resolve-pr-parallel
Resolve all unresolved PR review comments by spawning parallel agents for each thread.
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Installation Guide
How to use resolve-pr-parallel 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 machine
- ›Node.js 16+ with npm — verify with
node --version - ›Active project directory where you want to add
resolve-pr-parallel
Run the install command
Execute the skills CLI command in your project's root directory to begin installation:
Fetches resolve-pr-parallel from everyinc/compound-engineering-plugin and configures it for Cursor.
Select Cursor when prompted
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate resolve-pr-parallel. Access via /resolve-pr-parallel in your agent's command palette.
Security 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 environment. Always review source, verify the publisher, and test in isolation before production.
Documentation
Resolve PR Comments in Parallel
Resolve all unresolved PR review comments by spawning parallel agents for each thread.
Context Detection
Detect git context from the current working directory:
- Current branch and associated PR
- All PR comments and review threads
- Works with any PR by specifying the number
Workflow
1. Analyze
Fetch unresolved review threads using the GraphQL script at scripts/get-pr-comments:
bash scripts/get-pr-comments PR_NUMBER
This returns only unresolved, non-outdated threads with file paths, line numbers, and comment bodies.
If the script fails, fall back to:
gh pr view PR_NUMBER --json reviews,comments
gh api repos/{owner}/{repo}/pulls/PR_NUMBER/comments
2. Plan
Create a task list of all unresolved items grouped by type (e.g., TaskCreate in Claude Code, update_plan in Codex):
- Code changes requested
- Questions to answer
- Style/convention fixes
- Test additions needed
3. Implement (PARALLEL)
Spawn a compound-engineering:workflow:pr-comment-resolver agent for each unresolved item.
If there are 3 comments, spawn 3 agents — one per comment. Prefer running all agents in parallel; if the platform does not support parallel dispatch, run them sequentially.
Keep parent-context pressure bounded:
- If there are 1-4 unresolved items, direct parallel returns are fine
- If there are 5+ unresolved items, launch in batches of at most 4 agents at a time
- Require each resolver agent to return a short status summary to the parent: comment/thread handled, files changed, tests run or skipped, any blocker that still needs human attention, and for question-only threads the substantive reply text so the parent can post or verify it
If the PR is large enough that even batched short returns are likely to get noisy, use a per-run scratch directory such as .context/compound-engineering/resolve-pr-parallel/<run-id>/:
- Have each resolver write a compact artifact for its thread there
- Return only a completion summary to the parent
- Re-read only the artifacts that are needed to resolve threads, answer reviewer questions, or summarize the batch
4. Commit & Resolve
- Commit changes with a clear message referencing the PR feedback
- Resolve each thread programmatically using scripts/resolve-pr-thread:
bash scripts/resolve-pr-thread THREAD_ID
- Push to remote
5. Verify
Re-fetch comments to confirm all threads are resolved:
bash scripts/get-pr-comments PR_NUMBER
Should return an empty array []. If threads remain, repeat from step 1.
If a scratch directory was used and the user did not ask to inspect it, clean it up after verification succeeds.
Scripts
- scripts/get-pr-comments - GraphQL query for unresolved review threads
- scripts/resolve-pr-thread - GraphQL mutation to resolve a thread by ID
Success Criteria
- All unresolved review threads addressed
- Changes committed and pushed
- Threads resolved via GraphQL (marked as resolved on GitHub)
- Empty result from get-pr-comments on verify
List & Monetize Your Skill
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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
Steps
- 1Install product management skill
- 2Start with user story generation for known feature
- 3Progress to competitive analysis: research 2-3 competitors
- 4Use for roadmap prioritization: apply RICE/ICE scoring
- 5Draft stakeholder communications and refine based on feedback
- 6Build template library for recurring PM tasks
- 7Share 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
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Reviews
- DDhruvi Jain★★★★★Dec 24, 2024
resolve-pr-parallel reduced setup friction for our internal harness; good balance of opinion and flexibility.
- AAmelia Gill★★★★★Dec 20, 2024
Keeps context tight: resolve-pr-parallel is the kind of skill you can hand to a new teammate without a long onboarding doc.
- AAmina Jackson★★★★★Dec 12, 2024
Solid pick for teams standardizing on skills: resolve-pr-parallel is focused, and the summary matches what you get after install.
- OOshnikdeep★★★★★Nov 15, 2024
I recommend resolve-pr-parallel for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- YYusuf Ghosh★★★★★Nov 11, 2024
resolve-pr-parallel has been reliable in day-to-day use. Documentation quality is above average for community skills.
- GGanesh Mohane★★★★★Oct 6, 2024
Useful defaults in resolve-pr-parallel — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- OOmar Torres★★★★★Oct 2, 2024
Solid pick for teams standardizing on skills: resolve-pr-parallel is focused, and the summary matches what you get after install.
- SSakshi Patil★★★★★Sep 25, 2024
resolve-pr-parallel is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- SSakura Farah★★★★★Sep 13, 2024
Keeps context tight: resolve-pr-parallel is the kind of skill you can hand to a new teammate without a long onboarding doc.
- OOlivia Taylor★★★★★Sep 13, 2024
resolve-pr-parallel fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
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