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.
git clone --depth 1 https://github.com/ThibautBaissac/rails_ai_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/commands/thibautbaissac/rails_ai_agents/review-artifact)<a href="https://agentmods.dev/commands/thibautbaissac/rails_ai_agents/review-artifact"><img src="https://agentmods.dev/badge/commands/thibautbaissac/rails_ai_agents/review-artifact/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/commands/thibautbaissac/rails_ai_agents/review-artifact"><img src="https://agentmods.dev/badge/commands/thibautbaissac/rails_ai_agents/review-artifact.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 | $0.00041 | $0.03376 |
| Opus 5.5 | $0.00016 | $0.01350 |
| Sonnet 5.5 | $0.00008 | $0.00675 |
| Haiku 4.5 | $0.00004 | $0.00338 |
Grade A, and why
review-artifact 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 2mo 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 — 348 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Presenting Reviews
Create one unique, self-contained HTML artifact that helps the intended audience understand the review scope, findings, evidence, severity, impact, remediation path, verification needs, and remaining questions.
Input
Review source: $ARGUMENTS
Accept any of these input forms:
- Review notes, audit notes, or markdown report.
- Code review comments or PR feedback.
- PR URL or PR number.
- Branch name, commit range, comparison ref, diff file, or patch file.
- QA report, bug bash notes, security review notes, accessibility review notes, performance review notes, design critique, or architecture assessment.
- Empty input, if the current repository and branch can provide enough context for a code-oriented review artifact.
If $ARGUMENTS is empty, first try to infer review context from the current git repository:
- Identify the current branch.
- Compare it against the repository's default branch when that is locally available.
- Inspect local review-related files if clearly named, such as
review.md,audit.md,findings.md, orreport.md. - If no reliable review source exists, ask the user for review notes, a report file, PR URL, branch, commit range, or diff file and stop.
If $ARGUMENTS references network-only review or PR metadata and network access is unavailable, use local information when possible and clearly mark unavailable metadata under Open questions.
Output Requirement
Create a single .html file inside the artifacts/ directory in the current working directory.
If artifacts/ does not exist, create it.
The file must be fully self-contained:
- Include all CSS in a
<style>tag. - Include all JavaScript in a
<script>tag. - Do not depend on external CDNs, fonts, images, packages, analytics, or network requests.
- Use semantic HTML and accessible markup.
- Work by opening the file directly in a browser.
Name the file using a short slug derived from the review subject, PR title, branch, or report title, for example:
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.
- 2mo ago First seen · 348 lines · 41 tokens per session scan A 16c2ba8ebfcf
review-artifact is a command published in the GitHub repository ThibautBaissac/rails_ai_agents (672 stars, last pushed 5mo ago), licensed MIT. It adds 41 tokens to every session and 3,376 once invoked, about $0.0002 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 2026-08-30.
review-artifact compared with commands from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| review-artifactThibautBaissac/rails_ai_agents | Command | 672 | 5mo ago | MIT | A | 41 | Claude Code |
| latex-diffflonat/flonat-research | Command | 149 | 3d ago | MIT | A | 52 | Claude Code |
| reporttransilienceai/communitytools | Command | 607 | 3mo ago | MIT | A | 37 | Claude Code |
| review-exportsaeedkolivand/ai-job-hunter-app | Command | 63 | yesterday | Apache-2.0 | A | 20 | Claude Code |
| review-templatesaeedkolivand/ai-job-hunter-app | Command | 63 | yesterday | Apache-2.0 | A | 18 | Claude Code |
| documentlgbarn/shipyard | Command | 67 | 16d ago | MIT | A | 13 | Claude Code |
| journal-entryGAJETOso/financeskills | Command | 47 | 5d ago | MIT | A | 18 | Claude Code |