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Review

Community
notque
review

Code review: systematic single-file, parallel multi-reviewer, full-repo audit, PR diff review.

Overview

Publishernotque
Repositoryvexjoy-agent
Skill namereview
Stars
443
Forks
50
Bundled files
6
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.

  • 6 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by notque 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.
    https://github.com/notque/vexjoy-agent/tree/main/skills/review/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/notque/vexjoy-agent.git /tmp/vexjoy-agent
mkdir -p .claude/skills
cp -r /tmp/vexjoy-agent/skills/review/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

Three modes. Pick one by request shape, default to systematic.

Request matchesMode
Review a file, diff, or PRSystematic (default)
"parallel review", "multi-reviewer", "3-reviewer"Parallel
"full repo review", "codebase audit", "repo health", review ALL filesFull-repo

Deep References

SignalLoadWhy
Reviewing Go codereferences/go-review-patterns.mdExports, concurrency, resources, metrics, tests
Dispatching Architecture reviewer in parallel modereferences/architecture-smell-baseline.md12 Fowler smells with language counter-examples and severity cap
Writing full-repo reportreferences/report-template.mdReport structure and field definitions
Dispatching full-repo wave agentsreferences/audit-playbook.md8-category checklists with evidence requirements
Disproving findings before report (Systematic Phase 3.5, Parallel Step 3)references/disprove-pass.mdRefuter dispatch, drop rule, fail-open cases, model choice
Receiving review feedbackreferences/receiving-feedback.mdFeedback-handling patterns

Severity Classification

Shared across all modes. When in doubt, classify UP.

LevelScopeExamples
BLOCKINGSecurity, correctness, reliabilityAuth bypass, race condition, resource leak, logic error, test failure
SHOULD FIXMaterial quality, patterns, testsMissing tests, unhelpful errors, pattern violations, N+1 in hot paths
SUGGESTIONOptional, stylisticNaming preferences, comments, micro-optimizations

Decision: security/correctness/reliability risk? -> BLOCKING. Violates patterns or creates maintenance burden? -> SHOULD FIX. Purely stylistic? -> SUGGESTION.

Verdict Rules

ConditionVerdict
Any BLOCKING findingREQUEST-CHANGES (or BLOCK)
SHOULD FIX findings, no BLOCKINGFIX before merge
Only SUGGESTION or cleanAPPROVE

Omit empty severity sections. Include review scope, evidence, and limitations. Validate output:

bash
python3 scripts/validate-review-output.py --type systematic /tmp/review-output.md

Exit 0 = valid; 1 = schema errors; 2 = unparseable; 3 = missing jsonschema.


Mode 1: Systematic Review (default)

Single-reviewer, 4-phase review of a named file, diff, or PR.

Phase 1: UNDERSTAND

Read repository instructions and the complete diff. Read surrounding code to understand each changed path and its consumers. When signatures change, find all callers and interface implementations. Trace parameters to their source: query params may hold any user string; tokens may be server-issued IDs; enums have bounded values. Check validation at each caller.

Gate: Every changed path accounted for, callers traced where needed.

Phase 2: VERIFY

Run relevant tests and required repository checks. Reuse prior results only when they cover current code and environment. Check material claims in comments and PR description against code and tests. Missing tools, skipped checks, and inferred outcomes are not passes.

Gate: Claims have supporting evidence; required checks passed or the gap is recorded.

Phase 3: ASSESS

Assess risks: security (auth, validation, injection, secrets), performance (N+1, unbounded work, allocations on hot paths), architecture (conventions, compatibility, scope, unnecessary abstractions). For extracted helpers, recheck contract and callers.

Gate: Relevant risks and remaining uncertainty are explicit.

Phase 3.5: VERIFY FINDINGS

Before reporting, check each finding's input sequence, existing guards, and proposed fix. Drop unreachable or already-handled claims. Deduplicate when combining reviews; resolve severity from evidence. Then run the disprove pass in references/disprove-pass.md: it drops findings refuted by quoted code and keeps the rest.

Phase 4: DOCUMENT

text
Review Summary:
  Files Reviewed: N | Lines Changed: +X/-Y
  Test Status: PASS | FAIL | SKIPPED
  Risk Level: LOW | MEDIUM | HIGH | CRITICAL

BLOCKING:
  1. Issue and consequence — path/file.ext:42
SHOULD FIX:
  1. Issue and consequence — path/file.ext:52
SUGGESTIONS:
  1. Optional improvement — path/file.ext:62

Verdict: APPROVE | REQUEST-CHANGES | NEEDS-DISCUSSION
Rationale: Evidence, scope, and limitations.

Mode 2: Parallel Review

Three concurrent reviewers, then aggregate.

Step 1: Scope

Identify the diff or files. Record the reviewed revision.

bash
git diff --name-only HEAD
gh pr view --json files -q '.files[].path'

Select Architecture reviewer by language: Go -> golang-general-engineer; Python -> python-general-engineer; TypeScript -> typescript-frontend-engineer; mixed -> Explore.

Step 2: Dispatch

Dispatch three reviewers together. Read-only; no code edits.

ReviewerFocus
SecurityAuth, authorization, input validation, secrets, OWASP
Business LogicRequirements, edge cases, state transitions, failure modes
ArchitectureDesign, structure, performance, maintainability, scope

Pass references/architecture-smell-baseline.md verbatim to the Architecture reviewer. Require [Reviewer] file:line format with severity and consequence.

Step 3: Aggregate and Verdict

Deduplicate findings, resolve severity disagreements from evidence. Run the disprove pass in references/disprove-pass.md on the merged list. Apply shared verdict rules. Output a single combined report with severity matrix, combined findings, and recommendation.

Gate: Every reviewer returned, or missing coverage is explicitly stated without claiming approval.


Mode 3: Full-Repo Review

All source files through a 4-wave comprehensive review. Produces a prioritized backlog, not auto-fixes. Use for quarterly health checks, post-refactor audits, or new codebase onboarding.

Options: --directory [dir] (scope to one dir), --skip-precheck, --min-severity [level].

Step 1: Discover and Pre-check

Scan all source files (scripts/, hooks/, skills/, agents/, docs/). Never fall back to git diff.

bash
python3 ~/.claude/scripts/score-component.py --all-agents --all-skills --json

Save scores as triage context. A score alone never determines severity.

Step 2: Run Comprehensive Review

Call comprehensive-review with --review-only and the full file list. Run all 4 waves (0-3). Load references/audit-playbook.md as prompt context for wave agents.

Step 3: Report

Merge deterministic scores with LLM findings. Identify systemic patterns (3+ files). Write full-repo-review-report.md using references/report-template.md.

Gate: Report exists with severity sections and deterministic scores.


Error Handling

ErrorSolution
Reviewer times out or returns nothingReport partial findings, note gap, retry on reduced scope
Disprove pass fails or returns unparseable outputKeep all findings, note the gap in the verdict
Validator script missingRun review without validation, note gap in verdict
score-component.py failsProceed with LLM review only, note gap in report
Too many files for single sessionSplit by directory: scripts/, hooks/, agents/, skills/

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Review AI skill do?

Code review: systematic single-file, parallel multi-reviewer, full-repo audit, PR diff review.

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/notque/vexjoy-agent/tree/main/skills/review/review. TypingMind reads its SKILL.md and bundles its files 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 notque 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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