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Review Implement Phase

OrganizationPopular
prisma
review-implement-phase

Implements triaged review actions, commits focused fixes, and posts Done plus resolves threads. Use when the user wants only the implementation phase of the review-framework workflow.

Overview

Publisherprisma
Repositoryorm
Skill namereview-implement-phase
Stars
47.6K
Forks
2.5K
Bundled files
6
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.

  • 6 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 Review Implement Phase 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/review-implement-phase .claude/skills/review-implement-phase
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Review Implement Phase 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 Review Implement Phase 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 Review Implement Phase 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.

Review Implement Phase

Run only the implementation phase of the review-framework loop:

take triaged will_address actions, make code changes, commit in logical steps, post GitHub status updates, and update action status.

Run commands from this skill directory. All script paths below are relative to it.

Inputs

  • Required:
    • PR URL
    • existing review-actions.json in output dir
  • Optional:
    • output directory
    • scope constraints (specific action IDs or files)

If output directory is omitted, derive:

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

Preconditions

<output-dir>/review-actions.json must exist and be valid v2.

System dependencies required on PATH:

  • node (Node.js)
  • gh (GitHub CLI)

If either is missing, halt immediately and ask the user to install it. The implement-phase scripts require only node and gh.

GitHub admin capability must be available before starting implementation:

bash
node ./scripts/check-github-admin-ready.mjs --pr <PR_URL>

If missing, instruct user to run:

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

Behavior

  1. Read actions JSON and select actionable rows:
    • decision: will_address
    • status: pending | in_progress
  2. Preflight GitHub admin capability:
    • run check-github-admin-ready.mjs and fail fast if unavailable
  3. Always post standalone comments (never pending PR reviews):
    • When posting progress updates, do not create a PR review (draft/pending or otherwise).
    • Forbidden flows:
      • gh pr review --comment ...
      • GraphQL addPullRequestReview, addPullRequestReviewComment, addPullRequestReviewThread (this workflow never uses pending reviews)
    • Allowed flows:
      • thread replies via addPullRequestReviewThreadReply (or wrapper script)
      • issue comments via addComment (or wrapper script)
    • Before starting implementation:
      • Detect pending reviews authored by the acting user on this PR: gh api graphql -f query='query($owner:String!,$repo:String!,$pr:Int!,$before:String){viewer{login} repository(owner:$owner,name:$repo){pullRequest(number:$pr){reviews(last:100,states:PENDING,before:$before){pageInfo{hasPreviousPage startCursor} nodes{id author{login}}}}}}' -F owner=<owner> -F repo=<repo> -F pr=<number> --jq '.data as $d | $d.repository.pullRequest.reviews | {mine: [.nodes[] | select(.author.login == $d.viewer.login)], pageInfo}'
      • The author.login filter matters: another user's pending review is not yours to submit or dismiss, and must not block this workflow. --jq is gh's built-in filter and needs no jq binary.
      • The filter keeps pageInfo beside the matches, because an empty mine alone cannot tell "no pending review" from "the match is on an earlier page". Read both: while mine is empty and pageInfo.hasPreviousPage is true, re-run the query with -f before=<pageInfo.startCursor>. Conclude there is no pending review only when mine is empty and hasPreviousPage is false.
      • GitHub allows one pending review per user per PR, so mine holds at most one node across all pages.
      • If one exists, halt and clean it up (submit or dismiss) before continuing.
    • After posting any "On it" / "Done" comment:
      • Re-check for a pending review authored by the acting user, with the same filtered query and the same paging rule: keep reading pageInfo until mine is non-empty or hasPreviousPage is false.
      • If one exists, the workflow is blocked until it is cleaned up.
    • Implementation requirement:
      • For review_thread targets, always reply using thread replies (never inline PR review comments).
        • If you only have the thread node id, first fetch the thread’s primary comment node id, then call addPullRequestReviewThreadReply.
      • For pull_request_review targets (review-body findings, PRR_… node ids), inline replies are not possible. post-review-thread-reply.mjs auto-detects this and posts a top-level PR issue comment instead (response kind: "issue_comment"); there is no thread to resolve, so the implementer skips resolve-review-thread.mjs for these and records the issue-comment id in the action's done record.
  4. Delegate implementation to:
  • ./agents/review-implementer.md
  1. Require implementer responsibilities:
    • make code changes
    • run relevant checks
    • create focused commits
    • post "On it" when starting each action
    • post "Done" when finished (universal); resolve the thread only when target.kind === "review_thread" and a threadNodeId is available. pull_request_review targets have no inline thread, so the implementer skips the resolve step for them and records the issue-comment id in the action's done record (per behavior step 3).
    • use encoded helper scripts for thread admin operations:
      • node ./scripts/post-review-thread-reply.mjs --repo <owner>/<repo> --pr <number> --comment-node-id <primaryCommentNodeId> --body "<text>" (works for both review_thread and pull_request_review — auto-detects node kind)
      • node ./scripts/resolve-review-thread.mjs --thread-node-id <threadNodeId> (only for review_thread targets)
    • comments must be posted as individual standalone comments/replies, never as part of a pending review
    • after each action completion (Done + resolve when applicable), verify no new pending review was created by the acting user
    • never use inline parser snippets (for example: python -c, node -e, ruby -e, ad-hoc awk/sed JSON parsing)
    • only set status: done after Done (and, for review_thread targets, resolve) succeeds
    • update review-actions.json (status, done.doneAt, done.summary, done.commits) in the same completion step
  2. Render latest action markdown:
bash
node ../review-triage-phase/scripts/render-review-actions.mjs --in <output-dir>/review-actions.json --out <output-dir>/review-actions.md

Ownership

  • This phase owns actual fixes plus posting Done and resolving completed threads.
  • If GitHub thread reply/resolve cannot be performed, the phase is blocked and must not report completion.
  • If comments were accidentally posted as a pending review, the phase is blocked until the pending review is explicitly submitted or dismissed and the action comments are re-posted as standalone comments.

Output to user

Return:

  • commits created
  • actions transitioned to done
  • written artifacts (review-actions.json, review-actions.md)

Suggest next steps:

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

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 Review Implement Phase AI skill do?

Implements triaged review actions, commits focused fixes, and posts Done plus resolves threads. Use when the user wants only the implementation phase of the review-framework workflow.

Why use Review Implement Phase on TypingMind?

Because you install it once and use it with any model. Review Implement Phase 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 Review Implement Phase in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/prisma/orm/tree/main/skills-contrib/review-implement-phase. 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 Review Implement Phase?

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 Review Implement Phase?

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

Is the Review Implement Phase 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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