<Use_When>
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionautopilotExecute the skills CLI command in your project's root directory to begin installation:
Fetches autopilot from yeachan-heo/oh-my-claudecode and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate autopilot. Access via /autopilot in your agent's command palette.
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.
Submit your Claude Code skill and start earning
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
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
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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<Use_When>
<Do_Not_Use_When>
plan skill insteadralph or delegate to an executor agentplan --review<Why_This_Exists> Most non-trivial software tasks require coordinated phases: understanding requirements, designing a solution, implementing in parallel, testing, and validating quality. Autopilot orchestrates all of these phases automatically so the user can describe what they want and receive working code without managing each step. </Why_This_Exists>
<Execution_Policy>
/oh-my-claudecode:cancel at any time; progress is preserved for resume
</Execution_Policy>Phase 1 - Planning: Create an implementation plan from the spec
.omc/plans/autopilot-impl.mdPhase 2 - Execution: Implement the plan using Ralph + Ultrawork
Phase 3 - QA: Cycle until all tests pass (UltraQA mode)
Phase 4 - Validation: Multi-perspective review in parallel
Phase 5 - Cleanup: Delete all state files on successful completion
.omc/state/autopilot-state.json, ralph-state.json, ultrawork-state.json, ultraqa-state.json/oh-my-claudecode:cancel for clean exit<Tool_Usage>
Task(subagent_type="oh-my-claudecode:architect", ...) for Phase 4 architecture validationTask(subagent_type="oh-my-claudecode:security-reviewer", ...) for Phase 4 security reviewTask(subagent_type="oh-my-claudecode:code-reviewer", ...) for Phase 4 quality review<Escalation_And_Stop_Conditions>
/deep-interview for Socratic clarification, or pause and ask the user for clarification before proceeding
</Escalation_And_Stop_Conditions><Final_Checklist>
Optional settings in .claude/settings.json:
{
"omc": {
"autopilot": {
"maxIterations": 10,
"maxQaCycles": 5,
"maxValidationRounds": 3,
"pauseAfterExpansion": false,
"pauseAfterPlanning": false,
"skipQa": false,
"skipValidation": false
}
}
}
If autopilot was cancelled or failed, run /oh-my-claudecode:autopilot again to resume from where it stopped.
Stuck in a phase? Check TODO list for blocked tasks, review .omc/autopilot-state.json, or cancel and resume.
QA cycles exhausted? The same error 3 times indicates a fundamental issue. Review the error pattern; manual intervention may be needed.
Validation keeps failing? Review the specific issues. Requirements may have been too vague -- cancel and provide more detail.
When autopilot is invoked with a vague input, Phase 0 can redirect to /deep-interview for Socratic clarification:
User: "autopilot build me something cool"
Autopilot: "Your request is open-ended. Would you like to run a deep interview first?"
[Yes, interview first (Recommended)] [No, expand directly]
If a deep-interview spec already exists at .omc/specs/deep-interview-*.md, autopilot uses it directly as Phase 0 output (the spec has already been mathematically validated for clarity).
The recommended full pipeline chains three quality gates:
/deep-interview "vague idea"
→ Socratic Q&A → spec (ambiguity ≤ 20%)
→ /ralplan --direct → consensus plan (Planner/Architect/Critic approved)
→ /autopilot → skips Phase 0+1, starts at Phase 2 (Execution)
When autopilot detects a ralplan consensus plan (.omc/plans/ralplan-*.md or .omc/plans/consensus-*.md), it skips both Phase 0 (Expansion) and Phase 1 (Planning) because the plan has already been:
Autopilot starts directly at Phase 2 (Execution via Ralph + Ultrawork).
Make data-driven prioritization decisions faster
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
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ 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.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
ailabs-393/ai-labs-claude-skills
Keeps context tight: autopilot is the kind of skill you can hand to a new teammate without a long onboarding doc.
Useful defaults in autopilot — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
I recommend autopilot for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
autopilot has been reliable in day-to-day use. Documentation quality is above average for community skills.
autopilot is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
autopilot fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Useful defaults in autopilot — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
I recommend autopilot for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
autopilot is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Keeps context tight: autopilot is the kind of skill you can hand to a new teammate without a long onboarding doc.
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