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Github Review Iteration

OrganizationPopular
prisma
github-review-iteration

Orchestrates a GitHub PR review loop by delegating triage and implementation to dedicated sub-agents, then repeating until actionable review items are cleared. Use when the user says “address PR review”, “triage review comments”, or “iterate until review is clean”.

Overview

Publisherprisma
Repositoryorm
Skill namegithub-review-iteration
Stars
47.6K
Forks
2.5K
Bundled files
3
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by prisma on GitHub. Read the source before you install it.

Installation

Install the Github Review Iteration AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/prisma/orm.git /tmp/orm
mkdir -p .claude/skills
cp -r /tmp/orm/skills-contrib/github-review-iteration .claude/skills/github-review-iteration
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Github Review Iteration in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Github Review Iteration on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Github Review Iteration is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

GitHub Review Iteration

Run an iterative PR review loop: fetch state → render/summarize → triage actions → implement (code + Done + resolve) → re-fetch until the PR has no remaining actionable items.

This skill is an orchestrator. It delegates:

  • triage to ../review-triage-phase/agents/review-triager.md
  • implementation to ../review-implement-phase/agents/review-implementer.md

The orchestrator owns sequencing, handoff, and loop control. It does not perform triage or implementation directly when delegation is available.

Locating sibling skills and scripts

This skill depends on three sibling skills that live in the same parent directory:

  • ../review-fetch-phase/ — fetches and renders PR review state
  • ../review-triage-phase/ — triages review threads into an action plan
  • ../review-implement-phase/ — implements triaged actions and resolves threads

All script paths in this document are relative to this skill's directory. Use ../ to reach sibling skills. Do not search the workspace/repo for these files — they are part of the skills installation, not the project being reviewed.

Run the scripts from the repository root, addressing them by their full path under the skills installation. The reviews root defaults to wip/reviews resolved against the working directory, so a run started elsewhere writes artifacts outside the repo-root wip/ tree that .gitignore covers. Pass --reviews-root <repo-root>/wip/reviews if you must run from another directory.

To run a path from the repository root, prefix it with this skill's installed location — .agents/skills/github-review-iteration/. So ../review-fetch-phase/scripts/fetch-review-state.mjs becomes .agents/skills/review-fetch-phase/scripts/fetch-review-state.mjs.

Usage

This skill supports subcommands:

  • triage: fetch + triage into structured actions
  • implement: execute the triaged actions and update status
  • iterate: loop triageimplement until clear (default)
text
/github-review-iteration iterate <PR_URL> [output-dir]

When output-dir is omitted, use the standard layout: wip/reviews/<owner>_<repo>_pr-<number>/ (derived from the PR URL, with owner and repo lowercased — derive a directory name by hand the same way, or the artifacts split across two directories).

Example:

text
/github-review-iteration iterate https://github.com/OWNER/REPO/pull/123

Files written (deterministic layout)

Store artifacts under:

wip/reviews/<owner>_<repo>_pr-<number>/

Canonical artifacts:

  • review-state.json (canonical v2)
  • review-actions.json (canonical v2)

Derived artifacts:

  • review-state.md
  • summary.txt (or JSON summary)
  • review-actions.md
  • apply-log.json (optional)

When you need a thin wrapper for path setup + standard script calls, run:

bash
node .agents/skills/github-review-iteration/scripts/review-iterate.mjs --pr <PR_URL>

For phase-specific execution without full orchestration, use:

  • /review-fetch-phase <PR_URL> [output-dir]
  • /review-triage-phase <PR_URL> [output-dir]
  • /review-implement-phase <PR_URL> [output-dir]

Behavioral rules

  • WILL ADDRESS items:
    • reply + 👍
    • leave unresolved until fixed
  • Not addressed in this PR items:
    • reply with rationale + 👎
    • resolve the thread
  • Implementation:
    • granular, intent-driven commits
    • explicit staging only (never git add -A / git add .)
    • reply “Done” and resolve when complete

Operational reliability notes (Cursor)

gh api TLS / cert failures in sandboxed shells

If GitHub administration fails with an error like:

  • x509: OSStatus -26276 (or similar TLS/certificate verification failures)

Treat it as an environment/sandbox cert-store mismatch, not a script bug.

Recovery:

  • Re-run the affected gh calls outside the sandbox (use a shell mode that uses the system cert store).
  • Do not disable TLS verification (no GH_NO_VERIFY_SSL, no custom curl flags).
  • After re-running, continue the loop normally (fetch → triage → implement → resolve → repeat).

JSON-first deterministic commands

All script paths below are relative to this skill's directory.

  1. Fetch canonical JSON:
bash
node ../review-fetch-phase/scripts/fetch-review-state.mjs --pr <PR_URL> --out-json <review-dir>/review-state.json
  1. Render and summarize from JSON (pure scripts):
bash
node ../review-fetch-phase/scripts/render-review-state.mjs --in <review-dir>/review-state.json --out <review-dir>/review-state.md
node ../review-fetch-phase/scripts/summarize-review-state.mjs --in <review-dir>/review-state.json --format text --out <review-dir>/summary.txt
  1. Render triage plan from canonical actions JSON:
bash
node ../review-triage-phase/scripts/render-review-actions.mjs --in <review-dir>/review-actions.json --out <review-dir>/review-actions.md

Data exchange format (triager → implementer)

review-actions.json is the contract between the triager and implementer.

Minimum v2 shape:

json
{
  "version": 2,
  "pr": { "url": "https://github.com/OWNER/REPO/pull/123", "nodeId": "PR_kw..." },
  "reviewState": { "path": "review-state.json", "fetchedAt": "...", "version": 2 },
  "actions": [
    {
      "actionId": "A-001",
      "target": { "kind": "review_thread", "nodeId": "PRRT_xxx", "url": "..." },
      "decision": "will_address",
      "summary": "One-line description of what will be done",
      "rationale": null,
      "targetFiles": ["path/to/file.ts"],
      "acceptance": "How to tell it's done",
      "status": "pending",
      "done": null
    }
  ]
}

Rules:

  • Use node ids only for targets (target.nodeId).
  • Preserve actions[] order intentionally (do not reorder).
  • Implementer updates status (pending|in_progress|done) and done records in place.
  • Compound targets: A single pull_request_review body may contain multiple findings (especially from automated reviewers like CodeRabbit which bundle "outside diff range" comments into the review body). The triager must decompose these into sub-actions (e.g., A02a, A02b). Never blanket-dismiss review bodies without reading their content.

Procedure

triage

  1. Delegate to triage sub-agent

Invoke the review triager agent at ../review-triage-phase/agents/review-triager.md and pass:

  • PR URL
  • output paths:
    • <output-dir>/review-state.md
    • <output-dir>/review-state.json
    • <output-dir>/review-actions.md
    • <output-dir>/review-actions.json
  • optional scope constraints
  1. Require triage outputs

The triager must:

  • fetch review state (via ../review-fetch-phase/scripts/fetch-review-state.mjs)
  • triage review threads into review-actions.json decisions/status
  • write/update review-actions.md and review-actions.json
  1. Validate handoff contract

Before returning from triage, verify that <output-dir>/review-actions.json exists and is valid for implementer consumption (version, PR metadata, and actions[] with target.kind + target.nodeId).

implement

  1. Delegate to implementation sub-agent

Invoke the review implementer agent at ../review-implement-phase/agents/review-implementer.md and pass:

  • PR URL
  • <output-dir>/review-actions.md
  • <output-dir>/review-actions.json
  • optional scope constraints
  1. Require implementation outputs

The implementer must:

  • work through pending will_address actions
  • make focused, explicit-staging commits
  • run smallest relevant checks per action
  • reply "On it" when starting, then "Done" and resolve the thread when complete
  • update review-actions.json in-place (status, done)
  • re-fetch review state at the end to verify remaining actionable items

Responsibility note:

  • Posting "Done" and resolving completed threads belongs to the implementer phase and is part of marking actions done.

iterate

Repeat delegated triage → delegated implement until there are no remaining actionable review items.

Loop contract:

  1. Run triage delegation and read resulting review-actions.json.
  2. Inspect review-actions.json; if no will_address actions remain with pending or in_progress status, stop and report completion.
  3. Run implement delegation.
  4. Re-run triage delegation to refresh state and determine next iteration.
  5. Continue until clear.

Optional shortcuts (repo-specific)

If this repo provides dedicated slash commands or subagents for triage/implementation, prefer them to reduce manual steps.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Github Review Iteration AI skill do?

Orchestrates a GitHub PR review loop by delegating triage and implementation to dedicated sub-agents, then repeating until actionable review items are cleared. Use when the user says “address PR review”, “triage review comments”, or “iterate until review is clean”.

Why use Github Review Iteration on TypingMind?

Because you install it once and use it with any model. Github Review Iteration is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Github Review Iteration in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/prisma/orm/tree/main/skills-contrib/github-review-iteration. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Github Review Iteration?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Github Review Iteration?

As many as you like. As long as a model supports skills, you can use Github Review Iteration with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Github Review Iteration AI skill free?

Yes. It is published on GitHub by prisma under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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