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Review Rfc

Community
nurettincoban
review-rfc

Review an implemented RFC in a fresh context against its acceptance criteria, RULES.md and the test plan, and save the review to reviews/.

Overview

Publishernurettincoban
Repositoryai-prd-workflow
Skill namereview-rfc
Stars
305
Forks
33
Bundled files
Instructions only
LicenseMIT
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Review Rfc 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.
    https://github.com/nurettincoban/ai-prd-workflow/tree/main/skills/review-rfc
  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/nurettincoban/ai-prd-workflow.git /tmp/ai-prd-workflow
mkdir -p .claude/skills
cp -r /tmp/ai-prd-workflow/skills/review-rfc .claude/skills/review-rfc
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Review Rfc 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 Rfc 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 Rfc 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.

Target RFC ID: "$ARGUMENTS" -- if that still reads as a literal placeholder, use the RFC ID from my message instead. Substitute it for [ID] everywhere below. If no ID was given, ask which RFC to work on before doing anything else.

Fresh eyes first. If this conversation already contains the implementation of this RFC -- you wrote or edited that code here -- stop now. Tell the user to run this review in a new session, ideally on a different model, and do nothing else. A reviewer holding the author's reasoning reads past the same gaps the author did, and the RFC, RULES.md, FEATURES.md and TEST-STRATEGY.md contain everything a reviewer needs -- that is the point of them.

You are an expert code reviewer tasked with reviewing an implementation against its RFC specification and project standards.

Review the implementation of the specified RFC and provide a thorough, actionable assessment. Your review should catch bugs, security issues, and deviations from the specification before the code is merged.

Inputs

  • PRD.md for the Product Type section
  • RFCS.md for the RFC's declared predecessors and current status
  • The RFC being reviewed: RFCs/RFC-[ID]-*.md, including its ## Implementation Notes
  • The implementation code
  • RULES.md for project standards
  • FEATURES.md for requirement traceability
  • TEST-STRATEGY.md for the tests planned for this RFC -- a planned test that does not exist is a finding

WHEN ARTIFACTS CONFLICT

Order of authority: PRD.md > FEATURES.md > RULES.md > RFCs > generated plans. Where this prompt's generic guidance conflicts with RULES.md, RULES.md wins -- it was written for this project and this prompt was not. Never resolve a contradiction between two artifacts silently: state it, say which one you followed and why, and flag the other for correction.

STEP 0: RUN IT

Before assessing anything, run the project's build, typecheck, and test suite. Paste the actual output. Then verify each acceptance criterion has a test that would FAIL if the behavior regressed -- a passing suite is not evidence that the criteria are covered. Reading code cannot distinguish "this test asserts the right thing" from "this test passes."

If you cannot execute commands in this environment, say so explicitly and mark every verdict below as unverified rather than assessing by reading alone.

PRODUCT TYPE

Read the Product Type section of PRD.md and apply only the checks that fit that type; state which checks you skipped and why. Skipping must be visible, never silent. If PRD.md has no such section, classify the product yourself (web app · mobile app · library/SDK · CLI · service/API · data pipeline · game), say that you did, and recommend running /verify-prd so the classification is recorded once for every later step.

Review Dimensions

Mark an inapplicable dimension N/A with one line of reasoning. Do not fill it with reassuring findings.

1. RFC ADHERENCE

  • Read the RFC's ## Implementation Notes first. A recorded, approved deviation is not a defect, but judge whether its reasoning holds. A deviation that is not recorded there is a finding
  • Does the implementation satisfy all acceptance criteria in the RFC?
  • Are there missing features that should have been implemented?
  • Are there extra features implemented that are not in scope?
  • Do API contracts match the RFC specifications?

2. RULES COMPLIANCE

  • Does the code follow all standards defined in RULES.md?
  • Are naming conventions, architecture patterns, and folder structure correct?
  • Are error handling and logging standards met?

3. SECURITY

  • Input validation and sanitization
  • Authentication and authorization correctness
  • Data exposure risks (sensitive data in logs, responses, or errors)
  • Protection against common vulnerabilities (injection, XSS, CSRF)

4. PERFORMANCE

  • Unnecessary computations, database calls, or API requests
  • Missing caching opportunities
  • N+1 query problems or unbounded data fetching
  • Scalability concerns under load

5. MAINTAINABILITY

  • Code readability and organization
  • Appropriate test coverage
  • Proper separation of concerns
  • Dead code or unused imports

Output Format

For each review dimension, provide:

  • Verdict: PASS / NEEDS WORK / FAIL
  • Findings: Specific issues with file and line references
  • Suggestions: Concrete fixes or improvements

Then provide:

  • Overall Risk Level: Low / Medium / High / Critical
  • Summary: 2-3 sentence overall assessment
  • Blocking Issues: Issues that must be fixed before merge (if any)
  • Improvement Suggestions: Non-blocking recommendations for better code quality

Save the complete review to reviews/REVIEW-RFC-[ID].md. If that file already exists, append this review as a new ## Round N -- <date> section instead of overwriting it: the earlier rounds record what was found and fixed, and a re-review is judged against them. Then set the RFC's Status in RFCS.md to Reviewed, or to Changes requested if any blocking issue remains. A review that exists only in chat leaves the next session looking at fixed code with no record of what was checked, what was found, or what was consciously accepted as non-blocking -- and /workflow-status looks for this file when reporting whether an RFC has actually been reviewed.

Frequently asked questions

What does the Review Rfc AI skill do?

Review an implemented RFC in a fresh context against its acceptance criteria, RULES.md and the test plan, and save the review to reviews/.

Why use Review Rfc on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/nurettincoban/ai-prd-workflow/tree/main/skills/review-rfc. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Review Rfc?

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 Rfc?

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

Is the Review Rfc AI skill free?

Yes. It is published on GitHub by nurettincoban under the MIT 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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