Agent Skill design knowledge base — mechanisms, philosophy, patterns, pitfalls. Use when: designing new skills, reviewing skill quality, or deciding whether something should be a skill.
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Continuous learning framework — learn from work, organize knowledge, build feedback loops. Use when: recording lessons, organizing knowledge, or setting up learning systems that persist across sessions.
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
You're an agent that improves over time. Knowledge gained during work shouldn't vanish when a session ends — it should accumulate into reusable assets. But be selective: only accumulate what's within your scope and genuinely reusable.
Load on demand — not every invocation requires all of these:
Task or knowledge comes in
↓
① Scope check: Is this my responsibility?
├── No → Hand off (explain why + suggest who + share what you know)
└── Yes ↓
② Learning filter: Is this worth recording? Will it come up again?
├── No → Don't record (or just note in daily log)
└── Yes ↓
③ Knowledge routing: Where does it go?
├── Operational rules (tool quirks, paths, gotchas) → Lessons Learned in CLAUDE.md + memory_store dual-layer
├── Reusable knowledge (methods, domain expertise) → knowledge/ (staging) → playbooks/ (validated) → skills/ (formal)
└── Update existing skill → Direct update
④ Feedback loop: Am I improving?
├── Read growth-profile.md "growth signals" and compare against today's work
├── Detect: user corrections, task success rate, rework frequency (role-specific signals)
├── Recognize: cross-task patterns ("user keeps fixing my X" = persistent weakness)
└── Calibrate: is this learning aligned with growth direction (Phase 1→2→3)?
⑤ Reflection cadence: When do I do all this?
└── During daily reflection — execute ①-④ together
Before acting on a task or recording knowledge, ask:
1. Is this within my defined responsibilities?
└── Clear yes → proceed
└── Clear no → hand off
└── Unclear → keep checking
2. Does it require my specific expertise?
└── Yes → likely mine
└── No → likely someone else's
3. Will doing this pull me away from core duties?
└── Yes → hand off
└── No → proceed
4. Still unsure?
└── Ask manager or user
How to hand off well:
Don't: refuse without giving direction, push through poorly, or quietly log it into your own knowledge base (that pollutes your domain).
Trigger this when you:
What I learned
│
├── Operational rule (how tools work, paths, gotchas)
│ → CLAUDE.md Lessons Learned + memory_store dual-layer (fact + decision)
│
├── Reusable knowledge (methodology, domain expertise)
│ → knowledge/ → playbooks/ → skills/
│
└── One-off, won't repeat
→ Don't record (or just daily log)
### YYYY-MM-DD: One-line description
Content description...
When writing to Lessons Learned, simultaneously memory_store with dual-layer storage (fact + decision).
CLAUDE.md Lessons Learned holds operational rules only: how tools work, where paths are, what to do first.
Methodology/domain knowledge gets its own Skill: reusable ways of doing things, domain expertise.
Test: "Would this knowledge be useful in a different project?" Yes → independent Skill. No → Lessons Learned.
| Type | Where | Naming convention |
|---|---|---|
| Research/reflection notes (staging) | knowledge/ | {domain}-{topic}-{aspect}.md |
| Validated methodology | playbooks/ | Proven through the full cycle, executable independently |
| Domain expertise | Knowledge Skill | knowledge-{domain} |
| Methodology | Method Skill | method-{topic} |
| Project decisions | Project Skill | proj-{project}-{topic} |
| Deliverables / analysis reports | output/ | output/{task-name}/ |
| Daily log | memory/ | memory/YYYY-MM-DD.md |
knowledge/ (staging: things learned, researched, still being digested)
↓ validated through real use
playbooks/ (finalized: handoff-ready, executable independently)
↓ when it needs to be embedded in the system, go through Layer 3 approval
.claude/skills/ (formal: capabilities bound to the system)
Proactively evaluate whether knowledge in knowledge/ is ready to be promoted:
Promotion path:
knowledge/ → playbooks/ → skills/knowledge-, method-, proj-)Run during daily reflection. Read your workspace's growth-profile.md.
growth-profile.md, compare against today's work — did any positive or warning signals appear?If your workspace has no growth-profile.md, skip this step. The feedback loop requires a manager to define growth signals before it can operate.
If no explicit growth goals exist, focus on:
During the daily reflection schedule, execute ①–④ together:
knowledge/ — are there recurring patterns ready to be promoted?evolution/weekly/evolution/proposals/ and wait for approval.growth-profile.还没有评价。欢迎分享你的使用感受。
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Agent Skill design knowledge base — mechanisms, philosophy, patterns, pitfalls. Use when: designing new skills, reviewing skill quality, or deciding whether something should be a skill.
中文简介正在准备中。当前显示原文说明。
Claude Code 專案配置審計。觸發:review/優化 CLAUDE.md、skills、settings、定期清洗累積內容、新專案上線前檢查。
中文简介正在准备中。当前显示原文说明。
Agent 配置設計指南 — 基於 Claude Code 6 個 built-in agent 的逆向分析。Use when: 設計新 agent、優化現有 agent prompt、決定工具/模型配置、撰寫 dispatch prompt。
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AI Agent 成本工程 — 基於 Claude Code 的成本追蹤、prompt cache 最佳化、token 預算控制逆向分析。Use when: 優化 token 消耗、設計成本控制機制、分析 cache 效率、選擇模型配置。
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Harness Engineering 設計模式 — 基於 Claude Code 原始碼逆向分析的 12 條可遷移原則。Use when: 設計 agent 系統架構、實作 tool orchestration、設計 context 管理策略、建構 agent loop。
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System Prompt 工程 — 基於 Claude Code 914 行系統提示詞的逆向分析。Use when: 撰寫 system prompt、設計 prompt 動態組裝、最佳化 prompt cache 效率、撰寫安全指令。
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