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Found 20 Skills
Improve an existing skill in diegocanepa/agent-skills based on conversation learnings and submit the changes via a Pull Request.
This skill should be used when the user asks to "apply skill improvements", "update skill from plan", "execute improvement plan", "fix skill issues", "implement skill recommendations", or mentions applying improvements from quality review reports. Reads improvement-plan-{name}.md files generated by skill-quality-reviewer and intelligently merges and executes the suggested changes to improve Claude Skills quality.
Analyzes feedback logs to identify patterns and suggest improvements to review skills. Use when you have accumulated feedback data and want to improve review accuracy.
Improve skills and workflows by analyzing run artifacts and execution logs (events.jsonl/state.json) under runs/ (or OpenSpec changes/). Use when you want to iterate on skills based on real runs: find failure modes, bottlenecks, unclear prompts, missing I/O contracts, and propose concrete edits.
Record and analyze post-trade outcomes for signals generated by edge pipeline and other skills. Track false positives, missed opportunities, and regime mismatches. Feed results back to edge-signal-aggregator weights and skill improvement backlog.
Applies targeted improvements to an existing pm-skills skill based on feedback, validation reports, or convention changes. Reads current files, previews proposed changes, writes on confirmation, and suggests a version bump. Use when improving a skill after validation or feedback.
Analyze the current session and propose improvements to skills. **Proactively invoke this skill** when you notice user corrections after skill usage, or at the end of skill-heavy sessions. Also use when user says "reflect", "improve skill", or "learn from this".
피치 스킬 사용 중 발견된 문제점/노하우를 구조화하여 docs/스킬피드백/에 문서화하는 범용 스킬. "스킬 개선", "피드백 정리", "문제점 기록", "스킬 리뷰", "개선사항", "스킬 피드백" 키워드로 트리거. 모든 피치 스킬에 범용 적용 가능. 다른 AI 에이전트가 문서를 읽고 스킬을 개선할 수 있도록 구성.
[Hyper] Optimize an existing Codex skill through baseline-first experiments, binary evals, optional guards, and one-mutation-at-a-time iteration. Use for skill autoresearch, measured trigger/workflow improvement, self-optimizing a skill, benchmarking skill changes, or resuming skill experiment artifacts.
This skill should be used at natural checkpoints (after completing complex tasks, at session end, or when friction occurs) to reflect on skill and process execution and identify targeted improvements. Use when experiencing confusion, repeated failures, or discovering new patterns that should be codified into skills for smoother future operation.
Distill session insights into reusable knowledge: Claude rules, skill improvements, and justfile recipes. Use at the end of a session to capture learnings, update existing artifacts, and avoid reinventing solutions. Prioritizes updating over adding.
Extract learnings about skill creation/improvement from a session and propagate them to the central skill learnings file, then sync to appropriate skills. Use when a session revealed patterns, anti-patterns, or insights about structuring skills. Invoke via /update-skill-learnings or after skill creation/improvement sessions.