duet-agent: Skill for Claude Code

.agents/skills/review/SKILL.md

review is a skill for Claude Code, Codex from dzhng/duet-agent. It costs 91 tokens per session (454 once invoked), scanned A, a copy of review, Apache-2.0.

A final review workflow for a substantial software change. It checks the code's structure, the actual diff, and the related documentation in that order.

In plain words
What is it for?
For reviewing completed features or implementation passes, resolving findings, checking documentation links, and producing one final verdict.
Why use it?
It catches design problems, implementation mistakes, and broken or incomplete documentation before work is considered finished.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: installed under .agents/ (shared by several agents).

This is dzhng/duet-agent's own configuration. It tells Claude Code and Codex how to work on duet-agent itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything duet-agent configures →

Reuse

Borrowing it

Nothing to install: this file belongs to dzhng/duet-agent. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/dzhng/duet-agent/main/.agents/skills/review/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/dzhng/duet-agent

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 review

README.md
[![agentmods](https://agentmods.dev/badge/skills/dzhng/duet-agent/review.svg)](https://agentmods.dev/skills/dzhng/duet-agent/review)
Your own site
<a href="https://agentmods.dev/skills/dzhng/duet-agent/review"><img src="https://agentmods.dev/badge/skills/dzhng/duet-agent/review.svg" alt="Measured on agentmods" height="20"></a>
Per session 91 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 454 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 100% copy Near-identical to another mod 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 $0.00091 $0.00454
Opus 5 $0.00046 $0.00227
Sonnet 5 $0.00018 $0.00091
Haiku 4.5 $0.00009 $0.00045

Measured 8d ago against content hash 793308592d56, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-07, from the pricing page.

Security

Grade A, and why

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 8d 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.

Origin

This is a copy

100% identical to review — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

.agents/skills/review/SKILL.md · 35 lines

What it actually says

Review

A finished change earns "done" only after three lenses pass: its shape, its diff, and its docs. Each has a specialist skill. This skill owns only what they can't — the order they run in, the loop between them, and one verdict.

Workflow

  1. Scope. Fix the diff under review and confirm it's substantive; skip the whole pass for trivial edits.
  2. Shape — refactor-clean. Run it first, while restructuring is cheap and before you audit a diff that's about to move.
  3. Diff — code-review. Audit the settled shape. A finding that forces a structural change sends you back to step 2, not onward.
  4. Docs — write-docs. Update the docs this change touched. Then trace the link chain from the root README down to each one: every link on the path still resolves, and the hub-to-leaf flow still reads in order.
  5. Report. One verdict across all three passes — what each found, what you changed, what you left and why. Done only when every pass is clean or resolved.

Rules

  • When you enter a pass, read its skill — the rules live there, not here.
  • Order governs presentation, not disclosure: surface a finding the moment you hit it, even when a later pass owns it.
  • Loop, don't cascade: a later fix that reopens an earlier pass returns there. Stop when a full pass adds nothing.
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. 8d ago First seen · 35 lines · 91 tokens per session scan A 793308592d56

Subscribe to this mod's changes

review is a skill published in the GitHub repository dzhng/duet-agent (42 stars, last pushed 5d ago), licensed Apache-2.0. It adds 91 tokens to every session and 454 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to review, differing in 0 lines, and is treated as a copy.

Related

Other skills, from other repositories

evaluator-review-contract

Internal Auto-Harness evaluator skill for sprint contract review before implementation. Use only inside the Evaluator subagent during review mode.

redker56/auto-harness · 30 tokens

open-code-review

Performs AI-powered code review on Git changes using the ocr CLI from alibaba/open-code-review. Use when the user asks to review code, review a pull request, review staged/unstaged changes, review a commit, or compare branches for code quality issues. Produces line-level review comments and can automatically apply…

alibaba/open-code-review · 98 tokens

open-code-review-delegate

Delegation mode for open-code-review (OCR). Instead of OCR calling an LLM endpoint, this skill instructs the host agent to perform the code review itself, using OCR only for deterministic engineering: file selection and rule resolution. Use when the host agent should drive the review with its own LLM capabilities.

alibaba/open-code-review · 68 tokens

harness-creator

Build, audit, and improve harnesses that make AI coding agents reliable: AGENTS.md/CLAUDE.md instruction files, feature/state tracking, verification gates, scope boundaries, session handoff, memory persistence, context budgets, tool-permission safety, and multi-agent coordination. Use this whenever a coding agent is…

walkinglabs/learn-harness-engineering · 142 tokens

md-audit

Read-only code quality audit — scan the current working directory for common issues (bugs, dead code, security hotspots, missing error handling) and return a prioritised findings report. No files are edited. Use when asked to "audit the code", "quick audit", "find issues", "code scan", or "what's wrong with this…

chaitanyagiri/munder-difflin · 85 tokens

code-review

Paranoid architect review of code changes for bugs, security, missing tests, and undocumented assumptions. Works on local git diffs OR a GitHub pull request (e.g. owner/repo N). For PRs, can post findings as line-level review comments.

suyoumo/ClawProBench · 57 tokens