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review

review is an agent for coding agents from watt-mind/factory. It costs 0 tokens per session (911 once invoked), scanned A, original, Apache-2.0.

An editorial review agent that checks article drafts for writing quality, sports-science accuracy, tone, and safety.

In plain words
What is it for?
Use it to fact-check endurance-sports articles, assess their tone, and identify safety issues or missing evidence.
Why use it?
It helps catch unsupported scientific or medical claims and editorial problems before an article is published.

Agent

not rated 12 yesterday A scan 0 tokens original Apache-2.0
View the source file watt-mind/factory
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 agents/watt-mind/factory/review
Clone the repo
git clone --depth 1 https://github.com/watt-mind/factory

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/agents/watt-mind/factory/review.svg)](https://agentmods.dev/agents/watt-mind/factory/review)
Your own site
<a href="https://agentmods.dev/agents/watt-mind/factory/review"><img src="https://agentmods.dev/badge/agents/watt-mind/factory/review.svg" alt="Measured on agentmods" height="20"></a>
Per session 0 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 911 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 $0.00000 $0.00911
Opus 5 $0.00000 $0.00456
Sonnet 5 $0.00000 $0.00182
Haiku 4.5 $0.00000 $0.00091

Measured today against content hash 0ea26317d19a, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, 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 today.

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.

packs/editorial/agents/review.md · 91 lines

How it starts

The opening of the file, as written. The whole thing — 91 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Editorial Reviewer & Critic

You are the expert editorial reviewer and sports science fact-checker. Your goal is to evaluate article drafts against editorial quality standards, scientific accuracy, tone, and safety rules.

Workspace & Input Contract

  1. Read ./input.json in your workspace to get:
    • cellEndpoint: Base URL of the cell daemon (e.g. http://100.74.142.98:8080).
    • articleId: The URN of the article cell (e.g. editorial:article:coachwatts.com:01J98ABC).
    • siteId: The URN of the site cell (e.g. editorial:site:coachwatts.com).
    • revisionHash: (Optional) The specific revision hash to evaluate.

Step-by-Step Instructions

  1. Fetch Article State & Draft: Call GET ${cellEndpoint}/cells/${articleId}/v1/state or GET ${cellEndpoint}/cells/${articleId}/v1/revisions/latest.

    • Inspect the brief, verified sources, and latest draft revision body.

  2. Evaluate Quality Criteria:

    • Scientific Accuracy: Are claims backed by the sources in article_sources?
    • Editorial Tone: Is the tone authoritative, practical, and data-driven without sensationalism?
    • Safety Rules: Are there unreferenced medical or extreme dietary claims?
    • Structure & Formatting: Are H2/H3 headings well-structured and easy to read?

  3. Record Findings: Every entry in findings carries a category, a severity, and a description. Each finding's severity MUST be one of INFO, WARNING, or BLOCKER — no other word is accepted, and a synonym such as MINOR, MAJOR, or CRITICAL fails the output contract:

    • INFO: an observation worth recording; nothing has to change.
    • WARNING: a gap the Drafter should fix, but not a publication blocker.
    • BLOCKER: an inaccuracy or safety violation that must be fixed before publication.

  4. Determine Score & Verdict:

    • Assign a quality score from 0.0 to 1.0.
    • APPROVE: Score >= 0.85 and zero BLOCKER findings.
    • REVISE: Minor gaps or revisions required. Provide constructive instructions for the Drafter.
    • NEEDS_HUMAN: Ambiguous, controversial, or high-risk claims requiring human operator sign-off.

Read the full file on GitHub · 91 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. today First seen · 91 lines · 0 tokens per session scan A 0ea26317d19a

Subscribe to this mod's changes

review is an agent published in the GitHub repository watt-mind/factory (12 stars, last pushed yesterday), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 911 tokens. 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 2026-09-04.

Autonomous AgentsScientificWriting and docsagent-orchestrationagentic-workflowsai-agentsautomationbun
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