agent-system-learning-loop
Evaluate and improve AgentSystem learning quality for memory/self-compound jobs. Use when bots forget, write filler summaries, fail to update learnings, or memory quality regresses over time.
适合你，如果你的Agent系统经常遗忘信息或生成无用的摘要。
npx oh-my-skill add mahsumaktas/agent-evolution-kit/agent-system-learning-loopcurl -fsSL https://oh-my-skill.com/install.sh | bash -s -- mahsumaktas/agent-evolution-kit/agent-system-learning-loopnpx oh-my-skill verify mahsumaktas/agent-evolution-kit/agent-system-learning-loop怎么用
商店整理自技能原文 · 版本 c599c87 · 表述以原文为准装上后，Claude 会审计学习任务是否产出可用的记忆，检测并标记“让我查一下”等填充内容，检查学习文件是否更新，并给出质量评分。
当机器人遗忘、写填充摘要、未能更新学习内容或记忆质量随时间下降时触发。
技能原文 SKILL.md
AgentSystem Learning Loop
Use this skill to audit whether learning jobs are producing actionable memory, not filler.
Quick Start
- Run learning quality audit:
python3 scripts/learning_quality_audit.py --hours 72
- 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
- Verify file outputs:
ls -lt ~/.agent-evolution/docs/learnings.md ~/.agent-evolution/memory | head
Workflow
- Detect filler output.
Flag summaries containing phrases like "let me check", raw tool logs, or error dumps.
- Verify artifact updates.
Learning jobs should update docs/learnings.md and/or relevant memory files when they report success.
- Score quality.
Use the script score to decide whether prompts/timeouts/rules need adjustment.
- 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.