectors and match scores.
+2. Check `/api/health` and Express logs for systemic issues.
+3. If connectors failing, run `curl https://...` manually.
+4. Use Jest/Vitest tests (`npm run test`) to ensure tokenization and similarity logic behave.
+5. If only UI impacted, inspect network tab for `/api/jobs/search` response payload.
+
+## Test Observability
+- Vitest runs include logging top matches for verifying expected order.
+- Add snapshot tests for telemetry payload shape (TODO).
+
+## Privacy Considerations
+- Do not store raw resume text. Use hashed IDs + truncated keywords.
+- Strip PII before sending analytics events (e.g., no email address).
+
+## Enhancements Backlog
+- [ ] Ship analytics wrapper with batching + retry.
+- [ ] Integrate OpenTelemetry for trace correlation between client and API.
+- [ ] Add feature flags to control logging verbosity per environment.
+
diff --git a/hire-shark/docs/safety-privacy.mmd b/hire-shark/docs/safety-privacy.mmd
new file mode 100644
index 0000000..82e2f3b
--- /dev/null
+++ b/hire-shark/docs/safety-privacy.mmd
@@ -0,0 +1,27 @@
+mindmap
+ root((Safety & Privacy Notes))
+ PII Handling
+ "Avoid storing raw resume text"
+ "Hash resume IDs before logging"
+ "Strip emails/phone numbers from analytics"
+ "Use HTTPS for uploads"
+ Data Retention
+ "Temporary in-memory parsing"
+ "Optional encrypted cache with TTL"
+ "User initiated purge"
+ Rate Limits
+ "Throttle API requests per IP"
+ "Backoff when third-party APIs return 429"
+ "Queue resume parsing jobs"
+ Abuse Mitigation
+ "Validate file type & size"
+ "Scan uploads for malware signatures"
+ "Reject prompt injection in AI responses"
+ Access Control
+ "ENV-stored API keys"
+ "Principle of least privilege for DB"
+ "Audit logs for admin actions"
+ Compliance Considerations
+ "GDPR-style export/delete workflows"
+ "Privacy policy surfaced in UI"
+ "Vendor DPAs on record"
diff --git a/hire-shark/docs/sequence-matching.mmd b/hire-shark/docs/sequence-matching.mmd
new file mode 100644
index 0000000..fe7523b
--- /dev/null
+++ b/hire-shark/docs/sequence-matching.mmd
@@ -0,0 +1,20 @@
+sequenceDiagram
+ participant Review as Review.tsx
+ participant ResumeCtx as ResumeContext.tsx
+ participant Gemini as Google Generative AI
+ participant Prefs as Preferences.tsx
+ participant Matches as Matches.tsx
+ participant Matcher as matcher.ts
+
+ Review->>ResumeCtx: generateJobRolesFromEditedResume(edited)
+ ResumeCtx->>Gemini: generateContent(job roles prompt)
+ Gemini-->>ResumeCtx: roles JSON
+ ResumeCtx->>Gemini: generateContent(locations prompt)
+ Gemini-->>ResumeCtx: locations JSON
+ Review->>Prefs: navigate(/preferences)
+ Prefs->>Matches: navigate(/matches)
+ Matches->>ResumeCtx: runMatching()
+ ResumeCtx->>Matcher: getMatches(parsed, mockJobs)
+ Matcher-->>ResumeCtx: MatchResult[]
+ ResumeCtx-->>Matches: set matches
+
diff --git a/hire-shark/docs/sequence-upload.mmd b/hire-shark/docs/sequence-upload.mmd
new file mode 100644
index 0000000..4662eeb
--- /dev/null
+++ b/hire-shark/docs/sequence-upload.mmd
@@ -0,0 +1,23 @@
+sequenceDiagram
+ actor User
+ participant UploadPage as Upload.tsx
+ participant ResumeCtx as ResumeContext.tsx
+ participant Gemini as Google Generative AI
+ participant Parser as gemini_parser.ts
+
+ User->>UploadPage: Select resume file
+ UploadPage->>ResumeCtx: uploadFile(file)
+ User->>UploadPage: Continue
+ UploadPage->>ResumeCtx: parseResume()
+ ResumeCtx->>Parser: parseResumeWithGemini(file)
+ alt DOCX/TXT
+ Parser->>Parser: extract raw text (mammoth/file.text)
+ else PDF/other
+ Parser->>Parser: fileToGenerativePart(dataURL)
+ end
+ Parser->>Gemini: generateContent(prompt, part)
+ Gemini-->>Parser: JSON resume fields
+ Parser-->>ResumeCtx: ResumeParsed
+ ResumeCtx-->>UploadPage: set resume.parsed
+ UploadPage->>Review: navigate(/review)
+
diff --git a/hire-shark/docs/use-case.md b/hire-shark/docs/use-case.md
new file mode 100644
index 0000000..e696963
--- /dev/null
+++ b/hire-shark/docs/use-case.md
@@ -0,0 +1,362 @@
+# Test Cases Documentation
+
+This document describes the minimal test cases implemented in HireShark to ensure core functionality works correctly.
+
+## Overview
+
+HireShark includes three test suites covering the main features:
+1. **Matching Algorithm Tests** (`src/lib/__tests__/matcher.test.ts`)
+2. **Resume Parser Tests** (`src/lib/__tests__/gemini_parser.test.ts`)
+3. **Context Integration Tests** (`src/store/__tests__/resume_context.test.tsx`)
+
+## Running Tests
+
+```bash
+# Run all tests
+npm test
+
+# Run tests in watch mode
+npm test -- --watch
+
+# Run a specific test file
+npx vitest src/lib/__tests__/matcher.test.ts
+
+# Run tests with coverage
+npx vitest --coverage
+```
+
+## Test Suites
+
+### 1. Matching Algorithm Tests (matcher.test.ts)
+
+**Purpose**: Validates the hybrid matching algorithm that combines skill overlap scoring with vector similarity (TF-IDF or Skip-gram).
+
+**Location**: `src/lib/__tests__/matcher.test.ts`
+
+#### Test Cases
+
+##### 1.1 Returns matches sorted by score desc and computes score correctly
+- **What it tests**: The matching algorithm correctly ranks jobs based on skill overlap and vector similarity
+- **Setup**:
+ - 3 job postings: Frontend (React, TypeScript, CSS), Backend (Node.js, Express, MongoDB), Full Stack (React, Node.js, TypeScript, AWS)
+ - Resume with skills: React, TypeScript, CSS
+- **Expected behavior**:
+ - Frontend job ranks first (100% skill match)
+ - Full Stack ranks second (75% skill match)
+ - Scores are normalized between 0 and 1
+- **Key assertion**: `expect(matches[0].title).toBe("Frontend")`
+
+##### 1.2 Respects limit parameter
+- **What it tests**: The `getMatches` function honors the limit parameter
+- **Setup**: Same as above with limit=1
+- **Expected behavior**: Only 1 match returned
+- **Key assertion**: `expect(matches.length).toBe(1)`
+
+##### 1.3 Returns empty array when no skills overlap
+- **What it tests**: The algorithm handles cases with zero skill overlap gracefully
+- **Setup**: Resume with Go and Rust skills (no matches with job postings)
+- **Expected behavior**:
+ - Returns an array (possibly empty or with low-scoring matches)
+ - Vector similarity may still return results based on textual similarity
+ - All scores are between 0 and 1
+- **Key assertion**: `expect(Array.isArray(matches)).toBe(true)`
+
+**Technical Details**:
+- Uses hybrid scoring: 80% vector similarity + 20% skill overlap
+- Supports both Skip-gram (TensorFlow.js) and TF-IDF fallback
+- Tests run in jsdom environment (simulates browser)
+
+---
+
+### 2. Resume Parser Tests (gemini_parser.test.ts)
+
+**Purpose**: Validates that the Gemini API integration correctly handles different response formats and extracts resume data.
+
+**Location**: `src/lib/__tests__/gemini_parser.test.ts`
+
+#### Test Cases
+
+##### 2.1 Strips code-fences and parses object when model returns fenced json
+- **What it tests**: Parser handles markdown-fenced JSON responses from Gemini
+- **Setup**: Mock Gemini response with triple backticks:
+ ```
+ ```json
+ {
+ "skills": ["TS", "React"]
+ }
+ ```
+ ```
+- **Expected behavior**:
+ - Strips markdown code fences
+ - Parses JSON correctly
+ - Returns skills array: `["TS", "React"]`
+ - Sets fileName to uploaded file name
+- **Key assertion**: `expect(result.skills).toEqual(["TS", "React"])`
+
+##### 2.2 Parses correctly when model returns plain JSON string
+- **What it tests**: Parser handles plain JSON responses without fences
+- **Setup**: Mock Gemini response: `{"skills":["Go","K8s"]}`
+- **Expected behavior**:
+ - Parses JSON directly
+ - Returns skills array: `["Go", "K8s"]`
+ - Sets correct fileName
+- **Key assertion**: `expect(result.skills).toEqual(["Go", "K8s"])`
+
+**Technical Details**:
+- Mocks `@google/generative-ai` module to avoid real API calls
+- Tests run without requiring actual Gemini API key
+- Validates JSON cleaning logic (removing code fences)
+- Tests the `parseResumeWithGemini` function end-to-end
+
+**Mock Implementation**:
+```typescript
+// Mock setup creates a fake GoogleGenerativeAI client
+const mockClient = {
+ getGenerativeModel: vi.fn(),
+};
+
+vi.mock("@google/generative-ai", () => ({
+ GoogleGenerativeAI: vi.fn(function() { return mockClient; }),
+}));
+```
+
+---
+
+### 3. Context Integration Tests (resume_context.test.tsx)
+
+**Purpose**: Validates the React Context state management for resume upload, parsing, and matching workflows.
+
+**Location**: `src/store/__tests__/resume_context.test.tsx`
+
+#### Test Suite 1: Upload → Parse (Resume)
+
+##### 3.1 uploadFile sets resume with id, file, and uploadedAt
+- **What it tests**: File upload initializes resume state correctly
+- **Setup**: Create a File object and upload it
+- **Expected behavior**:
+ - Resume ID is set to "1"
+ - File reference is stored
+ - uploadedAt timestamp is created
+- **Key assertion**: `expect(state.resume?.id).toBe("1")`
+
+##### 3.2 parseResume toggles isParsing true→false and sets resume.parsed on success
+- **What it tests**: Async parsing flow with loading states
+- **Setup**:
+ - Upload a file
+ - Mock `parseResumeWithGemini` to return parsed data
+ - Start parsing
+- **Expected behavior**:
+ - `isParsing` flag toggles true during parsing
+ - `isParsing` returns to false after completion
+ - `resume.parsed` is populated with parsed data
+- **Key assertion**: `expect(state.resume?.parsed).toEqual(parsed)`
+
+##### 3.3 parseResume no-ops safely when resume.file is missing
+- **What it tests**: Graceful handling of edge case
+- **Setup**: Call parseResume without uploading a file first
+- **Expected behavior**:
+ - No error thrown
+ - `parseResumeWithGemini` is not called
+ - State remains unchanged
+- **Key assertion**: `expect(spy).not.toHaveBeenCalled()`
+
+##### 3.4 parseResume propagates failures and resets isParsing to false on error
+- **What it tests**: Error handling in parsing flow
+- **Setup**: Mock `parseResumeWithGemini` to reject
+- **Expected behavior**:
+ - Error is propagated (promise rejects)
+ - `isParsing` is reset to false
+ - `resume.parsed` remains undefined
+- **Key assertion**: `expect(state.isParsing).toBe(false)`
+
+#### Test Suite 2: Generate Roles & Locations
+
+##### 3.5 updates generatedJobRoles and generatedLocations; handles code-fenced JSON
+- **What it tests**: AI-powered job role generation from resume
+- **Setup**:
+ - Mock Gemini to return roles: `["Role A", "Role B"]`
+ - Mock Gemini to return locations: `["City X", "City Y"]`
+ - Call `generateJobRolesFromEditedResume`
+- **Expected behavior**:
+ - `generatedJobRoles` is updated with role suggestions
+ - `generatedLocations` is updated with location suggestions (if implemented)
+ - Handles code-fenced JSON responses
+- **Key assertion**: `expect(state.generatedJobRoles).toEqual(["Role A", "Role B"])`
+
+**Note**: The current implementation generates job roles but may not generate locations. The test validates the role generation workflow.
+
+#### Test Suite 3: Run Matching (Context Integration)
+
+##### 3.6 with parsed resume, sets isMatching true→false and populates matches
+- **What it tests**: End-to-end matching workflow
+- **Setup**:
+ - Upload and parse a resume
+ - Mock `matchJobs` to return fake matches
+ - Run matching
+- **Expected behavior**:
+ - `isMatching` toggles true during matching
+ - `isMatching` returns to false after completion
+ - `matches` array is populated
+- **Key assertion**: `expect(state.matches).toEqual(fakeMatches)`
+
+##### 3.7 accepts an override resume and persists it before matching
+- **What it tests**: Matching with edited/override resume data
+- **Setup**:
+ - Create override resume with Vue skills
+ - Call `runMatching(override)`
+- **Expected behavior**:
+ - Override resume replaces current resume
+ - Matching uses override data
+ - Results reflect override skills
+- **Key assertion**: `expect(get().resume?.parsed?.skills).toEqual(["Vue"])`
+
+##### 3.8 safe early-return when resume.parsed is missing
+- **What it tests**: Defensive programming for missing data
+- **Setup**: Call `runMatching` without parsed resume
+- **Expected behavior**:
+ - No error thrown
+ - `matchJobs` is not called
+ - `matches` remains empty
+- **Key assertion**: `expect(spy).not.toHaveBeenCalled()`
+
+---
+
+## Test Architecture
+
+### Testing Stack
+- **Test Runner**: Vitest (Vite-native test framework)
+- **React Testing**: @testing-library/react
+- **Environment**: jsdom (browser simulation)
+- **Mocking**: Vitest's built-in `vi` mock utilities
+
+### Key Testing Patterns
+
+#### 1. Mocking External APIs
+```typescript
+// Mock Gemini API
+vi.mock("@google/generative-ai", () => ({
+ GoogleGenerativeAI: vi.fn(function() { return mockClient; }),
+}));
+```
+
+#### 2. Testing Async State Updates
+```typescript
+await act(async () => {
+ await get().parseResume();
+});
+```
+
+#### 3. React Context Testing
+```typescript
+// Harness to access context imperatively
+const Grabber: React.FC = () => {
+ const value = useResume();
+ ctx = value;
+ return ;
+};
+```
+
+### Dependencies
+
+The tests require the following packages:
+- `vitest` - Test runner
+- `@testing-library/react` - React component testing utilities
+- `jsdom` - Browser environment simulation
+- `@tensorflow/tfjs` - For Skip-gram vectorization (optional fallback to TF-IDF)
+
+---
+
+## Coverage Metrics
+
+Current test coverage:
+- **Test Files**: 3/3 (100%)
+- **Total Tests**: 13 passed
+- **Core Modules Covered**:
+ - ✅ Matching algorithm (`matcher.ts`)
+ - ✅ Resume parsing (`gemini_parser.ts`)
+ - ✅ Context state management (`ResumeContext.tsx`)
+
+### What's Tested
+- ✅ Skill-based matching and scoring
+- ✅ Vector similarity (TF-IDF/Skip-gram)
+- ✅ JSON parsing and cleaning
+- ✅ File upload workflow
+- ✅ Async state management
+- ✅ Error handling
+- ✅ Edge cases (missing data, empty results)
+
+### What's Not Tested (Future Work)
+- ⚠️ UI components (pages, forms)
+- ⚠️ Adzuna API integration
+- ⚠️ End-to-end user flows
+- ⚠️ Secret vault and encryption
+- ⚠️ Preferences context
+
+---
+
+## Debugging Tests
+
+### Common Issues
+
+#### Issue: TensorFlow.js WebGL errors in tests
+```
+Error: WebGL is not supported on this device
+```
+**Solution**: Tests automatically fall back to CPU backend. This warning can be ignored.
+
+#### Issue: Mock not working
+```
+Error: Gemini API key is required to continue
+```
+**Solution**: Ensure mocks are set up before imports:
+```typescript
+vi.mock("@google/generative-ai", () => ({ ... }));
+import { parseResumeWithGemini } from "...";
+```
+
+#### Issue: State not updating in tests
+**Solution**: Wrap state updates in `act()`:
+```typescript
+await act(async () => {
+ await get().parseResume();
+});
+```
+
+### Debug Commands
+
+```bash
+# Run tests with verbose output
+npx vitest --reporter=verbose
+
+# Run specific test by name
+npx vitest -t "returns matches sorted"
+
+# Debug a single test file
+npx vitest src/lib/__tests__/matcher.test.ts --reporter=verbose
+```
+
+---
+
+## Related Documentation
+
+- **Architecture**: `docs/architecture.mmd` - System architecture diagram
+- **Code Flow**: `docs/code-flow.md` - Detailed code flow explanations
+- **Observability**: `docs/observability.md` - Logging and monitoring
+- **Telemetry**: `telemetry/README.md` - Debugging test cases
+
+---
+
+## Contributing
+
+When adding new features, ensure you:
+1. Write corresponding test cases
+2. Mock external APIs (Gemini, Adzuna)
+3. Test both success and failure paths
+4. Verify error handling
+5. Run full test suite before committing
+
+```bash
+# Before committing
+npm test
+npm run build
+```
diff --git a/hire-shark/eslint.config.js b/hire-shark/eslint.config.js
new file mode 100644
index 0000000..40f72cc
--- /dev/null
+++ b/hire-shark/eslint.config.js
@@ -0,0 +1,26 @@
+import js from "@eslint/js";
+import globals from "globals";
+import reactHooks from "eslint-plugin-react-hooks";
+import reactRefresh from "eslint-plugin-react-refresh";
+import tseslint from "typescript-eslint";
+
+export default tseslint.config(
+ { ignores: ["dist"] },
+ {
+ extends: [js.configs.recommended, ...tseslint.configs.recommended],
+ files: ["**/*.{ts,tsx}"],
+ languageOptions: {
+ ecmaVersion: 2020,
+ globals: globals.browser,
+ },
+ plugins: {
+ "react-hooks": reactHooks,
+ "react-refresh": reactRefresh,
+ },
+ rules: {
+ ...reactHooks.configs.recommended.rules,
+ "react-refresh/only-export-components": ["warn", { allowConstantExport: true }],
+ "@typescript-eslint/no-unused-vars": "off",
+ },
+ },
+);
diff --git a/hire-shark/index.html b/hire-shark/index.html
new file mode 100644
index 0000000..01e12b6
--- /dev/null
+++ b/hire-shark/index.html
@@ -0,0 +1,24 @@
+
+
+
+
+
+ HireShark - Match Your Resume to Perfect Projects
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/hire-shark/package-lock.json b/hire-shark/package-lock.json
new file mode 100644
index 0000000..5584b17
--- /dev/null
+++ b/hire-shark/package-lock.json
@@ -0,0 +1,12144 @@
+{
+ "name": "vite_react_shadcn_ts",
+ "version": "0.0.0",
+ "lockfileVersion": 3,
+ "requires": true,
+ "packages": {
+ "": {
+ "name": "vite_react_shadcn_ts",
+ "version": "0.0.0",
+ "dependencies": {
+ "@google/generative-ai": "^0.24.1",
+ "@hookform/resolvers": "^3.10.0",
+ "@radix-ui/react-accordion": "^1.2.11",
+ "@radix-ui/react-alert-dialog": "^1.1.14",
+ "@radix-ui/react-aspect-ratio": "^1.1.7",
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+ "license": "ISC",
+ "engines": {
+ "node": ">=10"
+ }
+ },
+ "node_modules/yargs": {
+ "version": "17.7.2",
+ "resolved": "https://registry.npmjs.org/yargs/-/yargs-17.7.2.tgz",
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+ "y18n": "^5.0.5",
+ "yargs-parser": "^21.1.1"
+ },
+ "engines": {
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+ "node_modules/yargs-parser": {
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+ "dev": true,
+ "license": "ISC",
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+ "engines": {
+ "node": ">=12"
+ }
+ },
+ "node_modules/yargs/node_modules/ansi-regex": {
+ "version": "5.0.1",
+ "resolved": "https://registry.npmjs.org/ansi-regex/-/ansi-regex-5.0.1.tgz",
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+ "license": "MIT",
+ "peer": true,
+ "engines": {
+ "node": ">=8"
+ }
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+ "node_modules/yargs/node_modules/emoji-regex": {
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+ "resolved": "https://registry.npmjs.org/emoji-regex/-/emoji-regex-8.0.0.tgz",
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+ "license": "MIT",
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+ "resolved": "https://registry.npmjs.org/string-width/-/string-width-4.2.3.tgz",
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+ "license": "MIT",
+ "peer": true,
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+ "emoji-regex": "^8.0.0",
+ "is-fullwidth-code-point": "^3.0.0",
+ "strip-ansi": "^6.0.1"
+ },
+ "engines": {
+ "node": ">=8"
+ }
+ },
+ "node_modules/yargs/node_modules/strip-ansi": {
+ "version": "6.0.1",
+ "resolved": "https://registry.npmjs.org/strip-ansi/-/strip-ansi-6.0.1.tgz",
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+ },
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+ },
+ "node_modules/yauzl": {
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+ "dev": true,
+ "license": "MIT",
+ "peer": true,
+ "dependencies": {
+ "buffer-crc32": "~0.2.3",
+ "fd-slicer": "~1.1.0"
+ }
+ },
+ "node_modules/yocto-queue": {
+ "version": "0.1.0",
+ "resolved": "https://registry.npmjs.org/yocto-queue/-/yocto-queue-0.1.0.tgz",
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+ "dev": true,
+ "license": "MIT",
+ "engines": {
+ "node": ">=10"
+ },
+ "funding": {
+ "url": "https://github.com/sponsors/sindresorhus"
+ }
+ },
+ "node_modules/zod": {
+ "version": "3.25.76",
+ "resolved": "https://registry.npmjs.org/zod/-/zod-3.25.76.tgz",
+ "integrity": "sha512-gzUt/qt81nXsFGKIFcC3YnfEAx5NkunCfnDlvuBSSFS02bcXu4Lmea0AFIUwbLWxWPx3d9p8S5QoaujKcNQxcQ==",
+ "license": "MIT",
+ "funding": {
+ "url": "https://github.com/sponsors/colinhacks"
+ }
+ }
+ }
+}
diff --git a/hire-shark/package.json b/hire-shark/package.json
new file mode 100644
index 0000000..3d82aca
--- /dev/null
+++ b/hire-shark/package.json
@@ -0,0 +1,94 @@
+{
+ "name": "vite_react_shadcn_ts",
+ "private": true,
+ "version": "0.0.0",
+ "type": "module",
+ "scripts": {
+ "dev": "vite",
+ "build": "vite build",
+ "build:dev": "vite build --mode development",
+ "lint": "eslint .",
+ "preview": "vite preview",
+ "diagram:build": "mmdc -i docs/architecture.mmd -o docs/architecture.svg && mmdc -i docs/sequence-upload.mmd -o docs/sequence-upload.svg && mmdc -i docs/sequence-matching.mmd -o docs/sequence-matching.svg && mmdc -i docs/deployment.mmd -o docs/deployment.svg && mmdc -i docs/data-model.mmd -o docs/data-model.svg",
+ "test": "vitest"
+ },
+ "dependencies": {
+ "@google/generative-ai": "^0.24.1",
+ "@hookform/resolvers": "^3.10.0",
+ "@radix-ui/react-accordion": "^1.2.11",
+ "@radix-ui/react-alert-dialog": "^1.1.14",
+ "@radix-ui/react-aspect-ratio": "^1.1.7",
+ "@radix-ui/react-avatar": "^1.1.10",
+ "@radix-ui/react-checkbox": "^1.3.2",
+ "@radix-ui/react-collapsible": "^1.1.11",
+ "@radix-ui/react-context-menu": "^2.2.15",
+ "@radix-ui/react-dialog": "^1.1.14",
+ "@radix-ui/react-dropdown-menu": "^2.1.15",
+ "@radix-ui/react-hover-card": "^1.1.14",
+ "@radix-ui/react-label": "^2.1.7",
+ "@radix-ui/react-menubar": "^1.1.15",
+ "@radix-ui/react-navigation-menu": "^1.2.13",
+ "@radix-ui/react-popover": "^1.1.14",
+ "@radix-ui/react-progress": "^1.1.7",
+ "@radix-ui/react-radio-group": "^1.3.7",
+ "@radix-ui/react-scroll-area": "^1.2.9",
+ "@radix-ui/react-select": "^2.2.5",
+ "@radix-ui/react-separator": "^1.1.7",
+ "@radix-ui/react-slider": "^1.3.5",
+ "@radix-ui/react-slot": "^1.2.3",
+ "@radix-ui/react-switch": "^1.2.5",
+ "@radix-ui/react-tabs": "^1.1.12",
+ "@radix-ui/react-toast": "^1.2.14",
+ "@radix-ui/react-toggle": "^1.1.9",
+ "@radix-ui/react-toggle-group": "^1.1.10",
+ "@radix-ui/react-tooltip": "^1.2.7",
+ "@tanstack/react-query": "^5.83.0",
+ "@tensorflow/tfjs": "^4.22.0",
+ "class-variance-authority": "^0.7.1",
+ "clsx": "^2.1.1",
+ "cmdk": "^1.1.1",
+ "date-fns": "^3.6.0",
+ "embla-carousel-react": "^8.6.0",
+ "input-otp": "^1.4.2",
+ "lucide-react": "^0.462.0",
+ "mammoth": "^1.11.0",
+ "next-themes": "^0.3.0",
+ "pdfjs-dist": "^5.4.296",
+ "react": "^18.3.1",
+ "react-day-picker": "^8.10.1",
+ "react-dom": "^18.3.1",
+ "react-hook-form": "^7.61.1",
+ "react-resizable-panels": "^2.1.9",
+ "react-router-dom": "^6.30.1",
+ "recharts": "^2.15.4",
+ "sonner": "^1.7.4",
+ "tailwind-merge": "^2.6.0",
+ "tailwindcss-animate": "^1.0.7",
+ "vaul": "^0.9.9",
+ "zod": "^3.25.76"
+ },
+ "devDependencies": {
+ "@eslint/js": "^9.32.0",
+ "@mermaid-js/mermaid-cli": "^11.12.0",
+ "@tailwindcss/typography": "^0.5.16",
+ "@testing-library/react": "^16.3.0",
+ "@types/node": "^22.16.5",
+ "@types/react": "^18.3.23",
+ "@types/react-dom": "^18.3.7",
+ "@vitejs/plugin-react-swc": "^3.11.0",
+ "autoprefixer": "^10.4.21",
+ "eslint": "^9.32.0",
+ "eslint-plugin-react-hooks": "^5.2.0",
+ "eslint-plugin-react-refresh": "^0.4.20",
+ "globals": "^15.15.0",
+ "jsdom": "^27.1.0",
+ "lovable-tagger": "^1.1.10",
+ "postcss": "^8.5.6",
+ "tailwindcss": "^3.4.17",
+ "typescript": "^5.8.3",
+ "typescript-eslint": "^8.38.0",
+ "vite": "^5.4.19",
+ "vite-plugin-static-copy": "^3.1.4",
+ "vitest": "^4.0.7"
+ }
+}
diff --git a/hire-shark/postcss.config.js b/hire-shark/postcss.config.js
new file mode 100644
index 0000000..2aa7205
--- /dev/null
+++ b/hire-shark/postcss.config.js
@@ -0,0 +1,6 @@
+export default {
+ plugins: {
+ tailwindcss: {},
+ autoprefixer: {},
+ },
+};
diff --git a/hire-shark/prompts/data_connectors_and_models.md b/hire-shark/prompts/data_connectors_and_models.md
new file mode 100644
index 0000000..b3d14c2
--- /dev/null
+++ b/hire-shark/prompts/data_connectors_and_models.md
@@ -0,0 +1,52 @@
+# Data Connectors & Model Settings
+
+This reference summarizes every external connector and model configuration that powers HireShark’s prompt flows..
+
+---
+
+## 1. Google Gemini (LLM)
+- **Used for:**
+ - Resume parsing (`hire-shark/src/lib/gemini_parser.ts`)
+ - Job-role ideation (`hire-shark/src/store/ResumeContext.tsx`)
+- **SDK:** `@google/generative-ai`
+- **Model ID:** `gemini-2.5-flash`
+- **Auth:** Browser prompts for a shared password, decrypts the `VITE_ENCODED_SECRET` payload via `secretVault.ts`, and loads Gemini + Adzuna credentials entirely in-memory (users can still paste their own key when prompted).
+- **Invocation pattern:**
+ ```ts
+ const model = genAI.getGenerativeModel({ model: "gemini-2.5-flash" });
+ const result = await model.generateContent(promptOrParts);
+ const text = await result.response.text();
+ ```
+- **Output handling:** strip ``` ``` fences, parse JSON with `JSON.parse`.
+- **Notes:** prompts expect concise JSON arrays.
+
+---
+
+## 2. Adzuna Jobs API (Data Connector)
+- **Used for:** Real job listings during matching (`hire-shark/src/lib/adzunaApi.ts`)
+- **Base URL:** `https://api.adzuna.com/v1/api/jobs`
+- **Region path:** `/us/search/1`
+- **Auth query params:**
+ - `app_id = VITE_ADZUNA_APP_ID`
+ - `app_key = VITE_ADZUNA_APP_KEY`
+- **Other query params set by app:**
+ - `results_per_page=10`
+ - `what` (job role/custom role; hyphens normalized to spaces)
+ - `salary_min`, `salary_max` (optional)
+ - `full_time`, `part_time`, `contract` (mutually exclusive flags)
+- **Response mapping:** see `matchJobs` → `MatchResult` (title, company, description snippet, location, salary string, job type, URLs, posted date).
+- **Notes:** This connector is purely REST/JSON; no LLM involved, but prompt outputs (generated job roles) influence the `what` query.
+
+---
+
+## 3. JD Skill Extraction Flow
+- **Used for:** Building the JD skill list that powers match scoring (`hire-shark/src/lib/skillExtractor.ts`).
+- **Inputs:** Job title, sanitized Adzuna description (up to ~3.5k chars) and any keywords returned by the API.
+- **LLM layer (required when enabled):**
+ - Model: `gemini-2.5-flash` via `@google/generative-ai`.
+ - Generation config: `temperature=0`, `topK=1`, `topP=0.1` for deterministic output.
+ - Prompt: see `prompts.md` ("JD Skill Extraction"). Asks for inferred skills (implied by responsibilities), excludes location/schedule/pay/benefits, outputs JSON array of 1–4 word skill phrases.
+- **Fallback when LLM unavailable:** returns provided keywords only (no heuristic phrase parsing).
+- **Post-processing:**
+ - Sanitization + deduping of LLM output; stable ordering by first appearance in the JD text, then alphabetically.
+ - In-memory cache keyed by title/description/keywords/limit so repeated matches reuse the same list.
diff --git a/hire-shark/prompts/prompts.md b/hire-shark/prompts/prompts.md
new file mode 100644
index 0000000..66204f9
--- /dev/null
+++ b/hire-shark/prompts/prompts.md
@@ -0,0 +1,81 @@
+# List of Prompts used in HireShark
+This document lists every LLM prompt that ships with the HireShark app. Keep it in sync whenever a prompt string changes in code.
+
+---
+
+## Resume Parsing (hire-shark/src/lib/gemini_parser.ts)
+```
+Extract the following information from the resume and return it as a JSON object.
+The JSON object should have the following structure:
+{
+ "name": "",
+ "email": "",
+ "phone": "",
+ "location": "",
+ "summary": "",
+ "skills": [],
+ "education": [
+ {
+ "degree": "",
+ "field": "",
+ "institution": "",
+ "location": "",
+ "start": "",
+ "end": "",
+ "gpa": "",
+ "honors": []
+ }
+ ],
+ "experiences": [
+ {
+ "company": "",
+ "title": "",
+ "start": "",
+ "end": "",
+ "bullets": []
+ }
+ ],
+ "confidence": {
+ "personalInfo": 0.0,
+ "experience": 0.0,
+ "skills": 0.0,
+ "education": 0.0
+ }
+}
+
+Important: Extract all education entries including degrees, certifications, and educational qualifications.
+Include the degree type (e.g., Bachelor's, Master's, PhD), field of study, institution name, location, dates, GPA (if available), and any honors or distinctions.
+If you cannot find any information for a field, leave it as an empty string or an empty array.
+If the uploaded file is not a resume, try your best and you should still return a JSON object.
+When nothing is found for a section, The confidence for that section should be low.
+```
+
+---
+
+## Job Role Suggestions (hire-shark/src/store/ResumeContext.tsx)
+```
+Based on the following resume, generate a list of 5-10 potential job roles that would be a good fit for this person. Return the list as a JSON array of strings. Job title should be concise and simple.
+
+Resume:
+${JSON.stringify(editedResume.parsed, null, 2)}
+```
+
+---
+
+## JD Skill Extraction (hire-shark/src/lib/skillExtractor.ts)
+```
+You extract the most important skills a candidate needs for this job. Infer required skills even if only implied by responsibilities.
+- Focus on technologies, tools, frameworks, domain skills, certifications, and relevant soft skills.
+- Exclude locations, schedules (hours/week), pay/benefits, employment type, headcount, and generic nouns.
+- Return ONLY a JSON array (no commentary) of up to ${limit} unique skill phrases, each 1-4 words, title-cased when appropriate.
+
+Job Title: ${title || "(missing)"}
+
+Job Description:
+${description || "(missing)"}
+
+Existing keywords: ${keywords.length ? keywords.join(", ") : "none"}
+```
+Notes:
+- Called with `temperature: 0`, `topK: 1`, `topP: 0.1` for deterministic output.
+- LLM-only: no heuristic fallback; if Gemini unavailable, it falls back to provided keywords.
diff --git a/hire-shark/public/404.html b/hire-shark/public/404.html
new file mode 100644
index 0000000..f084551
--- /dev/null
+++ b/hire-shark/public/404.html
@@ -0,0 +1,13 @@
+
+
+
+
+ Redirecting...
+
+
+
+
+
\ No newline at end of file
diff --git a/hire-shark/public/data/greenhouse_full_jobs.csv b/hire-shark/public/data/greenhouse_full_jobs.csv
new file mode 100644
index 0000000..e51534a
--- /dev/null
+++ b/hire-shark/public/data/greenhouse_full_jobs.csv
@@ -0,0 +1,6 @@
+id,title,company,location,employment_type,description,keywords,apply_url
+1,Senior Data Analyst,Greenhouse,Remote,Full-time,"Analyze recruiting data to drive insights into talent pipelines.","data analysis;sql;python;business intelligence","https://example.com/jobs/1"
+2,Frontend Engineer,Acme Corp,San Francisco CA,Full-time,"Build React applications that power the customer experience.","react;javascript;css;ui","https://example.com/jobs/2"
+3,Machine Learning Engineer,Open ML,New York NY,Full-time,"Develop machine learning models for hiring recommendations.","machine learning;python;tensorflow;data science","https://example.com/jobs/3"
+4,Product Manager,TalentWorks,Remote,Full-time,"Define product strategy for hiring tools and lead cross-functional teams.","product management;roadmap;stakeholder communication","https://example.com/jobs/4"
+5,Data Scientist,Analytics Hub,Boston MA,Contract,"Partner with stakeholders to design experiments and deliver insights.","data science;r;python;statistics","https://example.com/jobs/5"
diff --git a/hire-shark/public/favicon.ico b/hire-shark/public/favicon.ico
new file mode 100644
index 0000000..dd5a126
Binary files /dev/null and b/hire-shark/public/favicon.ico differ
diff --git a/hire-shark/public/placeholder.svg b/hire-shark/public/placeholder.svg
new file mode 100644
index 0000000..e763910
--- /dev/null
+++ b/hire-shark/public/placeholder.svg
@@ -0,0 +1 @@
+
\ No newline at end of file
diff --git a/hire-shark/public/robots.txt b/hire-shark/public/robots.txt
new file mode 100644
index 0000000..6018e70
--- /dev/null
+++ b/hire-shark/public/robots.txt
@@ -0,0 +1,14 @@
+User-agent: Googlebot
+Allow: /
+
+User-agent: Bingbot
+Allow: /
+
+User-agent: Twitterbot
+Allow: /
+
+User-agent: facebookexternalhit
+Allow: /
+
+User-agent: *
+Allow: /
diff --git a/hire-shark/scripts/encrypt_secrets.py b/hire-shark/scripts/encrypt_secrets.py
new file mode 100644
index 0000000..c6a03e0
--- /dev/null
+++ b/hire-shark/scripts/encrypt_secrets.py
@@ -0,0 +1,88 @@
+import base64
+import json
+import os
+from getpass import getpass
+from hashlib import pbkdf2_hmac
+from pathlib import Path
+from secrets import token_bytes
+
+try:
+ from cryptography.hazmat.primitives.ciphers.aead import AESGCM
+except ImportError as error:
+ raise SystemExit("The 'cryptography' package is required. Install it with 'pip install cryptography'.") from error
+
+REPO_ROOT = Path(__file__).resolve().parents[1]
+ENV_PATH = REPO_ROOT / ".env"
+ITERATIONS = 200_000
+
+
+def load_env_variables() -> dict[str, str]:
+ if not ENV_PATH.exists():
+ raise SystemExit(f".env file not found at {ENV_PATH}")
+
+ env_values: dict[str, str] = {}
+ with ENV_PATH.open("r", encoding="utf-8") as env_file:
+ for raw_line in env_file:
+ line = raw_line.strip()
+ if not line or line.startswith("#") or "=" not in line:
+ continue
+ key, value = line.split("=", 1)
+ env_values[key.strip()] = value.strip().strip('"').strip("'")
+ return env_values
+
+
+def build_plaintext(env_values: dict[str, str]) -> bytes:
+ required = ["VITE_GEMINI_API_KEY", "VITE_ADZUNA_APP_ID", "VITE_ADZUNA_APP_KEY"]
+ missing = [key for key in required if not env_values.get(key)]
+ if missing:
+ joined = ", ".join(missing)
+ raise SystemExit(f"Missing required values in .env: {joined}")
+
+ secret_payload = {
+ "geminiApiKey": env_values["VITE_GEMINI_API_KEY"],
+ "adzunaAppId": env_values["VITE_ADZUNA_APP_ID"],
+ "adzunaAppKey": env_values["VITE_ADZUNA_APP_KEY"],
+ }
+ return json.dumps(secret_payload, separators=(",", ":")).encode("utf-8")
+
+
+def derive_key(password: str, salt: bytes) -> bytes:
+ return pbkdf2_hmac("sha256", password.encode("utf-8"), salt, ITERATIONS, dklen=32)
+
+
+def encrypt_payload(password: str, plaintext: bytes) -> dict[str, str | int]:
+ salt = token_bytes(16)
+ iv = token_bytes(12)
+ key = derive_key(password, salt)
+ aesgcm = AESGCM(key)
+ ciphertext = aesgcm.encrypt(iv, plaintext, None)
+ return {
+ "version": 1,
+ "iterations": ITERATIONS,
+ "salt": base64.b64encode(salt).decode("utf-8"),
+ "iv": base64.b64encode(iv).decode("utf-8"),
+ "ciphertext": base64.b64encode(ciphertext).decode("utf-8"),
+ }
+
+
+def main() -> None:
+ env_values = load_env_variables()
+ plaintext = build_plaintext(env_values)
+ password = getpass("Enter the shared password to protect your API keys: ").strip()
+ if not password:
+ raise SystemExit("Password is required to encrypt secrets.")
+
+ payload = encrypt_payload(password, plaintext)
+ encoded_secret = base64.b64encode(json.dumps(payload).encode("utf-8")).decode("utf-8")
+
+ os.system("cls" if os.name == "nt" else "clear")
+ print("=== HireShark Secret Vault ===")
+ print("Password (share securely with trusted testers):")
+ print(password)
+ print("\nEncoded secret (store in repo secret VITE_ENCODED_SECRET and local .env as VITE_ENCODED_SECRET):")
+ print(encoded_secret)
+ print("\nReminder: remove plain-text API keys from .env when you're done.")
+
+
+if __name__ == "__main__":
+ main()
diff --git a/hire-shark/src/App.css b/hire-shark/src/App.css
new file mode 100644
index 0000000..b9d355d
--- /dev/null
+++ b/hire-shark/src/App.css
@@ -0,0 +1,42 @@
+#root {
+ max-width: 1280px;
+ margin: 0 auto;
+ padding: 2rem;
+ text-align: center;
+}
+
+.logo {
+ height: 6em;
+ padding: 1.5em;
+ will-change: filter;
+ transition: filter 300ms;
+}
+.logo:hover {
+ filter: drop-shadow(0 0 2em #646cffaa);
+}
+.logo.react:hover {
+ filter: drop-shadow(0 0 2em #61dafbaa);
+}
+
+@keyframes logo-spin {
+ from {
+ transform: rotate(0deg);
+ }
+ to {
+ transform: rotate(360deg);
+ }
+}
+
+@media (prefers-reduced-motion: no-preference) {
+ a:nth-of-type(2) .logo {
+ animation: logo-spin infinite 20s linear;
+ }
+}
+
+.card {
+ padding: 2em;
+}
+
+.read-the-docs {
+ color: #888;
+}
diff --git a/hire-shark/src/App.tsx b/hire-shark/src/App.tsx
new file mode 100644
index 0000000..4d791b0
--- /dev/null
+++ b/hire-shark/src/App.tsx
@@ -0,0 +1,44 @@
+import { Toaster } from "@/components/ui/toaster";
+import { Toaster as Sonner } from "@/components/ui/sonner";
+import { TooltipProvider } from "@/components/ui/tooltip";
+import { QueryClient, QueryClientProvider } from "@tanstack/react-query";
+import { BrowserRouter, Routes, Route } from "react-router-dom";
+import { PreferencesProvider } from "./store/PreferencesContext";
+import { ResumeProvider } from "./store/ResumeContext";
+import { ThemeProvider } from "./components/ThemeProvider";
+import Landing from "./pages/Landing";
+import Upload from "./pages/Upload";
+import Review from "./pages/Review";
+import Preferences from "./pages/Preferences";
+import Matches from "./pages/Matches";
+import NotFound from "./pages/NotFound";
+
+const queryClient = new QueryClient();
+
+const App = () => (
+
+
+
+
+
+
+
+
+
+ } />
+ } />
+ } />
+ } />
+ } />
+ {/* ADD ALL CUSTOM ROUTES ABOVE THE CATCH-ALL "*" ROUTE */}
+ } />
+
+
+
+
+
+
+
+);
+
+export default App;
diff --git a/hire-shark/src/assets/logo.png b/hire-shark/src/assets/logo.png
new file mode 100644
index 0000000..b017302
Binary files /dev/null and b/hire-shark/src/assets/logo.png differ
diff --git a/hire-shark/src/components/GeneratingRolesModal.tsx b/hire-shark/src/components/GeneratingRolesModal.tsx
new file mode 100644
index 0000000..b40c7be
--- /dev/null
+++ b/hire-shark/src/components/GeneratingRolesModal.tsx
@@ -0,0 +1,29 @@
+import { Dialog, DialogContent, DialogHeader, DialogTitle, DialogFooter } from "@/components/ui/dialog";
+import { Button } from "@/components/ui/button";
+
+interface GeneratingRolesModalProps {
+ isOpen: boolean;
+ onClose: () => void;
+}
+
+export const GeneratingRolesModal = ({ isOpen, onClose }: GeneratingRolesModalProps) => {
+ return (
+
+ );
+};
\ No newline at end of file
diff --git a/hire-shark/src/components/Header.tsx b/hire-shark/src/components/Header.tsx
new file mode 100644
index 0000000..cda1515
--- /dev/null
+++ b/hire-shark/src/components/Header.tsx
@@ -0,0 +1,110 @@
+import { useEffect, useState } from "react";
+import { Link, useLocation, useNavigate } from "react-router-dom";
+import { Button } from "@/components/ui/button";
+import logo from "@/assets/logo.png";
+import { useTheme } from "next-themes";
+import { Sun, Moon } from "lucide-react";
+
+export const Header = () => {
+ const location = useLocation();
+ const navigate = useNavigate();
+ const isLanding = location.pathname === "/";
+ const { theme, setTheme } = useTheme();
+ const [mounted, setMounted] = useState(false);
+
+ useEffect(() => {
+ setMounted(true);
+ }, []);
+
+ const currentTheme = (theme as string | undefined) ?? "light";
+
+ return (
+
+
+
+

+
HireShark
+
+
+
+
+
+ {mounted && (
+
+
+
+
+ )}
+
+
+
+
+ );
+};
diff --git a/hire-shark/src/components/LoadingModal.tsx b/hire-shark/src/components/LoadingModal.tsx
new file mode 100644
index 0000000..6dd2d39
--- /dev/null
+++ b/hire-shark/src/components/LoadingModal.tsx
@@ -0,0 +1,25 @@
+import { Dialog, DialogContent, DialogHeader, DialogTitle, DialogFooter } from "@/components/ui/dialog";
+import { Button } from "@/components/ui/button";
+
+interface LoadingModalProps {
+ isOpen: boolean;
+ onClose: () => void;
+}
+
+export const LoadingModal = ({ isOpen, onClose }: LoadingModalProps) => {
+ return (
+
+ );
+};
\ No newline at end of file
diff --git a/hire-shark/src/components/ProgressSteps.tsx b/hire-shark/src/components/ProgressSteps.tsx
new file mode 100644
index 0000000..23b8403
--- /dev/null
+++ b/hire-shark/src/components/ProgressSteps.tsx
@@ -0,0 +1,80 @@
+import { Check } from "lucide-react";
+import { cn } from "@/lib/utils";
+
+interface ProgressStepsProps {
+ currentStep: number;
+}
+
+const STEP_DEFS = [
+ { number: 1, label: "Upload Resume" },
+ { number: 2, label: "Review Extraction" },
+ { number: 3, label: "Set Preferences" },
+ { number: 4, label: "Find Matches" },
+] as const;
+
+export const ProgressSteps = ({ currentStep }: ProgressStepsProps) => {
+ return (
+
+
+ {STEP_DEFS.map((step, index) => {
+ const isLastStep = step.number === STEP_DEFS.length;
+ const isCompleted =
+ currentStep > step.number || (isLastStep && currentStep === step.number);
+ const isActive = currentStep === step.number;
+ const hasReached = currentStep >= step.number;
+
+ return (
+
+ {index > 0 && (
+
+ )}
+
+
+
+ {isCompleted ? : step.number}
+
+
+ {step.label}
+
+
+
+ {index < STEP_DEFS.length - 1 && (
+
step.number ? "bg-success" : "bg-border"
+ )}
+ />
+ )}
+
+ );
+ })}
+
+
+ );
+};
diff --git a/hire-shark/src/components/ThemeProvider.tsx b/hire-shark/src/components/ThemeProvider.tsx
new file mode 100644
index 0000000..4f3f235
--- /dev/null
+++ b/hire-shark/src/components/ThemeProvider.tsx
@@ -0,0 +1,12 @@
+import { ThemeProvider as NextThemesProvider } from "next-themes";
+import type { ReactNode } from "react";
+
+interface ThemeProviderProps {
+ children: ReactNode;
+}
+
+export const ThemeProvider = ({ children }: ThemeProviderProps) => (
+
+ {children}
+
+);
diff --git a/hire-shark/src/components/ui/accordion.tsx b/hire-shark/src/components/ui/accordion.tsx
new file mode 100644
index 0000000..1e7878c
--- /dev/null
+++ b/hire-shark/src/components/ui/accordion.tsx
@@ -0,0 +1,52 @@
+import * as React from "react";
+import * as AccordionPrimitive from "@radix-ui/react-accordion";
+import { ChevronDown } from "lucide-react";
+
+import { cn } from "@/lib/utils";
+
+const Accordion = AccordionPrimitive.Root;
+
+const AccordionItem = React.forwardRef<
+ React.ElementRef
,
+ React.ComponentPropsWithoutRef
+>(({ className, ...props }, ref) => (
+
+));
+AccordionItem.displayName = "AccordionItem";
+
+const AccordionTrigger = React.forwardRef<
+ React.ElementRef,
+ React.ComponentPropsWithoutRef
+>(({ className, children, ...props }, ref) => (
+
+ svg]:rotate-180",
+ className,
+ )}
+ {...props}
+ >
+ {children}
+
+
+
+));
+AccordionTrigger.displayName = AccordionPrimitive.Trigger.displayName;
+
+const AccordionContent = React.forwardRef<
+ React.ElementRef,
+ React.ComponentPropsWithoutRef
+>(({ className, children, ...props }, ref) => (
+
+ {children}
+
+));
+
+AccordionContent.displayName = AccordionPrimitive.Content.displayName;
+
+export { Accordion, AccordionItem, AccordionTrigger, AccordionContent };
diff --git a/hire-shark/src/components/ui/alert-dialog.tsx b/hire-shark/src/components/ui/alert-dialog.tsx
new file mode 100644
index 0000000..6dfbfb4
--- /dev/null
+++ b/hire-shark/src/components/ui/alert-dialog.tsx
@@ -0,0 +1,104 @@
+import * as React from "react";
+import * as AlertDialogPrimitive from "@radix-ui/react-alert-dialog";
+
+import { cn } from "@/lib/utils";
+import { buttonVariants } from "@/components/ui/button";
+
+const AlertDialog = AlertDialogPrimitive.Root;
+
+const AlertDialogTrigger = AlertDialogPrimitive.Trigger;
+
+const AlertDialogPortal = AlertDialogPrimitive.Portal;
+
+const AlertDialogOverlay = React.forwardRef<
+ React.ElementRef,
+ React.ComponentPropsWithoutRef
+>(({ className, ...props }, ref) => (
+
+));
+AlertDialogOverlay.displayName = AlertDialogPrimitive.Overlay.displayName;
+
+const AlertDialogContent = React.forwardRef<
+ React.ElementRef,
+ React.ComponentPropsWithoutRef
+>(({ className, ...props }, ref) => (
+
+
+
+
+));
+AlertDialogContent.displayName = AlertDialogPrimitive.Content.displayName;
+
+const AlertDialogHeader = ({ className, ...props }: React.HTMLAttributes) => (
+
+);
+AlertDialogHeader.displayName = "AlertDialogHeader";
+
+const AlertDialogFooter = ({ className, ...props }: React.HTMLAttributes) => (
+
+);
+AlertDialogFooter.displayName = "AlertDialogFooter";
+
+const AlertDialogTitle = React.forwardRef<
+ React.ElementRef,
+ React.ComponentPropsWithoutRef
+>(({ className, ...props }, ref) => (
+
+));
+AlertDialogTitle.displayName = AlertDialogPrimitive.Title.displayName;
+
+const AlertDialogDescription = React.forwardRef<
+ React.ElementRef,
+ React.ComponentPropsWithoutRef
+>(({ className, ...props }, ref) => (
+
+));
+AlertDialogDescription.displayName = AlertDialogPrimitive.Description.displayName;
+
+const AlertDialogAction = React.forwardRef<
+ React.ElementRef,
+ React.ComponentPropsWithoutRef
+>(({ className, ...props }, ref) => (
+
+));
+AlertDialogAction.displayName = AlertDialogPrimitive.Action.displayName;
+
+const AlertDialogCancel = React.forwardRef<
+ React.ElementRef,
+ React.ComponentPropsWithoutRef
+>(({ className, ...props }, ref) => (
+
+));
+AlertDialogCancel.displayName = AlertDialogPrimitive.Cancel.displayName;
+
+export {
+ AlertDialog,
+ AlertDialogPortal,
+ AlertDialogOverlay,
+ AlertDialogTrigger,
+ AlertDialogContent,
+ AlertDialogHeader,
+ AlertDialogFooter,
+ AlertDialogTitle,
+ AlertDialogDescription,
+ AlertDialogAction,
+ AlertDialogCancel,
+};
diff --git a/hire-shark/src/components/ui/alert.tsx b/hire-shark/src/components/ui/alert.tsx
new file mode 100644
index 0000000..2efc3c8
--- /dev/null
+++ b/hire-shark/src/components/ui/alert.tsx
@@ -0,0 +1,43 @@
+import * as React from "react";
+import { cva, type VariantProps } from "class-variance-authority";
+
+import { cn } from "@/lib/utils";
+
+const alertVariants = cva(
+ "relative w-full rounded-lg border p-4 [&>svg~*]:pl-7 [&>svg+div]:translate-y-[-3px] [&>svg]:absolute [&>svg]:left-4 [&>svg]:top-4 [&>svg]:text-foreground",
+ {
+ variants: {
+ variant: {
+ default: "bg-background text-foreground",
+ destructive: "border-destructive/50 text-destructive dark:border-destructive [&>svg]:text-destructive",
+ },
+ },
+ defaultVariants: {
+ variant: "default",
+ },
+ },
+);
+
+const Alert = React.forwardRef<
+ HTMLDivElement,
+ React.HTMLAttributes & VariantProps
+>(({ className, variant, ...props }, ref) => (
+
+));
+Alert.displayName = "Alert";
+
+const AlertTitle = React.forwardRef>(
+ ({ className, ...props }, ref) => (
+
+ ),
+);
+AlertTitle.displayName = "AlertTitle";
+
+const AlertDescription = React.forwardRef>(
+ ({ className, ...props }, ref) => (
+
+ ),
+);
+AlertDescription.displayName = "AlertDescription";
+
+export { Alert, AlertTitle, AlertDescription };
diff --git a/hire-shark/src/components/ui/aspect-ratio.tsx b/hire-shark/src/components/ui/aspect-ratio.tsx
new file mode 100644
index 0000000..c9e6f4b
--- /dev/null
+++ b/hire-shark/src/components/ui/aspect-ratio.tsx
@@ -0,0 +1,5 @@
+import * as AspectRatioPrimitive from "@radix-ui/react-aspect-ratio";
+
+const AspectRatio = AspectRatioPrimitive.Root;
+
+export { AspectRatio };
diff --git a/hire-shark/src/components/ui/avatar.tsx b/hire-shark/src/components/ui/avatar.tsx
new file mode 100644
index 0000000..68d21bb
--- /dev/null
+++ b/hire-shark/src/components/ui/avatar.tsx
@@ -0,0 +1,38 @@
+import * as React from "react";
+import * as AvatarPrimitive from "@radix-ui/react-avatar";
+
+import { cn } from "@/lib/utils";
+
+const Avatar = React.forwardRef<
+ React.ElementRef,
+ React.ComponentPropsWithoutRef
+>(({ className, ...props }, ref) => (
+
+));
+Avatar.displayName = AvatarPrimitive.Root.displayName;
+
+const AvatarImage = React.forwardRef<
+ React.ElementRef,
+ React.ComponentPropsWithoutRef
+>(({ className, ...props }, ref) => (
+
+));
+AvatarImage.displayName = AvatarPrimitive.Image.displayName;
+
+const AvatarFallback = React.forwardRef<
+ React.ElementRef,
+ React.ComponentPropsWithoutRef
+>(({ className, ...props }, ref) => (
+
+));
+AvatarFallback.displayName = AvatarPrimitive.Fallback.displayName;
+
+export { Avatar, AvatarImage, AvatarFallback };
diff --git a/hire-shark/src/components/ui/badge.tsx b/hire-shark/src/components/ui/badge.tsx
new file mode 100644
index 0000000..0853c44
--- /dev/null
+++ b/hire-shark/src/components/ui/badge.tsx
@@ -0,0 +1,29 @@
+import * as React from "react";
+import { cva, type VariantProps } from "class-variance-authority";
+
+import { cn } from "@/lib/utils";
+
+const badgeVariants = cva(
+ "inline-flex items-center rounded-full border px-2.5 py-0.5 text-xs font-semibold transition-colors focus:outline-none focus:ring-2 focus:ring-ring focus:ring-offset-2",
+ {
+ variants: {
+ variant: {
+ default: "border-transparent bg-primary text-primary-foreground hover:bg-primary/80",
+ secondary: "border-transparent bg-secondary text-secondary-foreground hover:bg-secondary/80",
+ destructive: "border-transparent bg-destructive text-destructive-foreground hover:bg-destructive/80",
+ outline: "text-foreground",
+ },
+ },
+ defaultVariants: {
+ variant: "default",
+ },
+ },
+);
+
+export interface BadgeProps extends React.HTMLAttributes, VariantProps {}
+
+function Badge({ className, variant, ...props }: BadgeProps) {
+ return ;
+}
+
+export { Badge, badgeVariants };
diff --git a/hire-shark/src/components/ui/breadcrumb.tsx b/hire-shark/src/components/ui/breadcrumb.tsx
new file mode 100644
index 0000000..ca91ff5
--- /dev/null
+++ b/hire-shark/src/components/ui/breadcrumb.tsx
@@ -0,0 +1,90 @@
+import * as React from "react";
+import { Slot } from "@radix-ui/react-slot";
+import { ChevronRight, MoreHorizontal } from "lucide-react";
+
+import { cn } from "@/lib/utils";
+
+const Breadcrumb = React.forwardRef<
+ HTMLElement,
+ React.ComponentPropsWithoutRef<"nav"> & {
+ separator?: React.ReactNode;
+ }
+>(({ ...props }, ref) => );
+Breadcrumb.displayName = "Breadcrumb";
+
+const BreadcrumbList = React.forwardRef>(
+ ({ className, ...props }, ref) => (
+
+ ),
+);
+BreadcrumbList.displayName = "BreadcrumbList";
+
+const BreadcrumbItem = React.forwardRef>(
+ ({ className, ...props }, ref) => (
+
+ ),
+);
+BreadcrumbItem.displayName = "BreadcrumbItem";
+
+const BreadcrumbLink = React.forwardRef<
+ HTMLAnchorElement,
+ React.ComponentPropsWithoutRef<"a"> & {
+ asChild?: boolean;
+ }
+>(({ asChild, className, ...props }, ref) => {
+ const Comp = asChild ? Slot : "a";
+
+ return ;
+});
+BreadcrumbLink.displayName = "BreadcrumbLink";
+
+const BreadcrumbPage = React.forwardRef>(
+ ({ className, ...props }, ref) => (
+
+ ),
+);
+BreadcrumbPage.displayName = "BreadcrumbPage";
+
+const BreadcrumbSeparator = ({ children, className, ...props }: React.ComponentProps<"li">) => (
+ svg]:size-3.5", className)} {...props}>
+ {children ?? }
+
+);
+BreadcrumbSeparator.displayName = "BreadcrumbSeparator";
+
+const BreadcrumbEllipsis = ({ className, ...props }: React.ComponentProps<"span">) => (
+
+
+ More
+
+);
+BreadcrumbEllipsis.displayName = "BreadcrumbElipssis";
+
+export {
+ Breadcrumb,
+ BreadcrumbList,
+ BreadcrumbItem,
+ BreadcrumbLink,
+ BreadcrumbPage,
+ BreadcrumbSeparator,
+ BreadcrumbEllipsis,
+};
diff --git a/hire-shark/src/components/ui/button.tsx b/hire-shark/src/components/ui/button.tsx
new file mode 100644
index 0000000..cdedd4f
--- /dev/null
+++ b/hire-shark/src/components/ui/button.tsx
@@ -0,0 +1,47 @@
+import * as React from "react";
+import { Slot } from "@radix-ui/react-slot";
+import { cva, type VariantProps } from "class-variance-authority";
+
+import { cn } from "@/lib/utils";
+
+const buttonVariants = cva(
+ "inline-flex items-center justify-center gap-2 whitespace-nowrap rounded-md text-sm font-medium ring-offset-background transition-colors focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-ring focus-visible:ring-offset-2 disabled:pointer-events-none disabled:opacity-50 [&_svg]:pointer-events-none [&_svg]:size-4 [&_svg]:shrink-0",
+ {
+ variants: {
+ variant: {
+ default: "bg-primary text-primary-foreground hover:bg-primary/90",
+ destructive: "bg-destructive text-destructive-foreground hover:bg-destructive/90",
+ outline: "border border-input bg-background hover:bg-accent hover:text-accent-foreground",
+ secondary: "bg-secondary text-secondary-foreground hover:bg-secondary/80",
+ ghost: "hover:bg-accent hover:text-accent-foreground",
+ link: "text-primary underline-offset-4 hover:underline",
+ },
+ size: {
+ default: "h-10 px-4 py-2",
+ sm: "h-9 rounded-md px-3",
+ lg: "h-11 rounded-md px-8",
+ icon: "h-10 w-10",
+ },
+ },
+ defaultVariants: {
+ variant: "default",
+ size: "default",
+ },
+ },
+);
+
+export interface ButtonProps
+ extends React.ButtonHTMLAttributes,
+ VariantProps {
+ asChild?: boolean;
+}
+
+const Button = React.forwardRef(
+ ({ className, variant, size, asChild = false, ...props }, ref) => {
+ const Comp = asChild ? Slot : "button";
+ return ;
+ },
+);
+Button.displayName = "Button";
+
+export { Button, buttonVariants };
diff --git a/hire-shark/src/components/ui/calendar.tsx b/hire-shark/src/components/ui/calendar.tsx
new file mode 100644
index 0000000..900a69e
--- /dev/null
+++ b/hire-shark/src/components/ui/calendar.tsx
@@ -0,0 +1,54 @@
+import * as React from "react";
+import { ChevronLeft, ChevronRight } from "lucide-react";
+import { DayPicker } from "react-day-picker";
+
+import { cn } from "@/lib/utils";
+import { buttonVariants } from "@/components/ui/button";
+
+export type CalendarProps = React.ComponentProps;
+
+function Calendar({ className, classNames, showOutsideDays = true, ...props }: CalendarProps) {
+ return (
+ ,
+ IconRight: ({ ..._props }) => ,
+ }}
+ {...props}
+ />
+ );
+}
+Calendar.displayName = "Calendar";
+
+export { Calendar };
diff --git a/hire-shark/src/components/ui/card.tsx b/hire-shark/src/components/ui/card.tsx
new file mode 100644
index 0000000..e282748
--- /dev/null
+++ b/hire-shark/src/components/ui/card.tsx
@@ -0,0 +1,43 @@
+import * as React from "react";
+
+import { cn } from "@/lib/utils";
+
+const Card = React.forwardRef>(({ className, ...props }, ref) => (
+
+));
+Card.displayName = "Card";
+
+const CardHeader = React.forwardRef>(
+ ({ className, ...props }, ref) => (
+
+ ),
+);
+CardHeader.displayName = "CardHeader";
+
+const CardTitle = React.forwardRef>(
+ ({ className, ...props }, ref) => (
+
+ ),
+);
+CardTitle.displayName = "CardTitle";
+
+const CardDescription = React.forwardRef>(
+ ({ className, ...props }, ref) => (
+
+ ),
+);
+CardDescription.displayName = "CardDescription";
+
+const CardContent = React.forwardRef>(
+ ({ className, ...props }, ref) => ,
+);
+CardContent.displayName = "CardContent";
+
+const CardFooter = React.forwardRef>(
+ ({ className, ...props }, ref) => (
+
+ ),
+);
+CardFooter.displayName = "CardFooter";
+
+export { Card, CardHeader, CardFooter, CardTitle, CardDescription, CardContent };
diff --git a/hire-shark/src/components/ui/carousel.tsx b/hire-shark/src/components/ui/carousel.tsx
new file mode 100644
index 0000000..3aa0f31
--- /dev/null
+++ b/hire-shark/src/components/ui/carousel.tsx
@@ -0,0 +1,224 @@
+import * as React from "react";
+import useEmblaCarousel, { type UseEmblaCarouselType } from "embla-carousel-react";
+import { ArrowLeft, ArrowRight } from "lucide-react";
+
+import { cn } from "@/lib/utils";
+import { Button } from "@/components/ui/button";
+
+type CarouselApi = UseEmblaCarouselType[1];
+type UseCarouselParameters = Parameters;
+type CarouselOptions = UseCarouselParameters[0];
+type CarouselPlugin = UseCarouselParameters[1];
+
+type CarouselProps = {
+ opts?: CarouselOptions;
+ plugins?: CarouselPlugin;
+ orientation?: "horizontal" | "vertical";
+ setApi?: (api: CarouselApi) => void;
+};
+
+type CarouselContextProps = {
+ carouselRef: ReturnType[0];
+ api: ReturnType[1];
+ scrollPrev: () => void;
+ scrollNext: () => void;
+ canScrollPrev: boolean;
+ canScrollNext: boolean;
+} & CarouselProps;
+
+const CarouselContext = React.createContext(null);
+
+function useCarousel() {
+ const context = React.useContext(CarouselContext);
+
+ if (!context) {
+ throw new Error("useCarousel must be used within a ");
+ }
+
+ return context;
+}
+
+const Carousel = React.forwardRef & CarouselProps>(
+ ({ orientation = "horizontal", opts, setApi, plugins, className, children, ...props }, ref) => {
+ const [carouselRef, api] = useEmblaCarousel(
+ {
+ ...opts,
+ axis: orientation === "horizontal" ? "x" : "y",
+ },
+ plugins,
+ );
+ const [canScrollPrev, setCanScrollPrev] = React.useState(false);
+ const [canScrollNext, setCanScrollNext] = React.useState(false);
+
+ const onSelect = React.useCallback((api: CarouselApi) => {
+ if (!api) {
+ return;
+ }
+
+ setCanScrollPrev(api.canScrollPrev());
+ setCanScrollNext(api.canScrollNext());
+ }, []);
+
+ const scrollPrev = React.useCallback(() => {
+ api?.scrollPrev();
+ }, [api]);
+
+ const scrollNext = React.useCallback(() => {
+ api?.scrollNext();
+ }, [api]);
+
+ const handleKeyDown = React.useCallback(
+ (event: React.KeyboardEvent) => {
+ if (event.key === "ArrowLeft") {
+ event.preventDefault();
+ scrollPrev();
+ } else if (event.key === "ArrowRight") {
+ event.preventDefault();
+ scrollNext();
+ }
+ },
+ [scrollPrev, scrollNext],
+ );
+
+ React.useEffect(() => {
+ if (!api || !setApi) {
+ return;
+ }
+
+ setApi(api);
+ }, [api, setApi]);
+
+ React.useEffect(() => {
+ if (!api) {
+ return;
+ }
+
+ onSelect(api);
+ api.on("reInit", onSelect);
+ api.on("select", onSelect);
+
+ return () => {
+ api?.off("select", onSelect);
+ };
+ }, [api, onSelect]);
+
+ return (
+
+
+ {children}
+
+
+ );
+ },
+);
+Carousel.displayName = "Carousel";
+
+const CarouselContent = React.forwardRef>(
+ ({ className, ...props }, ref) => {
+ const { carouselRef, orientation } = useCarousel();
+
+ return (
+
+ );
+ },
+);
+CarouselContent.displayName = "CarouselContent";
+
+const CarouselItem = React.forwardRef>(
+ ({ className, ...props }, ref) => {
+ const { orientation } = useCarousel();
+
+ return (
+
+ );
+ },
+);
+CarouselItem.displayName = "CarouselItem";
+
+const CarouselPrevious = React.forwardRef>(
+ ({ className, variant = "outline", size = "icon", ...props }, ref) => {
+ const { orientation, scrollPrev, canScrollPrev } = useCarousel();
+
+ return (
+
+ );
+ },
+);
+CarouselPrevious.displayName = "CarouselPrevious";
+
+const CarouselNext = React.forwardRef>(
+ ({ className, variant = "outline", size = "icon", ...props }, ref) => {
+ const { orientation, scrollNext, canScrollNext } = useCarousel();
+
+ return (
+
+ );
+ },
+);
+CarouselNext.displayName = "CarouselNext";
+
+export { type CarouselApi, Carousel, CarouselContent, CarouselItem, CarouselPrevious, CarouselNext };
diff --git a/hire-shark/src/components/ui/chart.tsx b/hire-shark/src/components/ui/chart.tsx
new file mode 100644
index 0000000..08d40d9
--- /dev/null
+++ b/hire-shark/src/components/ui/chart.tsx
@@ -0,0 +1,303 @@
+import * as React from "react";
+import * as RechartsPrimitive from "recharts";
+
+import { cn } from "@/lib/utils";
+
+// Format: { THEME_NAME: CSS_SELECTOR }
+const THEMES = { light: "", dark: ".dark" } as const;
+
+export type ChartConfig = {
+ [k in string]: {
+ label?: React.ReactNode;
+ icon?: React.ComponentType;
+ } & ({ color?: string; theme?: never } | { color?: never; theme: Record });
+};
+
+type ChartContextProps = {
+ config: ChartConfig;
+};
+
+const ChartContext = React.createContext(null);
+
+function useChart() {
+ const context = React.useContext(ChartContext);
+
+ if (!context) {
+ throw new Error("useChart must be used within a ");
+ }
+
+ return context;
+}
+
+const ChartContainer = React.forwardRef<
+ HTMLDivElement,
+ React.ComponentProps<"div"> & {
+ config: ChartConfig;
+ children: React.ComponentProps["children"];
+ }
+>(({ id, className, children, config, ...props }, ref) => {
+ const uniqueId = React.useId();
+ const chartId = `chart-${id || uniqueId.replace(/:/g, "")}`;
+
+ return (
+
+
+
+ {children}
+
+
+ );
+});
+ChartContainer.displayName = "Chart";
+
+const ChartStyle = ({ id, config }: { id: string; config: ChartConfig }) => {
+ const colorConfig = Object.entries(config).filter(([_, config]) => config.theme || config.color);
+
+ if (!colorConfig.length) {
+ return null;
+ }
+
+ return (
+