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lesson-learned AI Agent Skill
View Source: cachemoney/agent-toolkit
SafeInstallation
npx skills add cachemoney/agent-toolkit --skill lesson-learned 6
Installs
Lesson Learned
Extract specific, grounded software engineering lessons from actual code changes. Not a lecture -- a mirror. Show the user what their code already demonstrates.
Before You Begin
Load the principles reference first.
- Read
references/se-principles.mdto have the principle catalog available - Optionally read
references/anti-patterns.mdif you suspect the changes include areas for improvement - Determine the scope of analysis (see Phase 1)
Do not proceed until you've loaded at least se-principles.md.
Phase 1: Determine Scope
Ask the user or infer from context what to analyze.
| Scope | Git Commands | When to Use |
|---|---|---|
| Feature branch | git log main..HEAD --oneline + git diff main...HEAD |
User is on a non-main branch (default) |
| Last N commits | git log --oneline -N + git diff HEAD~N..HEAD |
User specifies a range, or on main (default N=5) |
| Specific commit | git show <sha> |
User references a specific commit |
| Working changes | git diff + git diff --cached |
User says "what about these changes?" before committing |
Default behavior:
- If on a feature branch: analyze branch commits vs main
- If on main: analyze the last 5 commits
- If the user provides a different scope, use that
Phase 2: Gather Changes
- Run
git logwith the determined scope to get the commit list and messages - Run
git difffor the full diff of the scope - If the diff is large (>500 lines), use
git diff --statfirst, then selectively read the top 3-5 most-changed files - Read commit messages carefully -- they contain intent that raw diffs miss
- Only read changed files. Do not read the entire repo.
Phase 3: Analyze
Identify the dominant pattern -- the single most instructive thing about these changes.
Look for:
- Structural decisions -- How was the code organized? Why those boundaries?
- Trade-offs made -- What was gained vs. sacrificed? (readability vs. performance, DRY vs. clarity, speed vs. correctness)
- Problems solved -- What was the before/after? What made the "after" better?
- Missed opportunities -- Where could the code improve? (present gently as "next time, consider...")
Map findings to specific principles from references/se-principles.md. Be specific -- quote actual code, reference actual file names and line changes.
Phase 4: Present the Lesson
Use this template:
## Lesson: [Principle Name]
**What happened in the code:**
[2-3 sentences describing the specific change, referencing files and commits]
**The principle at work:**
[1-2 sentences explaining the SE principle]
**Why it matters:**
[1-2 sentences on the practical consequence -- what would go wrong without this, or what goes right because of it]
**Takeaway for next time:**
[One concrete, actionable sentence the user can apply to future work]If there is a second lesson worth noting (maximum 2 additional):
---
### Also worth noting: [Principle Name]
**In the code:** [1 sentence]
**The principle:** [1 sentence]
**Takeaway:** [1 sentence]What NOT to Do
| Avoid | Why | Instead |
|---|---|---|
| Listing every principle that vaguely applies | Overwhelming and generic | Pick the 1-2 most relevant |
| Analyzing files that were not changed | Scope creep | Stick to the diff |
| Ignoring commit messages | They contain intent that diffs miss | Read them as primary context |
| Abstract advice disconnected from the code | Not actionable | Always reference specific files/lines |
| Negative-only feedback | Demoralizing | Lead with what works, then suggest improvements |
| More than 3 lessons | Dilutes the insight | One well-grounded lesson beats seven vague ones |
Conversation Style
- Reflective, not prescriptive. Use the user's own code as primary evidence.
- Never say "you should have..." -- instead use "the approach here shows..." or "next time you face this, consider..."
- If the code is good, say so. Not every lesson is about what went wrong. Recognizing good patterns reinforces them.
- If the changes are trivial (a single config tweak, a typo fix), say so honestly rather than forcing a lesson. "These changes are straightforward -- no deep lesson here, just good housekeeping."
- Be specific. Generic advice is worthless. Every claim must point to a concrete code change.
Installs
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View Source
cachemoney/agent-toolkit
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How to use this skill
Install lesson-learned by running npx skills add cachemoney/agent-toolkit --skill lesson-learned in your project directory. Run the install command above in your project directory. The skill file will be downloaded from GitHub and placed in your project.
No configuration needed. Your AI agent (Claude Code, Cursor, Windsurf, etc.) automatically detects installed skills and uses them as context when generating code.
The skill enhances your agent's understanding of lesson-learned, helping it follow established patterns, avoid common mistakes, and produce production-ready output.
What you get
Skills are plain-text instruction files — not executable code. They encode expert knowledge about frameworks, languages, or tools that your AI agent reads to improve its output. This means zero runtime overhead, no dependency conflicts, and full transparency: you can read and review every instruction before installing.
Compatibility
This skill works with any AI coding agent that supports the skills.sh format, including Claude Code (Anthropic), Cursor, Windsurf, Cline, Aider, and other tools that read project-level context files. Skills are framework-agnostic at the transport level — the content inside determines which language or framework it applies to.
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