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Reviewer Protocol

CommunityPopular
bradygaster
reviewer-protocol

Reviewer rejection workflow and strict lockout semantics

Overview

Publisherbradygaster
Repositorysquad
Skill namereviewer-protocol
Stars
3.3K
Forks
506
Bundled files
Instructions only
LicenseMIT
Links
Skill sourceRepositoryHomepage
  • 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 bradygaster on GitHub. Read the source before you install it.

Installation

Install the Reviewer Protocol 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/bradygaster/squad/tree/dev/packages/squad-cli/templates/skills/reviewer-protocol
  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/bradygaster/squad.git /tmp/squad
mkdir -p .claude/skills
cp -r /tmp/squad/packages/squad-cli/templates/skills/reviewer-protocol .claude/skills/reviewer-protocol
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Reviewer Protocol 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 Reviewer Protocol 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 Reviewer Protocol 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.

Context

When a team member has a Reviewer role (e.g., Tester, Code Reviewer, Lead), they may approve or reject work from other agents. On rejection, the coordinator enforces strict lockout rules to ensure the original author does NOT self-revise. This prevents defensive feedback loops and ensures independent review.

Patterns

Reviewer Rejection Protocol

Quality and test reviewers may block a PR or merge on quality, coverage, or regression grounds. Treat that block as a rejection that enters the protocol below.

When a team member has a Reviewer role:

  • Reviewers may approve or reject work from other agents.
  • On rejection, the Reviewer may choose ONE of:
    1. Reassign: Require a different agent to do the revision (not the original author).
    2. Escalate: Require a new agent be spawned with specific expertise.
  • The Coordinator MUST enforce this. If the Reviewer says "someone else should fix this," the original agent does NOT get to self-revise.
  • If the Reviewer approves, work proceeds normally.

Strict Lockout Semantics

When an artifact is rejected by a Reviewer:

  1. The original author is locked out. They may NOT produce the next version of that artifact. No exceptions.
  2. A different agent MUST own the revision. The Coordinator selects the revision author based on the Reviewer's recommendation (reassign or escalate).
  3. The Coordinator enforces this mechanically. Before spawning a revision agent, the Coordinator MUST verify that the selected agent is NOT the original author. If the Reviewer names the original author as the fix agent, the Coordinator MUST refuse and ask the Reviewer to name a different agent.
  4. The locked-out author may NOT contribute to the revision in any form — not as a co-author, advisor, or pair. The revision must be independently produced.
  5. Lockout scope: The lockout applies to the specific artifact that was rejected. The original author may still work on other unrelated artifacts.
  6. Lockout duration: The lockout persists for that revision cycle. If the revision is also rejected, the same rule applies again — the revision author is now also locked out, and a third agent must revise.
  7. Deadlock handling: If all eligible agents have been locked out of an artifact, the Coordinator MUST escalate to the user rather than re-admitting a locked-out author.

Examples

Example 1: Reassign after rejection

  1. Runtime Engineer writes authentication module
  2. Quality Engineer reviews → rejects: "Error handling is missing. Another engineer should fix this."
  3. Coordinator: Runtime Engineer is now locked out of this artifact
  4. Coordinator spawns a different engineer to revise the authentication module
  5. The revision engineer produces v2
  6. Quality Engineer reviews v2 → approves
  7. Lockout clears for next artifact

Example 2: Escalate for expertise

  1. Original Author writes TypeScript config
  2. Lead reviews → rejects: "Need someone with deeper TS knowledge. Escalate."
  3. Coordinator: Original Author is now locked out
  4. Coordinator spawns new agent (or existing TS expert) to revise
  5. New agent produces v2
  6. Lead reviews v2

Example 3: Deadlock handling

  1. Original Author writes module → rejected
  2. Revision Engineer revises → rejected
  3. Quality Engineer revises → rejected
  4. All 3 eligible agents are now locked out
  5. Coordinator: "All eligible agents have been locked out. Escalating to user: [artifact details]"

Example 4: Reviewer accidentally names original author

  1. Original Author writes module → rejected
  2. Quality Engineer says: "The original author should fix the error handling"
  3. Coordinator: "The original author is locked out. Please name a different agent."
  4. Quality Engineer names a revision engineer
  5. Coordinator spawns the revision engineer

Anti-Patterns

  • ❌ Allowing the original author to self-revise after rejection
  • ❌ Treating the locked-out author as an "advisor" or "co-author" on the revision
  • ❌ Re-admitting a locked-out author when deadlock occurs (must escalate to user)
  • ❌ Applying lockout across unrelated artifacts (scope is per-artifact)
  • ❌ Accepting the Reviewer's assignment when they name the original author (must refuse and ask for a different agent)
  • ❌ Clearing lockout before the revision is approved (lockout persists through revision cycle)
  • ❌ Skipping verification that the revision agent is not the original author

Frequently asked questions

What does the Reviewer Protocol AI skill do?

Reviewer rejection workflow and strict lockout semantics

Why use Reviewer Protocol on TypingMind?

Because you install it once and use it with any model. Reviewer Protocol 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 Reviewer Protocol in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/bradygaster/squad/tree/dev/packages/squad-cli/templates/skills/reviewer-protocol. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Reviewer Protocol?

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 Reviewer Protocol?

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

Is the Reviewer Protocol AI skill free?

Yes. It is published on GitHub by bradygaster 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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