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 stencila/stencila --skill skill-reviewgit clone --depth 1 https://github.com/stencila/stencilaWrote 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/stencila/stencila/skill-review)<a href="https://agentmods.dev/skills/stencila/stencila/skill-review"><img src="https://agentmods.dev/badge/skills/stencila/stencila/skill-review/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/stencila/stencila/skill-review"><img src="https://agentmods.dev/badge/skills/stencila/stencila/skill-review.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 Agent Snooping · line 142 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
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 | $0.00054 | $0.02671 |
| Opus 5.5 | $0.00022 | $0.01068 |
| Sonnet 5.5 | $0.00011 | $0.00534 |
| Haiku 4.5 | $0.00005 | $0.00267 |
Grade A, and why
skill-review 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 — 201 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Overview
Review an existing workspace skill for quality, correctness, completeness, and self-containment. Produce a structured report with specific, actionable suggestions for improvement. The review covers frontmatter fields, instruction clarity, step structure, examples, edge cases, adherence to the Agent Skills Specification, and whether the skill avoids depending on files outside its own directory.
Steps
- Identify the skill to review from the user's request — accept a skill name, a directory path, or a
SKILL.mdfile path - Resolve the skill file: if given a name, look for
.stencila/skills/<name>/SKILL.mdwalking up from the current directory; if given a path, use it directly - Read the full
SKILL.mdfile and any supporting files in the skill directory (scripts/,references/,assets/) - Check whether the skill refers to documentation, specifications, or other content outside the skill directory; if it does, assess whether that material should be copied, summarized, or excerpted into local
references/files instead - Evaluate the skill against each criterion in the Review Checklist below
- Produce a structured review report with a summary, per-criterion findings, and a prioritized list of suggestions
- If the user asks you to apply the improvements, make the changes and validate the result with
stencila skills validate <skill-name>
Review Checklist
Frontmatter
- name: present, matches directory name, valid kebab-case (
^[a-z0-9]([a-z0-9-]{0,62}[a-z0-9])?$) - description: present, under 1,024 characters, specific (not vague), includes keywords that help agents match the skill to user requests
- Optional fields:
license,compatibility,allowed-tools,metadata— check for correctness if present (e.g., valid SPDX identifier,compatibilityunder 500 characters,allowed-toolsis space-delimited)
Tooling and allowed-tools
- Check that the skill includes only tools it genuinely needs
- Check that tool choices match the actions described in the steps and examples
- If the skill modifies existing files, check whether
apply_patchoredit_fileshould be allowed - If the skill creates files from scratch, check whether
write_fileshould be allowed - If the skill may need clarification, confirmation, or approval from the user while executing, check whether
ask_usershould be allowed
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 · 201 lines · 54 tokens per session scan A d3fc13e8b40d
skill-review is a skill published in the GitHub repository stencila/stencila (909 stars, last pushed 2d ago), licensed Apache-2.0. It adds 54 tokens to every session and 2,671 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.
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