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 simota/agent-skills --skill gaugegit clone --depth 1 https://github.com/simota/agent-skillsWrote 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/simota/agent-skills/gauge)<a href="https://agentmods.dev/skills/simota/agent-skills/gauge"><img src="https://agentmods.dev/badge/skills/simota/agent-skills/gauge/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/simota/agent-skills/gauge"><img src="https://agentmods.dev/badge/skills/simota/agent-skills/gauge.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 4 findings, up to high
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 →
- high Prompt Injection · line 6 Hidden instructions were detected in comments or invisible text. These could contain malicious directives. Manual review is recommended.Fix: Audit all comments and invisible characters. Remove any instructions that direct the agent to perform unauthorized actions. Use plain, reviewable content.
- high Rogue Agent · line 180 Skill modifies its own code, configuration, or behavior at runtime. Self-modification enables an agent to escalate privileges, disable safety constraints, or install persistent backdoors.Fix: Prevent the skill from modifying its own code, SKILL.md, or configuration files. Treat skill files as read-only at runtime.
- high Rogue Agent · line 244 Skill modifies its own code, configuration, or behavior at runtime. Self-modification enables an agent to escalate privileges, disable safety constraints, or install persistent backdoors.Fix: Prevent the skill from modifying its own code, SKILL.md, or configuration files. Treat skill files as read-only at runtime.
- medium Excessive Agency · line 108 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00038 | $0.05034 |
| Opus 5 | $0.00019 | $0.02517 |
| Sonnet 5 | $0.00008 | $0.01007 |
| Haiku 4.5 | $0.00004 | $0.00503 |
Grade A, and why
gauge 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 6d 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 — 268 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Gauge
"What gets measured gets managed. What gets audited gets normalized."
You are the normalization auditor and self-evolving compliance agent for the skill ecosystem. You measure every SKILL.md against the 21-item checklist (19 structural + 2 content), classify violations with surgical precision, and produce actionable fix snippets — never vague recommendations. You also research emerging best practices via web sources and safely evolve your own detection patterns. You write no code and edit no SKILL.md files directly; you recommend only.
Principles: Measure precisely · Classify objectively · Recommend concretely · Evolve safely · Never edit directly · Continuous over periodic · Calibrate to reduce noise
What ships with it
16 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.
- _common 10 B
- reference/_common 13 B
- reference/architect 15 B
- reference/autorun-schema.md 911 B
- reference/compass 13 B
- reference/content-quality-audit.md 4.6 KB
- reference/detection-patterns.md 16 KB
- reference/fix-templates.md 6.8 KB
- reference/launch 12 B
- reference/nexus 11 B
- reference/normalization-checklist.md 18 KB
- reference/official-standards.md 9.6 KB
- reference/report-templates.md 7.4 KB
- reference/self-evolution.md 8.6 KB
- reference/staleness-detection.md 13 KB
- reference/web-sources.md 6.6 KB
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.
- 6d ago First seen · 268 lines · 38 tokens per session scan A e87f3a7c6fcd
gauge is a skill published in the GitHub repository simota/agent-skills (76 stars, last pushed 7d ago), licensed MIT. It adds 38 tokens to every session and 5,034 once invoked, about $0.0002 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 2026-09-03.
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