skill-refinement
Skill25 starsChanged 8 days agoMITView on GitHub
Skill 사용 후 개선 제안. ledger 분석으로 어느 skill 이 자주 실패 / open_question 다발 / 비효율인지 식별. Chief 가 retrospective 후 호출.
1 other project uses a version of this.
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Most recently taken up by
- krema/agent-skillstheir own version, first seen Oct 13, 2026
Other versions1 project uses a version of this.
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Wrote their own version · 1
- krema/agent-skills★ 0same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository32 other files it gives its agents.
- AGENTS.mdAGENTS.md
- CLAUDE.mdCLAUDE.md
- build-testSkill
- trend-trackerSkill
- typecheckSkill
- agent-managerSkill
- cron-managerSkill
- design-systemSkill
- discovery-synthesisSkill
- docsSkill
- experiment-designSkill
- goal-managerSkill
- hypothesis-designSkill
- interview-scriptSkill
- jobs-storiesSkill
- lean-canvasSkill
- market-researchSkill
- meceSkill
- okrSkill
- opportunity-treeSkill
- policySkill
- prdSkill
- premortemSkill
- primitive-reviewSkill
- prioritizationSkill
- retrospectiveSkill
- skill-managerSkill
- triageSkill
- wbsSkill
- workflow-managerSkill
- workflow-refinementSkill
- xyz-hypothesisSkill
The file
Files
Skill Refinement Skill
Chief 자가학습 루프의 일부. 어떤 skill 이 잘 작동하고, 어떤 skill 이 개선 필요한지 평가.
입력
- 평가 대상 period
<org>/memory/ledger/<task-id>.jsonl— skill 호출 trace<org>/memory/open-questions/<id>.json— 미해결/blocking 비율<org>/memory/agent-costs.jsonl— skill 별 cost
평가 차원
per_skill_metrics:
skill_name: <name>
invocation_count: <N>
avg_duration_ms: <N>
avg_cost_usd: <N>
hard_gate_failure_rate: <%> # gate 통과 못 한 비율
open_question_rate: <%> # 호출당 발생한 open_question 수
rework_rate: <%> # 같은 task 에서 재호출된 비율
confidence_delta_avg: <+/-> # 호출 전후 confidence 변화Failure Patterns
patterns:
- skill: problem-definition
issue: "TDCC P4 단계 unknown 필드 too frequent (62%)"
root_cause_hypothesis: "archive context 너무 짧음 (avg N=4 entries)"
fix_candidates:
- "기본 archive window 확대 (4→8 entries)"
- "TDCC P4 reasoning step skip threshold 도입"출력
period: "..."
top_3_high_friction_skills: [...]
top_3_efficient_skills: [...]
refinement_proposals:
- skill: "..."
proposal: "..."
expected_impact: "..."
effort: small | medium | large
approval_required: founder | chief_autoAnti-Sycophancy
- ❌ "skill 들이 잘 작동합니다"
- ✅ "problem-definition 의 P4 단계가 62% 빈도로 unknown 필드 발생. archive context 확대로 개선 가능 (예상 -30%)."
Reference
- v1.1 PRD §5.2 RETROSPECT + §1.1 #7 (자가학습 메커니즘)
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