PM Skills is a collection of plain-Markdown instructions that teach AI assistants structured methods for handling professional, personal, and life-admin tasks. People use it with Claude, ChatGPT, Gemini, Cursor, Codex, and other supported agents for work such as writing product requirements, reviewing documents, or planning difficult situations.
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.
git clone --depth 1 https://github.com/mohitagw15856/pm-claude-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/rules/mohitagw15856/pm-claude-skills/ai-usage-policy)<a href="https://agentmods.dev/rules/mohitagw15856/pm-claude-skills/ai-usage-policy"><img src="https://agentmods.dev/badge/rules/mohitagw15856/pm-claude-skills/ai-usage-policy/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/rules/mohitagw15856/pm-claude-skills/ai-usage-policy"><img src="https://agentmods.dev/badge/rules/mohitagw15856/pm-claude-skills/ai-usage-policy.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00112 | $0.01326 |
| Opus 5 | $0.00056 | $0.00663 |
| Sonnet 5 | $0.00022 | $0.00265 |
| Haiku 4.5 | $0.00011 | $0.00133 |
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 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 — 75 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Usage Policy Skill
Most corporate AI policies fail in one of two ways: a fearful ban everyone quietly ignores (shadow AI, zero visibility), or legal fog nobody can apply to the question they actually have — "can I paste this customer email into Claude?" This skill writes the policy as a decision aid: one page, answerable in the moment of use, with the reasoning logged separately for counsel.
What This Skill Produces
- A one-page policy: approved tools, the data traffic-light, disclosure duties, review obligations, and how to get a tool approved
- A decision log: the reasoning behind each rule, for legal/leadership review
- A rollout note: how the policy lands without becoming shelfware
Required Inputs
Ask for (if not already provided):
- The org: size, industry, regulatory exposure (health, finance, gov contracts change the answers)
- Current reality: which AI tools are already in use — officially and (honestly) unofficially
- Data landscape: what sensitive classes exist (customer PII, PHI, source code, financials, client-confidential)
- Enterprise agreements in place: which tools have zero-retention/no-training terms signed vs consumer accounts
- Risk appetite: enable-with-guardrails or restrict-hard? (Get the sponsor's one-word answer.)
Policy Method
- Legalise reality first. Shadow AI is the largest risk created by strict policies. Start from what people already use; the policy's first job is making the sanctioned path easier than the unsanctioned one — approved tools with enterprise terms, clearly listed, with a fast approval lane for new ones (named owner, ≤2-week SLA).
- Rule on data, not tools. Tools churn monthly; data classes don't. The core artifact is a traffic-light table people can apply in three seconds:
- 🟢 Fine in approved tools — public info, your own drafts, non-confidential work product
- 🟡 Approved tools with enterprise terms only — internal business data, code, unreleased plans
- 🔴 Never in any AI tool (until a named exception is granted) — regulated data (PHI, card data), client-confidential under NDA, credentials, anything under legal hold Each row names examples from this org's actual work, not abstract categories.
- Set the accountability rule once, clearly. The human who ships it owns it — AI-assisted or not. From that root, the review duties follow: outputs going to customers/public/regulators get human review by someone competent to catch the errors; internal drafts don't need ceremony. State both halves; policies that demand review-everything get review-nothing.
- Decide disclosure deliberately. Internal: generally not required (it's a tool). External: disclose where the audience would feel deceived otherwise (bylined content, legal filings, anything presented as human judgment — expert reports, references) or where law/regulator requires it. Write the specific disclosure lines for this org's cases, not a principle.
- Keep the enforcement honest. First violations of 🟡 rules are coaching moments; 🔴 violations follow the existing data-handling discipline process (don't invent a parallel one). The policy names its owner, its review cadence (quarterly — the landscape moves), and where questions go today.
- Log the reasoning separately. Every rule gets one line in the decision log: what we ruled, why, what we considered. Counsel reviews the log; humans read the page.
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 · 75 lines · 112 tokens per session scan A 4259f1e3c3f0
ai-usage-policy is a cursor rule published in the GitHub repository mohitagw15856/pm-claude-skills (1,357 stars, last pushed yesterday), licensed MIT. It adds 112 tokens to every session and 1,326 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 2026-09-03.
Other cursor rules, from other repositories
cursor
Always-on my-adhd-caveman. Off with "stop adhd-caveman" or "normal mode".
ponytail
Ponytail, lazy senior dev mode. Always pick the simplest solution that works.
angular-20
This rule provides comprehensive best practices and coding standards for Angular development, focusing on modern TypeScript, standalone components, signals, and performance optimizations.
dev-standard
Apache Superset development standards and guidelines for Cursor IDE.
cli-error-handling
CLI command error handling patterns.
prefer-direct-imports-over-module-mocks
Prefer extracting a testable core over vi.mock / vi.resetModules when unit tests need to reach production logic entangled with config, env, or singletons.