feat(recommender): add skill progression graph with gap scoring

[parinaB]

Summary [required]

Added a skill progression graph to the recommendation engine. Instead of only matching what the user already knows, the engine now computes reachable next skills using BFS on a directed graph and boosts projects that target those gap skills giving returning users a clear sense of what to learn next.

Related Issue [required]

Closes #390

Type of Change [required]

• Bug fix — resolves a broken behaviour
• Feature — adds new functionality
• Data — adds new projects to data/projects.json
• Documentation — updates docs, README, or code comments only
• Style — CSS or visual changes only, no logic change
• Refactor — restructures code without changing behaviour
• Test — adds or updates tests

What Was Changed [required]

• Added data/skill_graph.json — directed skill dependency graph covering all skills in projects.json plus Java extensions (spring, sql)
• Added _load_skill_graph() — loads graph from disk with soft failure
• Added _hops_to_skill() — BFS to find minimum hops from known skills to a target skill
• Added gap_boost() — boosts project score by 1/hops for each reachable missing skill
• Added get_progression() — returns up to 3 projects that are 1 hop away from user's current skills, not already recommended
• Updated get_recommendations() — now returns progression key alongside recommendations and related
• Added VALID_INTERESTS constant that was missing from validation
• Fixed duplicate project ID 10 in projects.json
• Updated test_recommender.py — added skill graph progression test section

How to Test This PR [required]

1. Clone this branch: git checkout your-branch-name
2. Install dependencies: pip install -r requirements.txt
3. Run the recommender tests:: python test_recommender.py

Expected test output:

Input validation
----------------
  PASS  empty skills caught
  PASS  empty level caught
  PASS  valid inputs pass through cleanly

Return shape
------------
  PASS  get_recommendations returns a dict
  PASS  dict has 'recommendations' key
  PASS  dict has 'related' key

Recommendations
---------------
  PASS  recommendations is a list  (3 result(s))
  PASS  respects MAX_RESULTS cap  (got 3)
  PASS  all results have required fields
  PASS  High time availability returns >= results than Low
  PASS  no-match input returns empty recommendations

Skill alias normalisation
-------------------------
  PASS  'js' alias resolves to 'javascript'

Skill graph progression
-----------------------
  PASS  dict has 'progression' key
  PASS  progression is a list  (1 result(s))
  PASS  progression projects don't repeat recommended ones
        → API ETL Pipeline  (gap_score: 2.0)

Done.

Test Results [required]

Self-Review Checklist [required]

• I have read CONTRIBUTING.md and followed all guidelines
• My branch name follows the convention: feat/, fix/, docs/, data/, style/, test/
• I have run python tests/test_basic.py and all 27 tests pass
• I have run flake8 . locally and there are no errors
• I have not introduced any print() or console.log() debug statements
• Every new function I wrote has a docstring
• I have not modified files outside the scope of the linked issue
• If I changed the UI, I tested it at 375px (mobile) and 1280px (desktop)
• If I added a project to the dataset, it has all required JSON fields

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