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 sananthanarayan/skilldrop --skill ai-usage-policygit clone --depth 1 https://github.com/sananthanarayan/skilldropWrote 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/sananthanarayan/skilldrop/ai-usage-policy)<a href="https://agentmods.dev/skills/sananthanarayan/skilldrop/ai-usage-policy"><img src="https://agentmods.dev/badge/skills/sananthanarayan/skilldrop/ai-usage-policy.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 5.1 | $0.00120 | $0.01383 |
| Opus 5 | $0.00060 | $0.00691 |
| Sonnet 5 | $0.00024 | $0.00277 |
| Haiku 4.5 | $0.00012 | $0.00138 |
Grade A, and why
ai-usage-policy 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 7d 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 — 68 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ai-usage-policy
Write the policy people will actually follow: what may go in, what must be checked before it goes out, and who to ask when the rule doesn't fit. The audience is every employee, not the security team — so it reads as rules for a job, not controls for an auditor.
A policy that prohibits without offering a permitted path doesn't reduce risk; it moves the same work onto personal accounts where nobody can see it. Every prohibition here carries an alternative.
How to respond
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Establish scope and the regulatory floor. Which population, which tools, and any regime already binding (sector rules, customer contracts, an existing data-classification scheme). If the organisation already classifies data, reuse those tier names rather than inventing a parallel scheme — two classification systems means neither is followed. Cap clarifying questions at 2.
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Define three data tiers and what may enter a tool at each. Three, because five is not memorable and one is not a policy:
Tier Examples Rule Open public docs, published marketing, open-source code any approved tool Internal internal docs, non-personal telemetry, private repo code approved tools with data-retention off / enterprise terms Restricted personal data, credentials, customer content under contract, regulated records never pasted; only via a named reviewed integration, if at all Give each tier two concrete examples from this organisation's actual work — abstract tiers are the reason policies get ignored.
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State permitted and prohibited uses as behaviours. ✅ "Drafting a first version of a customer email, then editing before sending." ❌ "Inappropriate use." Every prohibition names the permitted alternative: "Do not paste customer records to summarise them — use the approved integration, which does the same thing without the data leaving."
-
Build the human-review matrix on consequence, not on technology. What must a human check before an AI-assisted output is acted on? Key it to blast radius: reaches a customer · commits money or a legal position · changes production · informs a personnel decision. Each row says who reviews and what "reviewed" means. Low-consequence internal drafting needs no gate — say so, or the policy loses credibility everywhere else.
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.
- 7d ago First seen · 68 lines · 120 tokens per session scan A 585ad3a98116
ai-usage-policy is a skill published in the GitHub repository sananthanarayan/skilldrop (2 stars, last pushed 23d ago), licensed MIT. It adds 120 tokens to every session and 1,383 once invoked, about $0.0006 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 2026-08-31.
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