lesson-learned▌
softaworks/agent-toolkit · updated Apr 8, 2026
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Extract software engineering lessons from recent git changes by analyzing actual code patterns.
- ›Analyzes code diffs against a curated principles reference to identify dominant patterns, trade-offs, and structural decisions
- ›Supports multiple scopes: feature branches vs. main, specific commits, commit ranges, or uncommitted working changes
- ›Maps findings to concrete SE principles with specific file and line references, avoiding generic advice
- ›Presents lessons in a reflective format t
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.
How to use lesson-learned on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add lesson-learned
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches lesson-learned from GitHub repository softaworks/agent-toolkit and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate lesson-learned. Access the skill through slash commands (e.g., /lesson-learned) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
User Story & Requirements Generation
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Competitive Analysis
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ Use When
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid When
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.5★★★★★45 reviews- ★★★★★Noah Srinivasan· Dec 28, 2024
Useful defaults in lesson-learned — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Noah Jackson· Dec 16, 2024
lesson-learned is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Hana Perez· Dec 12, 2024
Registry listing for lesson-learned matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Yash Thakker· Nov 23, 2024
Useful defaults in lesson-learned — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Noah Shah· Nov 7, 2024
Keeps context tight: lesson-learned is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Michael Desai· Oct 26, 2024
I recommend lesson-learned for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Dhruvi Jain· Oct 14, 2024
Registry listing for lesson-learned matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Oshnikdeep· Sep 17, 2024
Keeps context tight: lesson-learned is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Harper Menon· Sep 17, 2024
lesson-learned is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Hana Gonzalez· Sep 9, 2024
I recommend lesson-learned for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
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