Audit

Audit Skill

Pre-flight

1. Check .claude/autoimprove/config.md exists. If not, stop: "Run /autoimprove:setup first."
2. Check git is available and working tree is clean.

Step 1: Run Measurement Suite

Run the measure skill to get the composite score and per-metric breakdown. Capture both the scores AND the raw command output for each metric.

For each metric defined in the config:

• Run the command (e.g., tsc --noEmit 2>&1, pnpm test 2>&1, pnpm lint 2>&1)
• Record the score (using the measure skill's scoring logic)
• Also capture the raw output for deficiency counting

Step 2: Count Individual Deficiencies

Parse the raw output from each command to count specific issues:

• Type errors: Count lines matching error patterns (e.g., error TS for TypeScript, error: for Rust). Group by file.
• Build: Pass/fail only — no granular count. If build fails, it becomes top priority.
• Tests: Count passing vs total from test runner output. Identify failing test names if any.
• Lint: Count warning/error lines from linter output. Group by rule if possible.

Step 3: Calculate Efficiency

For each metric with a gap (score < max):

1. Point gap = max_weight - current_score
2. Estimated iterations = ceil(issue_count / issues_per_iteration) using these heuristics:
   • Type errors: ~3 per iteration (clustered in files)
   • Lint warnings: ~2.5 per iteration
   • Tests (new): ~1 per iteration
   • Build fix: ~1-2 iterations
   • These are initial estimates — actual results will vary
3. Efficiency = point_gap / estimated_iterations
4. Estimated tokens = estimated_iterations × 22000 (rough average per iteration)

Read weights dynamically from .claude/autoimprove/config.md. If a metric is not applicable, skip it and redistribute weight as the measure skill already handles.

Sort areas by efficiency (highest pts/iteration first).

Step 4: Display Report

Print the audit report:

━━━ Codebase Audit ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
📊 Current Score: XX/100

  [Metric 1]:  XX/WW  [progress bar]  (gap pts to max) or ✓ maxed
  [Metric 2]:  XX/WW  [progress bar]  (gap pts to max) or ✓ maxed
  [Metric 3]:  XX/WW  [progress bar]  (gap pts to max) or ✓ maxed
  [Metric 4]:  XX/WW  [progress bar]  (gap pts to max) or ✓ maxed

━━━ Fastest Path to 100% ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

  #  Area          Gap    Issues  Est. iterations  Efficiency
  1  [best area]   Xpts   N items  M iterations    X.X pts/iter ← best
  2  [next area]   Xpts   N items  M iterations    X.X pts/iter
  ...

  Total: ~N iterations to reach 100/100
  ⚡ Estimated token usage: ~XXXK tokens (rough estimate, actual usage varies)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

The progress bar uses filled blocks (█) and empty blocks (░), scaled to 10 characters per metric.

If score is 100/100: Still print the full report (progress bars, efficiency table with all zeroes, and token estimate of ~0K tokens) so the output format is always consistent. After the report, print "Nothing to improve." and stop — do not prompt for the interactive fix loop.

If build fails: Show build as #1 priority with a note: "⚠️ Build is failing — other metrics may be unreliable. Fix the build first."

If only one area has a gap: Skip the selection prompt. Just ask: "Fix [area]? (~N iterations, ~XXXK tokens) (Y/n)"

Step 5: Interactive Fix Loop

Check for interrupted session first: If .claude/autoimprove/log.md exists and the last session has IN_PROGRESS status, show: "↩️ You have an interrupted session. Use /autoimprove:continue to resume, or continue with this audit for a fresh start."

Prompt the user:

Would you like to start fixing? (Y/n)

If no: Stop. User can run /autoimprove:improve manually.

If yes:

Which area? (Enter number, or press Enter for #1 — [area name], ~N iterations)
>

If the user picks a number, use that area's estimate. If they press Enter, use the most efficient area.

Starting the improve loop

Count all **Branch:** autoimprove/experiment- entries in .claude/autoimprove/log.md (if it exists) to determine the experiment offset.

Invoke the improve-loop skill directly with:

• iterations: The estimated count for the selected area
• focus: Generated focus string for that area:
  • Type safety → "Fix type errors"
  • Build → "Fix build errors"
  • Tests → "Add unit tests for untested functions"
  • Lint → "Fix lint warnings"
• session_mode: new
• baseline_score: The score just measured
• start_iteration: 1
• total_iterations: Same as iterations
• experiment_offset: Count of existing experiments

After area completes

Re-run the measurement suite (Step 1-3) to get updated scores. Then show:

━━━ Area Complete: [area name] ━━━━━━━━━━━━━━━━━━━━━━━━━━━━
📊 Score: XX → YY/100 (+delta)
  [area]:  WW/WW  ██████████  ✓ maxed

Remaining work:
  #  Area     Gap    Est. iterations  Efficiency
  1  [next]   Xpts   M iterations     X.X pts/iter
  ...

  Remaining: ~N iterations (~XXXK tokens)

Continue to next area? (Y/n)

If yes: Repeat the selection prompt (or auto-select if only one area left). Start a new improve-loop session for that area.

If no: Stop. Print final score.

If all areas maxed: "🎉 Codebase score: 100/100. All areas maxed!"

If area doesn't fully improve

If the improve loop finishes its estimated iterations but the area isn't maxed yet, show actual results:

Area [name] partially improved: XX → YY (expected ZZ)
Run more iterations on this area? (Y/n)

If yes, run more iterations (re-estimate based on remaining gap). If no, move to the next area.