learning-loop

name	learning-loop
description	Sensor-driven continuous improvement loop. Collects metrics from all sensors, detects regressions, creates issues, verifies past fixes, and self-tunes thresholds. Invoke with /learning-loop.
user-invocable	true

Learning Loop

Closed-loop improvement system: collect sensor data → detect regressions → create issues → verify fixes → learn from results.

Workflow

Step 1: Collect Sensor Data

Run the unified sensor report to gather metrics from all available sensors:

node scripts/sensor-report.mjs

Read the output. The script queries every sensor registered in scripts/sensors-registry.mjs (the list-of-record — check there for the current count and coverage) and persists the report to metrics/sensor-report.json. It also detects regressions by comparing against the previous report.

If the script exits with code 1, regressions were detected. Note them for Step 3.

Step 1b: Sentry Triage

If the Sentry MCP is available, run production error triage:

Invoke /sentry-triage to query Sentry for new/regressed production errors and create GitHub issues for any that pass the severity/frequency/deduplication filters.

This step is optional — if Sentry is not authenticated or unavailable, skip with a note in the summary.

Step 2: Verify Past Fixes

Run fix verification on recently-closed issues:

node scripts/verify-fixes.mjs

This finds issues closed in the last 48 hours with sensor labels (ci-fix, audit, acmm, sentry, bug), queries the originating sensor, and:

• Comments on the issue with verification evidence
• Reopens issues where the fix didn't improve the metric

Note any reopened issues for the summary.

Step 2b: Collect AI Issue Feedback

Run the feedback collector to update per-category acceptance rates:

node scripts/collect-ai-issue-feedback.mjs

This queries closed AI-created issues, classifies each as accepted/rejected/wontfix, and writes per-category rates + budgets to metrics/ai-issue-feedback.json. The budgets are used in Step 3 to cap issue creation for categories with high rejection rates (>40% rejected = budget halved).

Step 3: Triage Regressions

Read the sensor report from metrics/sensor-report.json and the issue feedback budgets from metrics/ai-issue-feedback.json. For each regression in the regressions array:

1. Check category budget — read budgets.<category> from the feedback file. If the budget for that regression's label category is 0, skip. Default budget is 3 if no feedback data exists.

2. Check for duplicates — search open issues for the same sensor + metric combination:

   gh issue list --state open --search "<sensor> <metric>" --json number,title --limit 5
   

3. Skip if duplicate exists — don't create noise.

4. Create issue if novel — use this format:

   gh issue create \
     --title "fix(<sensor>): <metric> regressed (<delta>)" \
     --label "ready,<sensor-label>,bug" \
     --body "## Regression Detected\n\n**Sensor:** <sensor>\n**Metric:** <metric>\n**Current:** <value>\n**Previous:** <value>\n**Delta:** <delta>\n**Severity:** <high|medium>\n\n## Acceptance Criteria\n\n- [ ] <metric> returns to previous level or better\n- [ ] Verified by next learning loop run\n\n_Detected by [learning-loop](../.claude/skills/learning-loop/SKILL.md) sensor report_"
   

5. Max issues per run — use the per-category budget from metrics/ai-issue-feedback.json (default 3). Prioritize high severity over medium. Never exceed the budget for any single category.

Step 4: Analyze Session Logs (Weekly)

Check if today is the configured skill-extraction day (default: Friday). If so:

1. Read session logs from .claude/session-logs/*.json (last 7 days)
2. Group sessions by branch prefix, files touched, and git diff patterns
3. Identify repeated workflows (3+ sessions with similar patterns):
   • Same file set modified across sessions
   • Same branch prefix pattern (e.g., fix/security-*)
   • High commit count with specific file patterns
4. Check existing skills in .claude/skills/*/SKILL.md for coverage
5. If a novel pattern is found, create a skill-proposal issue:

   gh issue create \
     --title "[skill-proposal] <Descriptive Name>" \
     --label "skill-proposal,ready" \
     --body "<draft skill content based on observed pattern>"
   

6. Max 2 proposals per run

Step 5: Threshold Self-Tuning

Read the verification log at .claude/improvement-loop/verifications.jsonl (last 30 days):

1. Compute false positive rate: issues closed as wontfix or invalid / total issues created by learning loop
2. Compute fix effectiveness rate: verified fixes / total verifications
3. If false positive rate > 30%, note in the summary that thresholds should be loosened
4. If fix effectiveness rate < 50%, note that fix strategies may need review

Step 6: Log and Summarize

Append a dated entry to .claude/improvement-loop/log.md:

## YYYY-MM-DD

**Sensors:** N/M available
**Regressions:** N detected, M issues created
**Verifications:** N checked, M verified, K failed (reopened)
**Skill proposals:** N (if Friday)
**Threshold notes:** <any self-tuning observations>

Print a summary to stdout.

Sensor Label Map

Sensor	Issue Label	What It Checks
CI Health	ci-fix	Pass rate on main branch
ACMM	acmm	Maturity criteria met
Lighthouse	audit	Performance/a11y scores
Sentry	sentry	Error rates (needs MCP auth)
General	bug	CI pass after fix

Scheduling

This skill is designed to run daily. Add a RemoteTrigger:

Trigger	Schedule	Notes
mbe-learning-loop	Daily 11:00am PT	After ACMM audit (10am)

Or invoke manually: /learning-loop

Guardrails

• Max issues per category governed by metrics/ai-issue-feedback.json budgets (default 3, halved when rejection rate >40%)
• Max 2 skill proposals per run
• Deduplication: always check open issues before creating
• Reopened issues get ready label for implement-queue pickup
• Session log analysis only on Fridays (configurable)
• Verification runs on 48h-old closures (configurable via --hours)
• AI issue feedback collection runs every invocation (Step 2b) to keep budgets current