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 agent-ecosystem/skill-validator --skill review-skillgit clone --depth 1 https://github.com/agent-ecosystem/skill-validatorWrote 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/agent-ecosystem/skill-validator/review-skill)<a href="https://agentmods.dev/skills/agent-ecosystem/skill-validator/review-skill"><img src="https://agentmods.dev/badge/skills/agent-ecosystem/skill-validator/review-skill/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/agent-ecosystem/skill-validator/review-skill"><img src="https://agentmods.dev/badge/skills/agent-ecosystem/skill-validator/review-skill.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Rogue Agent · line 98 Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
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 | $0.00067 | $0.01704 |
| Opus 5.5 | $0.00027 | $0.00682 |
| Sonnet 5.5 | $0.00013 | $0.00341 |
| Haiku 4.5 | $0.00007 | $0.00170 |
Grade A, and why
review-skill 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.
How it starts
The opening of the file, as written. The whole thing — 188 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Review Skill Workflow
You are helping a skill author review an Agent Skill before publishing. This is a multi-step process: determine environment, verify prerequisites, run structural validation, review content, optionally run LLM scoring, and interpret results. Follow every step in order.
Step 0: Determine Environment
Check for saved configuration:
cat ~/.config/skill-validator/review-state.yaml 2>/dev/null
If the state file exists with prereqs_passed: true, offer:
Found saved settings — configured for [provider/structural-only] reviews.
- Continue with saved settings — skip to Step 2
- Re-run prerequisite checks
- Change environment — switch provider or between LLM and structural-only
Option 1: read llm_scoring, provider, and cross_model from the file and
skip to Step 2.
Options 2-3: continue below.
If no state file exists, or the user chose to re-check/change, ask:
LLM scoring uses an Anthropic or OpenAI-compatible API, or the Claude CLI. Without an API key or CLI, we run structural validation only.
- Anthropic — use Claude via the Anthropic API (requires
ANTHROPIC_API_KEY)- OpenAI — use GPT via the OpenAI API (requires
OPENAI_API_KEY)- OpenAI-compatible — use a custom endpoint (Ollama, Groq, Azure, Together, etc.)
- Claude CLI — use the locally authenticated
claudebinary (no API key needed)- Skip LLM scoring — structural validation only
Options 1-4: set LLM_SCORING=true and record the provider choice.
Option 5: set LLM_SCORING=false. Run Step 1a only, then jump to Step 2.
If the user chose option 1 or 2, ask about cross-model comparison:
Scoring with a second model family gives more robust novelty scores, since each model has different training data. This requires API keys for both Anthropic and OpenAI.
- Yes, compare across model families — score with both Anthropic and OpenAI
- No, single provider is fine
Option 1: set CROSS_MODEL=true. Option 2: set CROSS_MODEL=false.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 First seen · 188 lines · 67 tokens per session scan A 9bfaae73364d
review-skill is a skill published in the GitHub repository agent-ecosystem/skill-validator (260 stars, last pushed yesterday), licensed MIT. It adds 67 tokens to every session and 1,704 once invoked, about $0.0003 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.
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