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Review

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HazAT
review

Coordinate a model-diverse local code review, automatically repair actionable findings with sequential workers, and re-review to a final verdict. Use for /review or when launched as an SC ensemble reviewer.

Overview

PublisherHazAT
Repositorypi-config
Skill namereview
Stars
450
Forks
44
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 HazAT on GitHub. Read the source before you install it.

Installation

Install the 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.
  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/HazAT/pi-config.git /tmp/pi-config
mkdir -p .claude/skills
cp -r /tmp/pi-config/skills/review .claude/skills/review
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable 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 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 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 and Repair

Review for correctness, security, regressions, and acceptance-criteria gaps. Delegated reviewers remain read-only. In coordinator mode, do not edit source directly; coordinate repair workers when the review requires changes.

Coordinator Mode

1. Establish context and scope

Derive the coordinator files before continuing:

bash
if [ -n "${PI_SESSION_FILE:-}" ]; then
  SESSION_FILE="$PI_SESSION_FILE"
elif [ -n "${CLAUDE_CODE_SESSION_ID:-}" ]; then
  SESSION_FILE="$HOME/.claude/projects/$(printf '%s' "${CLAUDE_PROJECT_DIR:-$PWD}" | tr '/.' '--')/${CLAUDE_CODE_SESSION_ID}.jsonl"
else
  echo "Error: /review requires a persistent session file (PI_SESSION_FILE or CLAUDE_CODE_SESSION_ID)." >&2
  exit 1
fi
test -f "$SESSION_FILE" || { echo "Error: session file not found: $SESSION_FILE" >&2; exit 1; }
PLAN_FILE="${SESSION_FILE%.jsonl}.plan.md"
TODOS_FILE="${SESSION_FILE%.jsonl}.todos.md"
REVIEW_FILE="${SESSION_FILE%.jsonl}.review.md"
  1. Require SESSION_FILE. Read PLAN_FILE or TODOS_FILE when each exists; they are optional.
  2. Recover intent from the current session and repository state when handovers are absent. Use the session-reader skill for focused extraction instead of parsing a large JSONL manually.
  3. Determine the exact base commit and current HEAD from managed-worktree and Git evidence. Ask only when a material scope ambiguity remains.
  4. Read ~/.pi/agent/skills/superconductor/SKILL.md and follow its live orchestration preflight.

2. Run one review attempt

Launch one model-diverse SC reviewer ensemble with three independent read-only Pi Chat UI sessions and unique attempt-scoped labels:

RolePi modelReasoningOwnsAvoids
leadopenai-codex/gpt-5.6-solhighbroad correctness, acceptance criteria, cross-report synthesisstyle-only polish
grokopenrouter/x-ai/grok-4.6configured defaultadversarial correctness, security, trust boundariesgeneral maintainability polish
kimiopenrouter/moonshotai/kimi-k3configured defaultregressions, cross-file behavior, frontend/accessibility, focused testsduplicating broad security review

At preflight, verify all three exact model IDs under the enabled pi provider with sc chat providers --json. Fail clearly rather than substituting a provider or model. Current sc team run cannot select a UI or model per role, so do not use it for this ensemble. Launch each role with sc layout run tabs, --provider pi, --ui chat, its exact --model, and --reasoning high only for the lead. The explicit Chat UI is required because SC's omitted/auto UI currently resolves delegated Pi launches to terminal mode rather than the configured Pi chat experience.

Give each role:

  • the exact diff/commit range and checkout;
  • a concise intent summary;
  • available plan/todos paths;
  • the session path only as optional fallback context;
  • its exclusive focus and the delegated contract below.

Start all three sessions without waiting between launches. Capture every returned stable target, wait for all three, and read each target's final report. Check target/provider errors and malformed or incomplete output; do not infer success from idle state.

After collecting the grok and kimi reports, send them to the same lead target for a synthesis turn. Require the lead to deduplicate claims, preserve disagreements, and return one proposed verdict with concrete findings. Wait for and read that same lead target again; do not launch a replacement lead.

Reviewers should not all run the same broad suite. Let the kimi role run focused tests when useful; the coordinator runs one definitive verification set after collecting reports.

Independently verify the lead's synthesis against the diff and specialist reports. A finding blocks approval only when it is concrete, introduced by the review range, and either P0/P1 or a P2 required by the selected acceptance criteria. P3 never blocks approval.

Write the current attempt to the supplied review file before any repair. Set APPROVED only when no blocking finding remains.

3. Repair NEEDS CHANGES automatically

When blocking findings remain:

  1. Require a clean checkout and a committed review range before automatic repair. If the reviewed implementation includes uncommitted changes, write NEEDS CHANGES and stop rather than letting a worker absorb user-owned work into a commit.
  2. Group findings into at most three coherent, dependency-ordered repair tasks. Use IDs REVIEW-FIX-<round>-<n>.
  3. Launch one source-writing Pi worker at a time through raw SC. Give it the review path, available plan/todos paths, exact findings, acceptance criteria, and checkout. Require it to read ~/.pi/agent/skills/worker/SKILL.md.
  4. Require one focused verified commit per repair task and no push. The worker must leave task-owned files clean after its final verification.
  5. Wait and read the same stable worker target. Verify its reported SHA, diff scope, checks, and clean status independently before launching the next worker.
  6. Record repair task IDs, commit SHAs, and verification in the review file.
  7. Re-run a fresh independent review attempt against the original base through the new HEAD, using new ensemble labels.

Run at most two repair rounds. Stop early and report NEEDS CHANGES when a worker blocks, verification fails, a safety decision requires the user, or blocking findings remain after round two. Do not hide or downgrade unresolved findings to force approval.

4. Finish visibly

Always end with a normal assistant response containing:

  • final verdict;
  • review-file path;
  • reviewed range;
  • repair commit SHAs;
  • remaining blocking findings or none;
  • verification results.

Do not end after only writing or rereading the review file.

The coordinator owns every handover-file write. Do not create a captain protocol, custom state store, worktree, push, PR, merge, or cleanup action.

Delegated Reviewer Contract

When the launch prompt identifies this session as an ensemble reviewer:

  • Read the supplied handovers and optional session path directly; treat them as read-only.
  • Inspect the assigned diff and trace relevant unchanged logic before judging it.
  • Stay within the assigned focus and avoid duplicating another role's scope.
  • Run only safe, read-only, focused checks. Do not run a broad suite unless your assigned focus requires it.
  • Report only concrete, introduced, actionable issues. Include priority, file/line, impact, and suggested fix.
  • Do not edit files, write handovers, commit, push, or launch agents.
  • Finish with a concise normal assistant response containing APPROVED or NEEDS CHANGES, followed by findings and evidence. Do not call sc team report; these model-pinned Chat UI reviewers are collected from their stable targets.

Priorities

  • P0: production breakage, data loss, or exploitable security flaw.
  • P1: likely functional failure or serious foot gun.
  • P2: concrete scoped improvement; blocks only when required by acceptance criteria.
  • P3: minor polish; never blocks approval.

Review File Format

markdown
# Code Review

**Verdict:** APPROVED | NEEDS CHANGES
**Session:** `/absolute/path/to/session.jsonl`
**Plan:** `/absolute/path/to/session.plan.md` | not provided
**Todos:** `/absolute/path/to/session.todos.md` | not provided
**Reviewed commits:** [base..HEAD]
**Repair rounds:** 0 | 1 | 2

## Summary
[Concise evidence-based assessment]

## Verification
- `[command]` — [result]

## Findings
### [P1] [title]
- **File:** `path/to/file:line`
- **Issue:** [specific problem]
- **Impact:** [observable consequence]
- **Suggested fix:** [concrete direction]
- **State:** open | addressed by `<sha>`

## Repair Commits
- `REVIEW-FIX-1-1``<sha>` — [summary and verification]

## Independent Reports
- **lead (`openai-codex/gpt-5.6-sol`, high):** [synthesized verdict and evidence]
- **grok (`openrouter/x-ai/grok-4.6`):** [verdict and evidence]
- **kimi (`openrouter/moonshotai/kimi-k3`):** [verdict and evidence]

## Residual Risks
- None | [nonblocking risk]

Omit empty findings or repair sections. Preserve concise attempt and repair evidence when replacing the file with the final verdict.

Frequently asked questions

What does the Review AI skill do?

Coordinate a model-diverse local code review, automatically repair actionable findings with sequential workers, and re-review to a final verdict. Use for /review or when launched as an SC ensemble reviewer.

Why use Review on TypingMind?

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

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

Which AI models can use 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 Review?

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

Is the Review AI skill free?

Yes. It is published on GitHub by HazAT 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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