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.
npx skills add tomzx/agents --skill review-articlegit clone --depth 1 https://github.com/tomzx/agentsWrote 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.
[](https://agentmods.dev/skills/tomzx/agents/review-article)<a href="https://agentmods.dev/skills/tomzx/agents/review-article"><img src="https://agentmods.dev/badge/skills/tomzx/agents/review-article/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/tomzx/agents/review-article"><img src="https://agentmods.dev/badge/skills/tomzx/agents/review-article.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00027 | $0.01456 |
| Opus 5.5 | $0.00011 | $0.00582 |
| Sonnet 5.5 | $0.00005 | $0.00291 |
| Haiku 4.5 | $0.00003 | $0.00146 |
Grade A, and why
review-article 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 17d 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.
How it starts
The opening of the file, as written. The whole thing — 156 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Review Article
Audits an article and reports findings across seven categories: accuracy, clarity, structure, sourcing, audience fit, cross-article consistency, and style. Each finding is prioritized with 🔴 MUST fix, 🟡 SHOULD fix, or 🟢 MAY fix.
Prerequisites
- Article provided in context or as a file path to read
- Target audience (if known, improves audience-fit check)
- Relevant sources (optional, improves accuracy check)
- Other related articles in the same series (optional, enables cross-article consistency checks)
- Plan file (optional): a
plan.mdnext to the article, when one was written, enables the plan-fidelity check
Steps
- Read the article thoroughly.
- Summarize the message the article is trying to convey in two to three sentences, as you understood it. This serves as a comprehension check: if the summary diverges from what the author intended, the clarity or structure sections will explain why.
- Cross-reference claims against provided sources if available.
- Compare claims, figures, and terminology against other related articles in context if available.
- If a plan file is provided, or a
plan.mdexists next to the article, check the article against it (see Plan Fidelity below). - Identify issues in each category below.
- Prioritize each finding: 🔴 MUST, 🟡 SHOULD, 🟢 MAY.
- Report findings using the output format. Omit categories with no findings.
Review Checklist
Accuracy
- Are factual claims correct and verifiable?
- Are statistics, numbers, and dates accurate?
- Are technical concepts described correctly?
- Are attributed claims accurately representing their sources?
Clarity
- Does the article open with the most important point, not background?
- Is every sentence necessary, with no filler or padding?
- Are abstract points anchored with concrete examples, numbers, or comparisons?
- Is jargon avoided or defined on first use?
- Is the language plain: common words over less frequent synonyms, short sentences, active voice, and no figurative language (metaphors, idioms) that the reader must decode?
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.
- 17d ago Changed · +12 lines 6a81c65a9320
- 22d ago Changed · -11 lines bd3a91f91bb8
- 23d ago Changed · -3 lines 269739c1de03
- 27d ago Changed · +9 lines f20d7e66f870
- 28d ago Changed · +7 lines 6abc743c79db
- 1mo ago First seen · 142 lines · 27 tokens per session scan A 251f7fb3449b
review-article is a skill published in the GitHub repository tomzx/agents (13 stars, last pushed yesterday), licensed MIT. It adds 27 tokens to every session and 1,456 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-09-03.
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