Borrowing it
Nothing to install: this file belongs to davidomil/cloudx. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/davidomil/cloudx/main/.agents/skills/review-agent-policy/SKILL.mdgit clone --depth 1 https://github.com/davidomil/cloudxWrote 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/davidomil/cloudx/review-agent-policy)<a href="https://agentmods.dev/skills/davidomil/cloudx/review-agent-policy"><img src="https://agentmods.dev/badge/skills/davidomil/cloudx/review-agent-policy/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/davidomil/cloudx/review-agent-policy"><img src="https://agentmods.dev/badge/skills/davidomil/cloudx/review-agent-policy.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 | $0.00030 | $0.00208 |
| Opus 5.5 | $0.00012 | $0.00083 |
| Sonnet 5.5 | $0.00006 | $0.00042 |
| Haiku 4.5 | $0.00003 | $0.00021 |
Grade A, and why
review-agent-policy 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 1mo 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.
What it actually says
Review Agent Guidance
Check whether the guidance helps an agent understand the task and repository without prescribing unnecessary roles, approvals, artifacts, or repeated work. Prefer concise project-specific context over generic advice or duplicated rules.
- Verify paths, commands, skill descriptions, and source claims.
- Look for conflicting instructions, accidental scope expansion, and rigid workflow requirements hidden in linked documents or validators.
- Distinguish adaptable prose from actual machine schemas and security checks. Simplifying guidance must not silently disable executable authorization, credential isolation, candidate identity, or artifact validation.
- Consider realistic tasks: can an agent make a narrow fix, investigate an unknown API, or perform a read-only review without unrelated ceremony?
Report substantive findings with evidence and practical corrections. Machine
output, when requested, follows docs/AI_CHANGE_PROCESS.md.
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.
- 1mo ago Changed · -19 lines · +3 tokens per session 8c0dc5550881
- 1mo ago First seen · 42 lines · 27 tokens per session scan A ce0d60a0440d
review-agent-policy is a skill published in the GitHub repository davidomil/cloudx (1 stars, last pushed 9d ago), licensed MIT. It adds 30 tokens to every session and 208 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 2026-08-31.
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Use when the scheduled repo-hygiene workflow runs from GitHub Actions (or an operator dry-run) to scan the repository for small, certain docs/test/code hygiene issues and fix them as one batched branch.
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