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 agentmods add skills/agent-ecosystem/skill-validator/review-skillnpx 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.svg" alt="Measured on agentmods" 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 | $0.00067 | $0.01704 |
| Opus 5 | $0.00034 | $0.00852 |
| Sonnet 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 4d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- review-skill — 100% identical, 0 lines differ
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.
- 4d 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 (236 stars, last pushed 10d ago), licensed MIT. It adds 67 tokens to every session and 1,704 once invoked, about $0.0003 per session on Opus 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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