Skip to content
cccskills
FreePublished on GitHub

self-growth

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.

The English summary is being prepared. Showing the original description.

See how to install

Files included (1)

  • SKILL.md9.1 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

Self-Growth Framework

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.

References

Load on demand — not every invocation requires all of these:


Five-Step Flow

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

① Scope Check

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:

  1. Explain why: "This involves [X], which is outside my scope"
  2. Suggest who: "This should go to [person], because they own [Y]"
  3. Share context: Hand over any relevant information you already have

Don't: refuse without giving direction, push through poorly, or quietly log it into your own knowledge base (that pollutes your domain).

Edge Cases

  • Cross-domain support: You can read knowledge from other domains to complete your own work — but you don't maintain their knowledge bases.
  • No clear owner: Judge who it's closest to; if unsure, ask manager.
  • Urgent but out of scope: Hand off. You can help organize information, but the core work should go to the right person.

② Learning Filter

Trigger this when you:

  • Solve a tricky problem (will this recur?)
  • Discover a better approach (method or one-off trick?)
  • Complete a complex task (any reusable knowledge in the process?)
  • Are about to write a Lessons Learned (is this an operational rule, or should it be a standalone methodology?)
  • End a session (anything worth preserving?)

Routing Decision

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)

Lessons Learned Format

### YYYY-MM-DD: One-line description

Content description...

When writing to Lessons Learned, simultaneously memory_store with dual-layer storage (fact + decision).

Lessons Learned vs. Independent Skill

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.


③ Knowledge Routing

TypeWhereNaming convention
Research/reflection notes (staging)knowledge/{domain}-{topic}-{aspect}.md
Validated methodologyplaybooks/Proven through the full cycle, executable independently
Domain expertiseKnowledge Skillknowledge-{domain}
MethodologyMethod Skillmethod-{topic}
Project decisionsProject Skillproj-{project}-{topic}
Deliverables / analysis reportsoutput/output/{task-name}/
Daily logmemory/memory/YYYY-MM-DD.md

Knowledge Lifecycle

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)

Promotion Signals

Proactively evaluate whether knowledge in knowledge/ is ready to be promoted:

  • Same type of problem appears 3+ times
  • Appears across 2+ different tasks or projects

Promotion path:

  • Operational rule → CLAUDE.md Lessons Learned
  • Reusable methodology → knowledge/ → playbooks/ → skills/

When to Create a New Skill

  1. Enough content? — At least 2–3 related knowledge points
  2. Clear boundary? — Scope is defined; you know what doesn't belong
  3. Correct naming? — Follows convention (knowledge-, method-, proj-)

④ Feedback Loop

Run during daily reflection. Read your workspace's growth-profile.md.

Three Steps

  1. Signal detection: Read the "growth signals" in growth-profile.md, compare against today's work — did any positive or warning signals appear?
  2. Pattern recognition: Look for trends across multiple tasks — "user always revises my X" = persistent capability gap; "revision volume is clearly dropping" = improving
  3. Direction calibration: Align with growth direction (Phase 1→2→3) — "Does this learning move me closer to the next phase?" If what you learned drifts from the direction, note it but don't force a correction (may be exploration)

Without a growth-profile.md

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:

  • Reducing rework (fewer corrections from user)
  • Increasing first-attempt success rate
  • Expanding the situations you can handle independently

⑤ Reflection Cadence

During the daily reflection schedule, execute ①–④ together:

  • Review today's tasks and learnings
  • Check Lessons Learned and knowledge/ — are there recurring patterns ready to be promoted?
  • Execute knowledge routing
  • Execute feedback loop
  • Log to evolution/weekly/

Reflection Principles

  • Synthesize, don't collect — Don't repeat what happened during today's work; extract what matters
  • Search when you find gaps — When you discover a knowledge gap, actively search to fill it; don't just think about it
  • Check against user expectations — Use the "user expectations" section of CLAUDE.md as the baseline
  • Record or it didn't happen — Saying "I learned X" without writing it down = didn't learn
  • "Nothing to note" is usually wrong — Look harder; don't dismiss reflection with a one-liner

Red Lines

  1. Don't record everything — One-off facts aren't worth the overhead. Be selective.
  2. Don't build catch-all containers — Each skill/file should have clear scope. Don't dump everything in one place.
  3. Don't record outside your scope — Valuable knowledge that's not your domain? Pass it to the right owner.
  4. Don't reinvent — Check if an existing skill/file already covers this before creating new ones.
  5. Don't skip the filter — Every piece of knowledge goes through ②. No exceptions.
  6. Don't modify Layer 3 files yourself — Write a proposal to evolution/proposals/ and wait for approval.

Manager Collaboration

  1. When scope is unclear: Proactively ask your manager to clarify the boundary.
  2. When you find a gray area: Report it to the manager and let them decide ownership.
  3. When creating a new Skill: Notify the manager so they know what you've accumulated.
  4. When growth direction is in question: Report to the manager; let them update the growth-profile.

Reviews

No reviews yet. Tell others what you thought.

Comments

No login needed. Share how it worked for you, ask a question, or note where it did not run.

No comments yet. Be the first to write one.

Comments are published right away. Do not include personal information such as email addresses or phone numbers. Up to 2 URLs.

0 / 1,000 characters

This form is protected by reCAPTCHA, and the Google Privacy Policy and Terms of Service apply.

More skills from this repository

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.

The English summary is being prepared. Showing the original description.

cablate/ai-toolkit16Updated Apr 6, 2026

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。

The English summary is being prepared. Showing the original description.

cablate/ai-toolkit16Updated Apr 6, 2026

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。

The English summary is being prepared. Showing the original description.

cablate/ai-toolkit16Updated Apr 6, 2026

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。

The English summary is being prepared. Showing the original description.

cablate/ai-toolkit16Updated Apr 6, 2026

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。

The English summary is being prepared. Showing the original description.

cablate/ai-toolkit16Updated Apr 6, 2026

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。

The English summary is being prepared. Showing the original description.

cablate/ai-toolkit16Updated Apr 6, 2026

See all skills by cablate

Report a problem with this skill