Automated agent-driven development loop that executes AI agents against user story acceptance criteria.
Works with
Structures features as JSON-formatted user stories with testable acceptance criteria that agents can verify and track
Runs AI agents in a continuous loop to implement features, check acceptance criteria, and log progress for subsequent agent iterations
Requires prerequisite setup of AI coding agent configuration and user stories framework before running the Ralph agent loop
Inte
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionralph-loopExecute the skills CLI command in your project's root directory to begin installation:
Fetches ralph-loop from andrelandgraf/fullstackrecipes 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 ralph-loop. Access via /ralph-loop 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
2
total installs
2
this week
9
GitHub stars
0
upvotes
Run in your terminal
2
installs
2
this week
9
stars
Complete setup for automated agent-driven development. Define features as user stories with testable acceptance criteria, then run AI agents in a loop until all stories pass.
Complete these recipes first (in order):
Configure AI coding agents like Cursor, GitHub Copilot, or Claude Code with project-specific patterns, coding guidelines, and MCP servers for consistent AI-assisted development.
curl -H "Accept: text/markdown" https://fullstackrecipes.com/api/recipes/agent-setup
Create a structured format for documenting feature requirements as user stories. JSON files with testable acceptance criteria that AI agents can verify and track.
curl -H "Accept: text/markdown" https://fullstackrecipes.com/api/recipes/user-stories-setup
Document and track feature implementation with user stories. Workflow for authoring stories, building features, and marking acceptance criteria as passing.
curl -H "Accept: text/markdown" https://fullstackrecipes.com/api/recipes/using-user-stories
Set up automated agent-driven development with Ralph. Run AI agents in a loop to implement features from user stories, verify acceptance criteria, and log progress for the next agent.
curl -H "Accept: text/markdown" https://fullstackrecipes.com/api/recipes/ralph-setup
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
mattpocock/skills
I recommend ralph-loop for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Useful defaults in ralph-loop — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
We added ralph-loop from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Registry listing for ralph-loop matched our evaluation — installs cleanly and behaves as described in the markdown.
ralph-loop fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Useful defaults in ralph-loop — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
I recommend ralph-loop for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
ralph-loop is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
ralph-loop has been reliable in day-to-day use. Documentation quality is above average for community skills.
ralph-loop reduced setup friction for our internal harness; good balance of opinion and flexibility.
showing 1-10 of 39