code-review

code-review is a skill for Claude Code, Codex from liza-mas/liza. It costs 43 tokens per session (5,567 once invoked), scanned A, original, Apache-2.0.

A two-sided process for reviewing code changes, where reviewers report problems and authors respond to them. It covers both raising findings and answering review feedback.

In plain words
What is it for?
Use it when reviewing staged changes, pull requests, commits, or pending work, and when responding to review comments or a rejected review.
Why use it?
It gives everyone a shared way to decide how serious a finding is and what would resolve it.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/liza-mas/liza/code-review
Any agent
npx skills add liza-mas/liza --skill code-review
Clone the repo
git clone --depth 1 https://github.com/liza-mas/liza

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for code-review

README.md
[![agentmods](https://agentmods.dev/badge/skills/liza-mas/liza/code-review.svg)](https://agentmods.dev/skills/liza-mas/liza/code-review)
Your own site
<a href="https://agentmods.dev/skills/liza-mas/liza/code-review"><img src="https://agentmods.dev/badge/skills/liza-mas/liza/code-review.svg" alt="Measured on agentmods" height="20"></a>
Per session 43 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 5,567 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00043 $0.05567
Opus 5 $0.00022 $0.02783
Sonnet 5 $0.00009 $0.01113
Haiku 4.5 $0.00004 $0.00557

Measured 6d ago against content hash 013fd74a2525, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, from the pricing page.

Security

Grade A, and why

code-review scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/code-review/SKILL.md · 345 lines

How it starts

The opening of the file, as written. The whole thing — 345 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Code review is risk mitigation, not gatekeeping — catching what the author couldn't see, and occasionally sharing a better pattern when it genuinely helps.

Two-sided protocol. Reviewers load it to raise findings; authors load it to answer them. Both sides read the whole file — a contested finding converges only when both agree what [blocker] means and what closes one. Everything through Review Summary Format is shared; Answering Findings is author-side.

Review Context

Before reviewing, establish context:

  • Scope: Default to staged files (git diff --cached --name-only, then git diff --cached --stat, then targeted path diffs). For PRs or commits, inspect changed files and stats before reading targeted hunks. Only broaden if explicitly asked.
  • Local working tree: For local reviews, triage unstaged and untracked files before review. If related but not staged, surface this explicitly and include only if the review target is "pending changes"; if unrelated, ignore; if unclear, ask before including.
  • Initial scope: Before reading the diff, record in one line what the change set out to do — take it from the author's stated intent when supplied (Pairing: Change Summary; multi-agent: task description and done-when; PRs: description and linked ticket), derive it only when it is not, and clarify with the author when it is unclear. This is the anchor for every later round: it bounds which behavior is required, not which code is inspected. Every submitted line is still reviewed for P0-P2 defects. Work beyond the initial scope is scope creep, not thoroughness — review it, then flag it [overreach].
  • Timing: Is now the right time for this functionality? Half-baked or premature additions warrant a [question].
  • Approach: Round 1 only. Is the approach sound, not merely correct — would another team take this shape, and was the first workable rung of CORE's Minimality Ladder taken? Raise an alternative only when it is named and its benefit demonstrable (have-you-considered); "I'd have done it differently" is not a finding. [question] when exploratory, [concern] when materially cheaper and still cheap to switch. After round 1 the approach is settled — reopening it is relitigation.
  • Absence: Review what should be here and is not. Take the baseline from the author's declarations (Pairing: Change Summary success criteria, doc impact, test impact; multi-agent: done-when) and check each against the diff. Diff-first reading optimizes for what is present; absence has to be sought deliberately. Where no declarations exist, say so — it caps confidence.
  • Diff-first: Read bounded diff context before source files. Prefer name-only/stat first, then targeted path or hunk diffs. Only read source when a finding needs surrounding context. Never pre-read the entire codebase.
  • Sources: State what you have read before drawing conclusions, per CORE Rule 5, and extend it as you read. The summary line reports this list; it does not create it. A conclusion reached before its source was read is not grounded by reading that source afterwards.
  • Size: Beyond 800 lines, 20 files, 80K chars total, or 20K chars in one file — large diffs hide bugs. Consider suggesting a split (PR) or incremental commits (pending); avoid unbounded full-diff reads, classify files and inspect targeted paths or hunks, and verify critical findings against source before tagging [blocker] or [concern].
  • Reviewer limits: If reviewing outside your expertise, say so. Make assumptions explicit.

Read the full file on GitHub · 345 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 6d ago First seen · 345 lines · 43 tokens per session scan A 013fd74a2525

Subscribe to this mod's changes

code-review is a skill published in the GitHub repository liza-mas/liza (364 stars, last pushed 4d ago), licensed Apache-2.0. It adds 43 tokens to every session and 5,567 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Related

Other skills, from other repositories

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

local-ai-agents

Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…

microsoft/ai-agents-for-beginners · 200 tokens

chronicle

Analyze Copilot session history for standup reports, usage tips, session search, and session reindexing. Use when the user asks for a standup, daily summary, usage tips, workflow recommendations, wants to search or find past sessions by keyword/file/PR, wants to reindex their session store, or asks about deleting…

microsoft/vscode · 72 tokens

chat-pet-sprite-creation

Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.

microsoft/vscode · 53 tokens

cpu-profile-analysis

Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…

microsoft/vscode · 71 tokens

next-cache-components-adoption

Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…

vercel/next.js · 95 tokens