pr-retro▌
boshu2/agentops · updated Apr 8, 2026
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Learn from PR outcomes by analyzing accept/reject patterns.
PR Retro Skill
Learn from PR outcomes by analyzing accept/reject patterns.
Overview
After a PR is merged or rejected, analyze what worked and what didn't to improve future contributions.
Output: .agents/learnings/YYYY-MM-DD-pr-{repo}-{outcome}.md
When to Use:
- After a PR is merged (capture success patterns)
- After a PR is rejected (understand why)
- After receiving significant review feedback
- Periodically to review contribution patterns
Workflow
1. PR Discovery -> Find the PR to analyze
2. Outcome Analysis -> Merged/rejected/changes requested
3. Feedback Extraction -> What did reviewers say?
4. Pattern Identification -> What worked/didn't
5. Lesson Extraction -> Reusable learnings
6. Output -> Write retro document
Phase 1: PR Discovery
# If PR number provided
gh pr view <number> --json state,reviews,comments,mergedAt,closedAt
# Find recent PRs by you
gh pr list --state all --author @me --limit 10
# Find PRs to a specific repo
gh pr list -R <owner/repo> --state all --author @me --limit 10
Phase 2: Outcome Analysis
| Outcome | Meaning | Focus |
|---|---|---|
| Merged | Success | What worked? |
| Closed (not merged) | Rejected | Why? |
| Open (stale) | Ignored/abandoned | What went wrong? |
| Changes requested | Needs work | What feedback? |
# Get PR outcome
gh pr view <number> --json state,mergedAt,closedAt,reviews
Phase 3: Feedback Extraction
# Get all review comments
gh pr view <number> --json reviews --jq '.reviews[] | "\(.author.login): \(.body)"'
# Get all comments
gh api repos/<owner>/<repo>/pulls/<number>/comments --jq '.[].body'
# Get requested changes
gh pr view <number> --json reviews --jq '.reviews[] | select(.state == "CHANGES_REQUESTED")'
Feedback Categories
| Category | Examples |
|---|---|
| Style | Naming, formatting, conventions |
| Technical | Algorithm, architecture, patterns |
| Scope | Too big, scope creep, unrelated changes |
| Testing | Missing tests, coverage, edge cases |
| Documentation | Missing docs, unclear comments |
| Process | Wrong branch, missing sign-off |
Phase 4: Pattern Identification
Success Patterns (If Merged)
| What Worked | Evidence |
|---|---|
| Small, focused PR | < 5 files |
| Followed conventions | No style comments |
| Good tests | No "add tests" requests |
| Clear description | Quick approval |
Failure Patterns (If Rejected)
| What Failed | Evidence |
|---|---|
| Too large | "Please split this PR" |
| Scope creep | "This is out of scope" |
| Missing tests | "Please add tests" |
| Wrong approach | "Consider using X instead" |
Phase 5: Lesson Extraction
Lesson Template
## Lesson: [Title]
**Context**: [When does this apply?]
**Learning**: [What did we learn?]
**Action**: [What to do differently?]
**Evidence**:
- PR #N: [quote or summary]
Common Lessons
| Lesson | Action |
|---|---|
| PR too large | Split PRs under 200 lines |
| Missing context | Add "## Context" section |
| Style mismatch | Run linter before PR |
| Missing tests | Add tests for new code |
| Slow review | Ping after 1 week |
Phase 6: Output
Write to .agents/learnings/YYYY-MM-DD-pr-{repo}-{outcome}.md
# PR Retro: {repo} #{number}
**Date**: YYYY-MM-DD
**PR**: {url}
**Outcome**: Merged / Rejected / Stale
## Summary
{What was the PR about? What happened?}
## Timeline
| Date | Event |
|------|-------|
| {date} | PR opened |
| {date} | First review |
| {date} | {outcome} |
## Feedback Analysis
### Positive Feedback
- {quote}
### Requested Changes
- {quote}
### Rejection Reasons (if applicable)
- {quote}
## Lessons Learned
### Lesson 1: {title}
**Context**: {when this applies}
**Learning**: {what we learned}
**Action**: {what to do differently}
## Updates to Process
{Any changes to make to pr-prep, pr-plan, or other skills}
## Next Steps
{Future actions based on this retro}
Anti-Patterns
| DON'T | DO INSTEAD |
|---|---|
| Skip retros on merged PRs | Learn from success too |
| Blame maintainers | Focus on what YOU can change |
| Generic lessons | Specific, actionable learnings |
| Skip rejected PRs | Most valuable learning source |
Examples
Learn From Rejected PR
User says: "Run a retro on why this PR was rejected."
What happens:
- Analyze reviewer feedback and timeline.
- Identify preventable process and scope issues.
- Capture reusable lessons for future PRs.
Learn From Successful Merge
User says: "Extract what worked from this merged PR."
What happens:
- Identify patterns that sped review/approval.
- Distill actionable playbook updates.
- Save lessons for future contribution flows.
Troubleshooting
| Problem | Cause | Solution |
|---|---|---|
| Retro is generic | Feedback not tied to evidence | Cite specific comments/decisions and outcomes |
| No clear lesson extracted | Analysis stayed descriptive | Convert observations into behavior changes |
| Maintainer signal is mixed | Contradictory review comments | Separate hard blockers from preference feedback |
| Process changes not adopted | Lessons not operationalized | Add explicit updates to prep/plan/validate workflow |
How to use pr-retro 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 pr-retro
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches pr-retro from GitHub repository boshu2/agentops 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 pr-retro. Access the skill through slash commands (e.g., /pr-retro) 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★★★★★61 reviews- ★★★★★Dhruvi Jain· Dec 24, 2024
pr-retro is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Carlos Harris· Dec 24, 2024
pr-retro reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Pratham Ware· Dec 20, 2024
I recommend pr-retro for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Henry Abebe· Dec 20, 2024
I recommend pr-retro for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Aarav Patel· Dec 12, 2024
pr-retro has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Charlotte Nasser· Dec 8, 2024
Registry listing for pr-retro matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Aanya Harris· Dec 4, 2024
pr-retro fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★William Shah· Dec 4, 2024
Keeps context tight: pr-retro is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Amelia Desai· Nov 27, 2024
pr-retro fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Hana Sethi· Nov 23, 2024
Registry listing for pr-retro matched our evaluation — installs cleanly and behaves as described in the markdown.
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