measuring-ai-proficiency: Skill for Claude Code

Pre-Flight Check

Surfaces relevant accumulated knowledge at the start of a session. This is the bridge that connects the outer loop back into the inner loop — it makes prior learnings visible before the agent starts work.

Without this, accumulated .learnings/ are invisible to new sessions. The agent repeats mistakes that were already captured because nobody told it to look.

When It Runs

• Automatically via SessionStart hook (lightweight scan, ~100-200 tokens)
• Manually before major tasks (deep scan with area filtering)

Hook Output (Automatic — Lightweight)

The SessionStart hook (scripts/pre-flight.sh) does a fast scan and outputs a brief reminder if there are relevant signals:

<pre-flight-check>
Active learnings: N entries in .learnings/
Recent errors (last 7 days): N
Promotion-ready patterns: N
Failed evals: N

High-priority items:
- [Pattern-Key]: [one-line summary] (seen N times)
- [Pattern-Key]: [one-line summary] (seen N times)

Consider running /learning-aggregator if promotion-ready count > 0.
</pre-flight-check>

If there are no signals (empty .learnings/, no failed evals), the hook outputs nothing — zero overhead.

Manual Deep Scan

When invoked explicitly, the pre-flight check does a deeper analysis:

Step 1: Scan .learnings/

Read .learnings/LEARNINGS.md, .learnings/ERRORS.md, .learnings/FEATURE_REQUESTS.md.

For each entry, extract:

• Pattern-Key, Summary, Priority, Status, Area, Related Files, Recurrence-Count, Last-Seen

Step 2: Scan .evals/ (if exists)

Read .evals/EVAL_INDEX.md for any failed or stale evals.

Step 3: Check Context-Surfing Handoffs

Look for unread files in .context-surfing/ (same as handoff-checker.sh but integrated).

Step 4: Relevance Filter

If the user described the task area, filter learnings to:

• Entries whose Area matches the task
• Entries whose Related Files overlap with likely-touched files
• Entries with Priority: high/critical regardless of area
• Entries with Status: promotion_ready (need attention)