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AutoWeave

Static Badge Static Badge License: MIT JavaScript Status

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AutoWeave is developed and maintained by Sloths Intel. It is a deterministic, browser-first CSV merge + ETL tool designed for real-world operational datasets (time logs, income records, project lists) that arrive asynchronously and inconsistently.

AutoWeave focuses on reproducibility, auditability, and low-friction workflows: validate → normalise → merge → deduplicate → summarise → visualise → export.

🌐 Website: https://autoweave.slothsintel.com


Contents


Features

Guided Merge (Deterministic ETL)

  • Upload time, income, and project CSV files
  • Validate schema before processing
  • Normalise dates, ids, and numeric fields
  • Deterministic join keys (no fuzzy matching)
  • Deduplicate merged output
  • Export a clean merged dataset

Quick Stats

  • Row count
  • Total time
  • Total income
  • Income/hour
  • Project-level summaries:
    • total time
    • total income
    • income/hour

Visual Overview

  • Stacked daily charts (date on x-axis)
    • Time by project
    • Income by project
    • Income/hour by project
  • Floating legend (AutoTrac-style)
  • Designed to be R-style readable: structured tables + consistent scales

Demo Mode (Auto-load)

  • Auto-loads sample datasets from:

    • assets/technology/time_sample.csv
    • assets/technology/income_sample.csv
    • assets/technology/project_sample.csv
  • User uploads override demo data.


Architecture

AutoWeave frontend:

├── AutoWeave
│   ├── index.html
│   ├── tech.html
│   ├── script.js
│   ├── autoweave.svg
│   └── assets
│       └── technology
│           ├── time_sample.csv
│           ├── income_sample.csv
│           └── project_sample.csv
└── README.md

Tech Stack

Frontend

  • HTML + CSS
  • Vanilla JavaScript
  • CSV parsing (client-side)
  • Browser rendering for tables + charts

Backend

  • Backend: FastAPI (validation + persistence)
  • Database: PostgreSQL (versioned datasets + lineage)

Data Contracts

AutoWeave expects three datasets: time, income, and projects (optional).

Time

Required columns:

  • project_id (string)
  • work_date (date; ISO preferred)

Recommended columns (used to compute/validate duration):

  • start_time (time)
  • end_time (time)
  • duration (number; hours or minutes depending on your export — AutoWeave normalises to hours)

Income

Required columns:

  • project_id (string)
  • work_date (date)
  • income (number)

Projects

Required columns:

  • project_id (string)
  • project (string; display name)

Merge & Validation Rules

Normalisation

  • Trim whitespace on all identifiers
  • Coerce numeric fields (income, duration)
  • Normalise work_date to a consistent date key
  • Treat missing values explicitly (no silent inference)

Invalid Time Rows

  • Drop time entries where both start_time and end_time are missing/unparseable and duration is missing/unparseable.

Join Strategy

Deterministic join key:

project_id + work_date

Income aligns to time entries by exact match on this key.

Deduplication

Merged output is deduplicated using a deterministic composite key (implementation-defined), typically:

project_id + work_date + duration + income

AutoWeave does not perform fuzzy matching.


Quick Stats & Visualisations

Quick Stats

Computed from the merged dataset:

  • Total rows
  • Total time
  • Total income
  • Income per hour
  • By-project summaries:
    • total time
    • total income
    • income per hour

Visualisations

Stacked daily charts:

  • Time by project
  • Income by project
  • Income/hour by project

All charts:

  • Use a consistent project color mapping
  • Include a floating legend
  • Default to most recent dates available in the merged dataset

Roadmap

  • export audit metadata
  • predictive hooks (AutoPred)

Contribution

Maintained by Sloths Intel GitHub, and Daddy Sloth GitHub.


License

© 2026 Sloths Intel.

A trading name of Sloths Intel Ltd Registered in England and Wales (Company No. 16907507).

MIT License.


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