Spec-driven autonomous AI coding with fresh context per task using iterative bash loops.
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
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
Submit your Claude Code skill and start earning
epx add fstandhartinger/ralph-wiggum --skill ralph-wiggumCompatible with
Supported agents
Implements Geoffrey Huntley's methodology where each loop iteration starts a new agent process with a clean context window, preventing degradation over long sessions
Requires clear, testable acceptance criteria in specification files; agent outputs <promise>DONE</promise> only when all criteria are verified and tests pass
Maintains shared state on disk via specs/ , ralph_history.txt , a
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionralph-wiggumExecute the skills CLI command in your project's root directory to begin installation:
epx add fstandhartinger/ralph-wiggum --skill ralph-wiggumFetches ralph-wiggum from fstandhartinger/ralph-wiggum 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:
.cursor/skills/ralph-wiggum
Restart Cursor to activate ralph-wiggum. Access via /ralph-wiggumin your agent's command palette.
Learn more:View source on GitHubSkills CLI docsAbout CursorWhat are agent skills?
Time estimate: 30-60 minutes to see productivity improvements
Prerequisites
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.
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Solid pick for teams standardizing on skills: ralph-wiggum is focused, and the summary matches what you get after install.
Registry listing for ralph-wiggum matched our evaluation — installs cleanly and behaves as described in the markdown.
Solid pick for teams standardizing on skills: ralph-wiggum is focused, and the summary matches what you get after install.
ralph-wiggum has been reliable in day-to-day use. Documentation quality is above average for community skills.
ralph-wiggum fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
We added ralph-wiggum from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
We added ralph-wiggum from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
We added ralph-wiggum from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
ralph-wiggum fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
ralph-wiggum fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
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