Agent System Learning Loop

Use this skill to audit whether learning jobs are producing actionable memory, not filler.

Quick Start

1. Run learning quality audit:

python3 scripts/learning_quality_audit.py --hours 72

2. Inspect latest learning job runs:

agent-system cron runs --job 046bb2db-42ad-4b79-96d4-f9aa1392ede8 --limit 5
agent-system cron runs --job 079a9ab8-d706-465c-801d-741f4a2d55a3 --limit 5

3. Verify file outputs:

ls -lt ~/.agent-evolution/docs/learnings.md ~/.agent-evolution/memory | head

Workflow

1. Detect filler output. Flag summaries containing phrases like "let me check", raw tool logs, or error dumps.

2. Verify artifact updates. Learning jobs should update docs/learnings.md and/or relevant memory files when they report success.

3. Score quality. Use the script score to decide whether prompts/timeouts/rules need adjustment.

4. Apply remediation. Tighten prompts for concrete outputs, increase timeout where needed, and re-run the same jobs for verification.

Resources

• scripts/learning_quality_audit.py: Scores recent learning runs and flags regressions.
• references/quality-criteria.md: Quality gates for learning/memory cron jobs.