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 ai-analyst-lab/ai-analyst-plugin --skill guardrailsgit clone --depth 1 https://github.com/ai-analyst-lab/ai-analyst-pluginWrote 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/ai-analyst-lab/ai-analyst-plugin/guardrails)<a href="https://agentmods.dev/skills/ai-analyst-lab/ai-analyst-plugin/guardrails"><img src="https://agentmods.dev/badge/skills/ai-analyst-lab/ai-analyst-plugin/guardrails/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/ai-analyst-lab/ai-analyst-plugin/guardrails"><img src="https://agentmods.dev/badge/skills/ai-analyst-lab/ai-analyst-plugin/guardrails.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, 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 Excessive Agency · line 32 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.
- medium Excessive Agency · line 173 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 | $0.00091 | $0.02144 |
| Opus 5 | $0.00046 | $0.01072 |
| Sonnet 5 | $0.00018 | $0.00429 |
| Haiku 4.5 | $0.00009 | $0.00214 |
Grade A, and why
guardrails 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 9d 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 — 178 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: Guardrails Awareness
Purpose
Ensure that every success metric is paired with at least one guardrail metric, and that positive findings are checked for trade-offs before being presented as wins.
When to Use
Apply this skill in two situations:
- When defining metrics — after using the Metric Spec skill, check whether the metric has a guardrail pair
- When reporting positive findings — before presenting any improvement, check whether a related guardrail metric degraded
Instructions
What Are Guardrails?
A guardrail metric is a metric you don't want to degrade while optimizing a success metric. Guardrails protect against winning the metric game while losing the business game.
SUCCESS METRIC: The metric you're trying to improve
GUARDRAIL: The metric that must not get worse
The rule: Never celebrate an improvement on a success metric without checking its guardrail(s). An improvement with a degraded guardrail is a trade-off, not a win.
Common Guardrail Pairs
| Success Metric | Guardrail(s) | Why |
|---|---|---|
| Conversion rate | Average order value, Return rate | Aggressive discounts inflate conversion but erode margin and invite returns |
| Signup rate | Activation rate, 7-day retention | Lowering the signup bar brings in unqualified users who churn immediately |
| Revenue per user | User satisfaction (NPS/CSAT), Support ticket volume | Monetization pressure degrades experience |
| Feature adoption | Core workflow completion, Session duration | Forcing feature usage may disrupt existing workflows |
| Time to complete (speed) | Error rate, Quality score | Rushing degrades accuracy |
| Cost reduction | Quality, Customer satisfaction | Cutting costs can degrade service |
| Engagement (DAU, sessions) | Revenue per user, Churn rate | Engagement tricks (notifications, dark patterns) don't translate to value |
| Support resolution time | Customer satisfaction, Reopen rate | Fast close ≠ good close if tickets reopen |
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.
- 9d ago First seen · 178 lines · 91 tokens per session scan A 93dddb2f0646
guardrails is a skill published in the GitHub repository ai-analyst-lab/ai-analyst-plugin (32 stars, last pushed 13d ago), licensed MIT. It adds 91 tokens to every session and 2,144 once invoked, about $0.0005 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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