review-spec

review-spec is a skill for Claude Code from Codagent-AI/agent-skills. It costs 54 tokens per session (348 once invoked), scanned A, original, MIT.

A review of proposals, specifications, designs, and task documents for consistency, traceability, and testability.

In plain words
What is it for?
It is for checking that planning artifacts agree with one another and give implementers clear, testable instructions.
Why use it?
It identifies mismatched requirements, missing scenarios, unresolved placeholders, incomplete tasks, and broken references before implementation.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the codagent plugin — 26 skills shipped together

Good fit It is for checking that planning artifacts agree with one another and give implementers clear, testable instructions.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/codagent-ai/agent-skills/review-spec
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.

Any agent
npx skills add Codagent-AI/agent-skills --skill review-spec
Clone the repo
git clone --depth 1 https://github.com/Codagent-AI/agent-skills

Made for: Claude Code.

Or install codagent, the plugin that ships this one along with the rest of its 26 skills.

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-spec

README.md
[![agentmods](https://agentmods.dev/badge/skills/codagent-ai/agent-skills/review-spec.svg)](https://agentmods.dev/skills/codagent-ai/agent-skills/review-spec)
Your own site
<a href="https://agentmods.dev/skills/codagent-ai/agent-skills/review-spec"><img src="https://agentmods.dev/badge/skills/codagent-ai/agent-skills/review-spec.svg" alt="Measured on agentmods" height="20"></a>
Per session 54 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 348 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 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 $0.00054 $0.00348
Opus 5 $0.00027 $0.00174
Sonnet 5 $0.00011 $0.00070
Haiku 4.5 $0.00005 $0.00035

Measured 8d ago against content hash 29780839612a, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-07, from the pricing page.

Security

Grade A, and why

review-spec 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.

skills/review-spec/SKILL.md · 41 lines

What it actually says

Review Spec

Review the supplied planning artifacts for internal coherence, structural testability, traceability, and cross-artifact consistency. Accept product and design choices as written; consequential gaps, alternatives, and weak decisions belong to an approach review.

Read all relevant markdown artifacts and infer their roles from path and content. Missing artifact types are not findings.

Check applicable concerns:

  • contradictions, scope drift, dropped commitments, or terminology drift across artifacts;
  • behavioral requirements without a concrete testable scenario, scenarios that do not express their requirement, or unresolved placeholders;
  • tasks that are not self-contained, lack exact source citations, alter source acceptance criteria, over-prescribe implementation, retain placeholders, or separate ordinary infrastructure, tests, or docs from the behavior they support; and
  • contradictions or unresolved references within an artifact.

Every finding must cite the exact path and section. Recommend the smallest consistency-preserving fix when more than one is possible.

Do not critique product intent or architecture, search for missing behavior or failure-mode decisions, review implementation code, invent missing artifacts, or rewrite the artifacts.

## Findings

### [high|medium|low] <title>
- Artifact: <path and section>
- Issue: <consistency, testability, traceability, or coherence defect>
- Fix: <smallest safe correction>

## Assessment
<Artifact readiness, or "No findings.">
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 · 41 lines · 54 tokens per session scan A 29780839612a

Subscribe to this mod's changes

review-spec is a skill published in the GitHub repository Codagent-AI/agent-skills (30 stars, last pushed 1mo ago), licensed MIT. It adds 54 tokens to every session and 348 once invoked, about $0.0003 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

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

next-partial-prefetching-adoption

Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…

vercel/next.js · 103 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

babysit-pr

Babysit a GitHub pull request after creation by continuously polling review comments, CI checks/workflow runs, and mergeability state until the PR is merged/closed or user help is required. Diagnose failures, retry likely flaky failures up to 3 times, auto-fix/push branch-related issues when appropriate, and keep…

openai/codex · 114 tokens