
Creates meaningful git commits by analyzing changes and committing in logical units. Use when the user wants to commit changes to git, requests commit creation, or asks to save changes to version control. Supports --english and --japanese for commit language selection and --branch to create a new branch before committing.
Processes AI reviewer feedback and applies only verified fixes. Works in two modes: (1) fetches comments from a PR URL or current branch, (2) processes feedback pasted directly into the conversation. Trigger when the user wants to bulk-process or apply AI review suggestions — from a GitHub PR or pasted text. Do NOT trigger for single questions about what a bot said, or general code review discussion.
Creates meaningful git commits by analyzing changes and committing in logical units. Use when the user wants to commit changes to git, requests commit creation, or asks to save changes to version control. Supports --english and --japanese for commit language selection and --branch to create a new branch before committing.
Use when the user invokes /workflow. Injects project workflow methodology as context. Accepts argument: plan, exec, review (default: all).
Executes one user-selected task from an approved plan bundle using TDD, or rechecks DoD independently (mode=implement|dod-recheck). Use when the user specifies a task ID to implement, e.g. "execute Task 3" or "implement task 5", after decompose-plan review PASS. Also use with dod-recheck mode when the user says "recheck task DoD", "verify task completion", or "dod-recheck".
Audits product completion after task-level PASS artifacts exist. Use when the plan or Ralph story metadata marks a completion gate for a public CLI/API/runtime/release claim; otherwise it is optional.
Adversarial verification of code changes — probes target files for vulnerabilities through edge cases, error paths, security boundaries, and concurrency attacks. Use when you want to stress-test implementation correctness or validate defensive robustness before shipping.
Decomposes an approved design document into a TDD-based implementation plan or reviews an existing plan (mode=create|review). Create mode: task breakdown with traceability. Review mode: independent sub-agent verification absorbing the former analyze-plan audit. Use after design-doc approval when the user needs to break down a design into executable tasks, create an implementation plan, or generate a task list from a design.
Prepares .ralph/ runtime state from an approved and reviewed plan bundle. Syncs plan tasks into prd.json and updates prompt.run.md with project-specific context and quality gates. Use after plan approval, decompose-plan review PASS, and ralph init.
Review text for AI writing tropes and mechanical prose patterns, then report findings with concrete rewrite suggestions. Use when the user asks to proofread, lint, or polish writing — especially docs, blog posts, READMEs, commit messages, PR descriptions, or any prose that should read as human-written. Also use when the user says "check for AI slop", "does this sound like AI", "make this sound more natural", or wants to de-AI their text.
Audits and refines a CLAUDE.md or AGENTS.md file for instruction density, staleness, and effectiveness. Use when reviewing or improving an agent instruction file, after significant project changes (skills, architecture, or tooling), when agent behavior suggests instructions are ignored or misinterpreted, when the file feels bloated or repetitive, or when the user says "review my AGENTS.md" or "audit agent instructions".
Creates or refreshes a HANDOVER.md that captures current session state for the next assistant. Use when wrapping up a session, context is getting full, switching operators, or when the user asks for a handoff summary including decisions, pitfalls, lessons learned, next steps, and important files. Also trigger when the user says "save progress", "session summary", or "prepare for next session".
Reviews committed changes and creates a pull request on GitHub. Use when the user wants to create a PR, push changes for review, or open a pull request. Requires a GitHub repository. Supports --japanese flag for Japanese descriptions, --base flag to specify target branch, and --update flag to update an existing PR.
Audits and refines a CLAUDE.md or AGENTS.md file for instruction density, staleness, and effectiveness. Use when reviewing or improving an agent instruction file, after significant project changes (skills, architecture, or tooling), when agent behavior suggests instructions are ignored or misinterpreted, when the file feels bloated or repetitive, or when the user says "review my AGENTS.md" or "audit agent instructions".
Creates or reviews a design document (mode=create|review). Create mode: iterative dialogue producing design docs and ADRs. Review mode: independent sub-agent verification of an approved design doc. Use this skill whenever a feature, API, architecture, or data model change would benefit from written design before coding — even if the user doesn't explicitly say "design doc". When in doubt, invoke this skill first.
Adversarial verification of code changes — probes target files for vulnerabilities through edge cases, error paths, security boundaries, and concurrency attacks. Use when you want to stress-test implementation correctness or validate defensive robustness before shipping.
Reviews committed changes and creates a pull request on GitHub. Use when the user wants to create a PR, push changes for review, or open a pull request. Requires a GitHub repository. Supports --japanese flag for Japanese descriptions, --base flag to specify target branch, and --update flag to update an existing PR.
Review text for AI writing tropes and mechanical prose patterns, then report findings with concrete rewrite suggestions. Use when the user asks to proofread, lint, or polish writing — especially docs, blog posts, READMEs, commit messages, PR descriptions, or any prose that should read as human-written. Also use when the user says "check for AI slop", "does this sound like AI", "make this sound more natural", or wants to de-AI their text.
Simplifies recently changed code by running three parallel reviews (reuse, quality, efficiency) and applying only behavior-preserving fixes. Use when the user asks to simplify, clean up, reduce duplication, improve code reuse, or optimize recently changed code, a staged diff, a branch diff, or explicitly listed files. Also use when the user says things like 'simplify this', 'まとめて整理して', 'コードをスリムにして', or invokes `/simplify`.
Processes AI reviewer feedback and applies only verified fixes. Works in two modes: (1) fetches comments from a PR URL or current branch, (2) processes feedback pasted directly into the conversation. Trigger when the user wants to bulk-process or apply AI review suggestions — from a GitHub PR or pasted text. Do NOT trigger for single questions about what a bot said, or general code review discussion.