Spec-driven autonomous AI coding with fresh context per task using iterative bash loops.
Works with
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:
Fetches 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:
Restart Cursor to activate ralph-wiggum. Access via /ralph-wiggum 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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Autonomous AI coding with spec-driven development
Ralph Wiggum combines Geoffrey Huntley's iterative bash loop with spec-driven development for fully autonomous AI-assisted software development.
The key insight: Fresh context each iteration. Each loop starts a new agent process with a clean context window, preventing context overflow and degradation.
Use Ralph Wiggum when:
┌─────────────────────────────────────────────────────────────┐
│ RALPH LOOP │
├─────────────────────────────────────────────────────────────┤
│ Loop 1: Pick spec A → Implement → Test → Commit → DONE │
│ Loop 2: Pick spec B → Implement → Test → Commit → DONE │
│ Loop 3: Pick spec C → Implement → Test → Commit → DONE │
│ ... │
│ │
│ Each iteration = Fresh context window │
│ Shared state = Files on disk (specs, plan, history) │
└─────────────────────────────────────────────────────────────┘
# Using Vercel's add-skill
npx add-skill fstandhartinger/ralph-wiggum
# Using OpenSkills
openskills install fstandhartinger/ralph-wiggum
For full Ralph Wiggum setup with constitution and interview:
# Tell your AI agent:
"Set up Ralph Wiggum using https://github.com/fstandhartinger/ralph-wiggum"
The agent will guide you through a lightweight, pleasant setup:
For existing projects, the agent detects your tech stack automatically. The interview prioritizes understanding what you're building and why.
Each iteration of the Ralph loop starts a new AI agent process. This means:
State persists between loops via files:
specs/ — Feature specifications with acceptance criteriaralph_history.txt — Log of breakthroughs, blockers, learningsIMPLEMENTATION_PLAN.md — Optional detailed task breakdownThe agent outputs <promise>DONE</promise> ONLY when:
The bash loop checks for this phrase. If not found, it retries.
Tests, lints, and builds act as guardrails. The agent must fix issues before outputting the completion signal.
The key to success: Each spec needs clear, testable acceptance criteria. This is what tells Ralph when a task is truly "done."
# Feature: User Authentication
## Requirements
- OAuth login with Google
- Session management
- Logout functionality
## Acceptance Criteria
- [ ] User can log in with Google
- [ ] Session persists across page reloads
- [ ] User can log out
- [ ] Tests pass
**Output when complete:** `<promise>DONE</promise>`
Good criteria: "User can log in with Google and session persists" Bad criteria: "Auth works correctly"
The more specific your acceptance criteria, the better Ralph performs.
# Start building (Claude Code)
./scripts/ralph-loop.sh
# With max iterations
./scripts/ralph-loop.sh 20
# Using Codex CLI
./scripts/ralph-loop-codex.sh
Every loop run writes all output to log files in logs/:
logs/ralph_*_session_YYYYMMDD_HHMMSS.log (entire run, including CLI output)logs/ralph_*_iter_N_YYYYMMDD_HHMMSS.log (per-iteration CLI output)logs/ralph_codex_output_iter_N_*.txt| Mode | Purpose | Command |
|---|---|---|
| build (default) | Pick spec, implement, test, commit | ./scripts/ralph-loop.sh |
| plan (optional) | Create detailed task breakdown | ./scripts/ralph-loop.sh plan |
Trust the AI to self-identify, self-correct, and self-improve. Observe patterns and adjust prompts.
For Ralph to work effectively, enable full autonomy:
--dangerously-skip-permissions--dangerously-bypass-approvals-and-sandbox⚠️ Use at your own risk. Only in sandboxed environments.
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.
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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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