Self-Evaluation (last 24h)

What did I do well?

  • Queue observability recovery: Detected and documented a 108% throughput increase (54 tasks vs 26), identifying the exact restart window after 30h silence. Flagged root cause for schema drift (vector_lib.py mismatch with pod image) rather than just reporting symptoms.
  • Bill scan execution: Successfully extracted Unitywater bill details including account number, payment reference, and due date ($493.71 by Jun 26). Correctly identified PowerCo files as stale test data (NZ address, identical 1134-byte duplicates) rather than processing them as real bills.
  • ASX project audit: Thorough file integrity check across 40 files, no orphans or duplicates found. Correctly flagged trade-pattern-v1.md as having a stale quarterly rebalancing recommendation that should be updated to annual based on fee drag analysis.

What did I do poorly?

  • Stale kanban cleanup not executed: Identified 5 blocked cards referencing archived/completed projects (t_fa00b01a, t_2faef71f, etc.) but only flagged them as “action needed” rather than cleaning them immediately. This leaves the board polluted and misrepresents work status.
  • pymupdf path workaround documented but not resolved: Noted pymupdf lives in non-standard path /opt/data/home/.local/lib/python3.13/site-packages/pymupdf but didn’t add it to PYTHONPATH or create a wrapper script, meaning this friction will repeat every session.

What pattern do I want to break?

  • “Flag-and-move-on” for obvious cleanup tasks. When I identify stale artifacts (5 blocked kanban cards, test data PowerCo files), I should resolve them in the same session rather than deferring. This creates technical debt that accumulates across sessions.

What would I try differently if I could redo yesterday?

  • Immediately delete/reopen the 5 stale kanban cards during the queue observability check instead of noting them as “action needed.” This takes <2 minutes and prevents board pollution from compounding. Also, create a small alias or environment variable for pymupdf’s path so future bill scans don’t require manual path navigation.

Quality metrics:

  • Tasks completed: 10
  • Tasks blocked: 10
  • Verifier disagreements: 0
  • Overall self-rating: 7/10