Wf Review

Community

tomsej

wf-review

Review a change with an optional workflow contract using deterministic scope, adaptive cross-model reviewers, verified findings, fixes, and a gate. Use standalone or from wf-impl.

Overview

Publisher	tomsej
Repository	`pi-ext`
Skill name	`wf-review`
Stars	80
Forks	8
Bundled files	Instructions only
License	MIT
Links	Skill source Repository

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 tomsej on GitHub. Read the source before you install it.

Installation

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

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/tomsej/pi-ext/tree/main/skills/wf-review
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.

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/tomsej/pi-ext.git /tmp/pi-ext
mkdir -p .claude/skills
cp -r /tmp/pi-ext/skills/wf-review .claude/skills/wf-review

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Wf 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 Wf 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 Wf 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.

/wf-review

Argument: optional absolute path to a contract. Without a contract, review the current diff. You own the review: reviewers only report; verify every finding and delegate fixes sequentially. Never edit production code yourself.

GATE = node ~/Workspace/pi-ext/wf/wf-gate.mjs

1. Scope

TARGET = sc worktree status --json → target_branch (fallback main). Run ocr delegate preview --from TARGET --to HEAD, restore excluded product files such as .md when they carry the change or the contract names them, then load rules with ocr delegate rule <files>. Keep lockfiles and generated code out.

Write .wf/review-packet.md: contract path or “no contract”, refs, merge_base, final files, diff stats, and rules. Every reviewer gets it. Without ocr, use git diff --stat TARGET...HEAD and disclose the fallback.

2. Pick the panel

With a contract, use its exact GATE agents <spec> --json → review plan; never infer models from YAML. Without a contract, inspect the packet and use:

docs/tests-only with no runtime behavior or sensitive config: 1 reviewer;
behavior changes: 2 reviewers for correctness and tests/edge cases;
security, auth, permissions, secrets, migrations, data integrity, concurrency, public APIs, or a large cross-module diff: 3 reviewers for correctness, the relevant risk, and impact/tests.

Read PI_PROVIDER and PI_MODEL. Reviewers must differ by model from the current session and each other. Prefer claude/fable, codex/gpt-5.6-sol, pi/zai/glm-5.2, then pi/anthropic/claude-opus-5. Skip matching or unavailable models, record the fallback, and never add reviewers merely because more models exist.

3. Review — max 2 rounds

Run each round's reviewers IN PARALLEL with subagent_spawn, each in a fresh context; never collapse them onto one model. Give each the packet, focus, read-only mode, and these rules:

with claude harness: the prompt MUST start with /code-review; codex would treat /review as plain text, so other harnesses get no slash prefix;
with pi harness: use sem_impact for blast radius/tests and sem_context;
findings are CLAIMS: severity, file:line, problem, and impact; with a contract also cite the violated criterion or scope;
Critical/High = bug, security, data loss, or uncovered criterion; Medium = performance or missing error handling with context; discard Low unless useful;
project rules are the checklist; read-only means never gh pr create or gh pr merge — for the codex harness this sentence is the only guard.

Save raw outputs to .wf/reviews/<HEAD>/round-<N>/<focus>.md; a missing planned report means incomplete review. Verify every finding against the code and explain rejections. Batch legitimate findings by 3–5 and delegate them sequentially; only one agent may edit at a time. Each batch ends with a commit and targeted test. Send repeatable mechanical findings to review-guards; never edit skills, the workflow pipeline, or AGENTS.md yourself.

Run round 2 only after fixes and only on changed areas. With a contract use its planned round; standalone use one best-matching reviewer.

4. Result

With a contract, GATE verify <spec> quick must pass (max 3 repair attempts per hypothesis). Without a contract, do not run GATE agents or verify; after fixes run targeted project tests. An unresolved finding or missing report means FAILED; otherwise PASS. wf-impl creates the review attest only after its final full gate; never create it here.

Report fixes with commits, rejections with reasons, planned/used reviewers and model fallbacks, artifact paths, scope stats, and gate/test results.

Frequently asked questions

What does the Wf Review AI skill do?

Review a change with an optional workflow contract using deterministic scope, adaptive cross-model reviewers, verified findings, fixes, and a gate. Use standalone or from wf-impl.

Why use Wf Review on TypingMind?

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

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

Which AI models can use Wf 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 Wf Review?

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

Is the Wf Review AI skill free?

Yes. It is published on GitHub by tomsej 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

/wf-review

1. Scope

2. Pick the panel

3. Review — max 2 rounds

4. Result

Frequently asked questions

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