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Squid Review

Community
iusztinpaul
squid-review

Push the committed feature branch, create or update its PR, then run Product Architect acceptance and PR-Reviewer on it. Output: a clean PR with no blockers, or ONE rollup task. Trigger after a feature's tasks are implemented and committed.

Overview

Publisheriusztinpaul
Repositorysquid
Skill namesquid-review
Stars
207
Forks
34
Bundled files
Instructions only
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.

  • Self-contained

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

  • Open source

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

Installation

Install the Squid Review 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/iusztinpaul/squid/tree/main/skills/squid-review
  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/iusztinpaul/squid.git /tmp/squid
mkdir -p .claude/skills
cp -r /tmp/squid/skills/squid-review .claude/skills/squid-review
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Squid Review 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 Squid Review 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 Squid Review 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 — push, PA acceptance, PR review

Take the committed feature branch and turn it into a clean, pushed feature PR — or a rollup task describing what to fix. Both gates run on the pushed PR, sequentially: Product Architect (PA) acceptance first, then PR-Reviewer.

You are the orchestrator — a MANAGER. You push, launch the review agents, enforce the gates, and route failures back as rollup tasks. You do NOT review the diff, write code, or merge.

Read AGENTS.md first (tracker mode). Retry caps are below; the Severity Rule lives in the PR-Reviewer's role definition.

Input: a feature branch whose tasks are implemented and committed (the worktree when orchestrated by /squid-implement-night; the current branch when run standalone). Output: a pushed feature PR with NO blockers (Nits appended to the PR description), OR ONE rollup task.

Critical rules:

  • Never rubber-stamp an ACCEPT or NO BLOCKERS — spot-check the evidence; re-launch with feedback if it's thin.
  • One rollup task per failed gate, never one ticket per issue.

Step 1 — Push + create/update the PR

Agent(
  subagent_type="squid:software-engineer",
  prompt="""Push feature {title} and open/update its PR per your Push / open PR section. {Working directory: {path}.}
  The PR description summarizes the feature and lists each task by ID. Hand back the PR number."""
)

Capture the PR number for the gates below.


Step 2 — PA acceptance (Any product issues?)

Agent(
  subagent_type="squid:product-architect",
  prompt="""Acceptance review for feature {title} on PR #{N}. Read AGENTS.md first. Follow your acceptance-review role.
  {Working directory: {path}.}
  Walk the feature from the user's perspective against the Tasks Plan's acceptance criteria. Verdict: ACCEPT or REJECT. On REJECT, write ONE rollup task capturing ALL product issues."""
)
  • ACCEPT (verified) → Step 3.
  • REJECT → the PA filed ONE rollup task. Return it (see Output).
  • Cap: PA REJECT max 3 per feature. On the 4th would-be REJECT, stop and surface USER ACTION REQUIRED.

Step 3 — PR-Reviewer (Any Blockers?)

Agent(
  subagent_type="squid:pr-reviewer",
  prompt="""Review PR #{N} (branch feat/{slug}). Read AGENTS.md first. Follow your role definition.
  {Working directory: {path}.}
  Walk every review dimension, including the Simplicity / anti-over-engineering pass. Produce ONE rollup task if there are Blockers; else report NO BLOCKERS and append the Nits to the PR description."""
)
  • NO BLOCKERS → done. Nits are already on the PR description. Output: the clean pushed PR (#{N}).
  • Blockers → the PR-Reviewer filed ONE rollup task (Blockers + Nits). Return it (see Output).
  • Cap: PR-Reviewer max 3 per feature. On the 4th would-be Blockers verdict, stop and surface USER ACTION REQUIRED.

Output

  • Pass → "Feature PR #{N} clean (no blockers)." Hand to /squid-review-ci (or the human) for CI validation.
  • Fail → ONE rollup task. The caller (/squid-implement-night) routes it back through /squid-implement-task, then re-invokes /squid-review.

Frequently asked questions

What does the Squid Review AI skill do?

Push the committed feature branch, create or update its PR, then run Product Architect acceptance and PR-Reviewer on it. Output: a clean PR with no blockers, or ONE rollup task. Trigger after a feature's tasks are implemented and committed.

Why use Squid Review on TypingMind?

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

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

Which AI models can use Squid Review?

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 Squid Review?

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

Is the Squid Review AI skill free?

Yes. It is published on GitHub by iusztinpaul 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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