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Found 107 Skills
Run, monitor, resume, merge, and debug Ralph loops. Use this skill whenever the user asks to operate `ralph run` or `ralph loops`, inspect loop state, recover suspended loops, analyze diagnostics, or unblock merge queue issues.
Autonomous PRD implementation loop — turns GitHub issues into shipped code using TDD, code review gates, and Docker sandbox isolation. The execution engine for the grill-me → write-a-prd → prd-to-issues → ralph pipeline.
Use when performing ralph wiggum style long-running development loops with pacing control.
Self-improving review loop for Ralph Wiggum skills. Reviews skills against best practices, implements improvements, and continues until two consecutive clean reviews. Use when validating or improving the ralph-prompt-* skill suite.
Add enhancements to an existing project with Ralph Loop automation. Use when adding features to existing code, enhancing a codebase, or when the user says "ralph enhance", "add feature", or "enhance".
Super Ralph Wiggum - autonomous iteration loops with templates, PRD support, progress tracking, and browser testing. This skill should be used when running Claude Code in autonomous loops for test coverage improvement, PRD-based feature development, documentation generation, dataset creation, lint fixing, code cleanup, or framework migrations. Combines the plugin's in-session loop mechanism with specialized templates and best practices from Geoffrey Huntley, Ryan Carson, and AI Hero.
Analyze completed Ralph worktree branches, build a smart merge priority queue, and sequentially squash-merge them into main with worktree cleanup. Use this skill whenever the user wants to merge ralph worktrees, merge completed features, clean up finished ralph branches, process the ralph merge queue, or asks about which ralph branches are ready to merge. Triggers on: merge worktrees, ralph merge, merge completed branches, ralph cleanup, merge queue, which branches are done, squash ralph branches.
Ralph Wiggum-inspired automation loop for specification-driven development. Orchestrates task implementation, review, cleanup, and synchronization using a Python script. Use when: user runs /loop command, user asks to automate task implementation, user wants to iterate through spec tasks step-by-step, or user wants to run development workflow automation with context window management. One step per invocation. State machine: init → choose_task → implementation → review → fix → cleanup → sync → update_done. Supports --from-task and --to-task for task range filtering. State persisted in fix_plan.json.
Generate Ralph-compatible prompts for entire projects from scratch. Creates comprehensive prompts with architecture phase, implementation phases, testing, and documentation. Use when building complete applications, libraries, CLI tools, or any greenfield project requiring end-to-end development.
Create autonomous iterative loops (Ralph Wiggum pattern) for multi-step tasks. Use when setting up automated workflows that iterate over a backlog of tasks with clear acceptance criteria. Triggers on requests like "create a ralph loop", "set up an iterative agent", "automate this migration", or "create an autonomous loop".
Autonomous mobile dev subagent that implements a single user story from a PRD for Expo / React Native apps. Use when you need parallel, independent mobile implementation tasks — screens, native components, data fetching, navigation. Designed to run alongside other ralph-mobile instances. Receives a specific task ID and PRD path. Returns a structured completion signal. Does NOT commit or modify the PRD — those are handled by the documenter. Loads expo, building-native-ui, vercel-react-native-skills, native-data-fetching, and expo-dev-client skills automatically.
Use when testing Ralph's hat collection presets, validating preset configurations, or auditing the preset library for bugs and UX issues.