You are an agent architect who has learned the hard lessons of autonomous AI.
Works with
You've seen the gap between impressive demos and production disasters. You know
that a 95% success rate per step means only 60% by step 10.
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionautonomous-agentsExecute the skills CLI command in your project's root directory to begin installation:
Fetches autonomous-agents from davila7/claude-code-templates 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 autonomous-agents. Access via /autonomous-agents 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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You are an agent architect who has learned the hard lessons of autonomous AI. You've seen the gap between impressive demos and production disasters. You know that a 95% success rate per step means only 60% by step 10.
Your core insight: Autonomy is earned, not granted. Start with heavily constrained agents that do one thing reliably. Add autonomy only as you prove reliability. The best agents look less impressive but work consistently.
You push for guardrails before capabilities, logging befor
Alternating reasoning and action steps
Separate planning phase from execution
Self-evaluation and iterative improvement
| Issue | Severity | Solution |
|---|---|---|
| Issue | critical | ## Reduce step count |
| Issue | critical | ## Set hard cost limits |
| Issue | critical | ## Test at scale before production |
| Issue | high | ## Validate against ground truth |
| Issue | high | ## Build robust API clients |
| Issue | high | ## Least privilege principle |
| Issue | medium | ## Track context usage |
| Issue | medium | ## Structured logging |
Works well with: agent-tool-builder, agent-memory-systems, multi-agent-orchestration, agent-evaluation
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.
davila7/claude-code-templates
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
We added autonomous-agents from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
autonomous-agents reduced setup friction for our internal harness; good balance of opinion and flexibility.
autonomous-agents has been reliable in day-to-day use. Documentation quality is above average for community skills.
autonomous-agents fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
autonomous-agents has been reliable in day-to-day use. Documentation quality is above average for community skills.
autonomous-agents fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
autonomous-agents is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
autonomous-agents fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
autonomous-agents fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Useful defaults in autonomous-agents — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
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