Fetches canonical PR review state and renders derived state artifacts. Use when the user wants the state acquisition phase only (fetch, render, summarize) for a review-framework PR.
npx skills add https://github.com/prisma/prisma --skill review-fetch-phase
Run only the state acquisition phase of the review-framework loop:
fetch canonical review state JSON (v2), validate it, and render all derived artifacts via scripts.
Run commands from this skill directory. All script paths below are relative to it.
https://github.com/OWNER/REPO/pull/123)If output directory is omitted, derive:
wip/reviews/<owner>_<repo>_pr-<number>/
<output-dir>/review-state.json<output-dir>/review-state.md<output-dir>/summary.txt<output-dir>/review-targets.json<output-dir> exists.node ./scripts/guard-review-artifacts-ignored.mjs --dir <output-dir>
node ./scripts/fetch-review-state.mjs --pr <PR_URL> --out-json <output-dir>/review-state.json
node ./scripts/validate-review-state.mjs --in <output-dir>/review-state.json
node ./scripts/render-review-state.mjs --in <output-dir>/review-state.json --out <output-dir>/review-state.md
node ./scripts/summarize-review-state.mjs --in <output-dir>/review-state.json --format text --out <output-dir>/summary.txt
node ./scripts/extract-review-targets.mjs --in <output-dir>/review-state.json --out <output-dir>/review-targets.json
Target extraction includes:
review-state.json is canonical and must be schema version 2.review-state.md, summary.txt, review-targets.json) are regenerable from canonical JSON.gh api fails with TLS/cert errors in sandbox (x509 / OSStatus -26276), fail fast and instruct rerun outside sandbox.Return artifact paths:
review-state.jsonreview-state.mdsummary.txtreview-targets.jsonSuggest next step:
/review-triage-phase <PR_URL> [output-dir]Use when receiving code review feedback, before implementing suggestions, especially if feedback seems unclear or technically questionable - requires technical rigor and verification, not performative agreement or blind implementation
Use when completing tasks, implementing major features, or before merging to verify work meets requirements
Execute git commit with conventional commit message analysis, intelligent staging, and message generation. Use when user asks to commit changes, create a git commit, or mentions "/commit". Supports: (1) Auto-detecting type and scope from changes, (2) Generating conventional commit messages from diff, (3) Interactive commit with optional type/scope/description overrides, (4) Intelligent file staging for logical grouping
Comprehensive GitHub code review with AI-powered swarm coordination
Behavioral guidelines to reduce common LLM coding mistakes. Use when writing, reviewing, or refactoring code to avoid overcomplication, make surgical changes, surface assumptions, and define verifiable success criteria.
Use this skill to review code. It supports both local changes (staged or working tree) and remote Pull Requests (by ID or URL). It focuses on correctness, maintainability, and adherence to project standards.
Refactor bloated AGENTS.md, CLAUDE.md, or similar agent instruction files to follow progressive disclosure principles. Splits monolithic files into organized, linked documentation.
Create high-quality git commits: review/stage intended changes, split into logical commits, and write clear commit messages (including Conventional Commits). Use when the user asks to commit, craft a commit message, stage changes, or split work into multiple commits.
Take prisma/review-fetch-phase from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.