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 shawnpang/startup-founder-skills --skill privacy-policygit clone --depth 1 https://github.com/shawnpang/startup-founder-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/shawnpang/startup-founder-skills/privacy-policy)<a href="https://agentmods.dev/skills/shawnpang/startup-founder-skills/privacy-policy"><img src="https://agentmods.dev/badge/skills/shawnpang/startup-founder-skills/privacy-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/skills/shawnpang/startup-founder-skills/privacy-policy"><img src="https://agentmods.dev/badge/skills/shawnpang/startup-founder-skills/privacy-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 | $0.00031 | $0.01735 |
| Opus 5 | $0.00015 | $0.00868 |
| Sonnet 5 | $0.00006 | $0.00347 |
| Haiku 4.5 | $0.00003 | $0.00173 |
Grade A, and why
privacy-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 12d 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.
Copies of this mod
2 near-identical copies found in the catalogue:
- privacy-policy — 100% identical, 0 lines differ
- privacy-policy-mkurman — 91% identical, 14 lines differ
How it starts
The opening of the file, as written. The whole thing — 115 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Privacy Policy
When to Use
Activate when a founder needs to create a privacy policy for a new product launch, update an existing policy for new data practices or features, expand into a new jurisdiction (EU, California, etc.), or assess whether current data handling is properly disclosed. Also activate when the user asks about GDPR, CCPA, CPRA, or general data privacy compliance.
Context Required
- From startup-context: product type, platform (web/mobile/API), target customer segments, geographic markets, business model, tech stack.
- From the user: product name and URL, company legal name and address, contact email for privacy inquiries, what personal data is collected and how, which third-party services process data (analytics, payment processors, CRMs, AI providers), applicable jurisdictions, whether the product targets minors, and any existing privacy documentation.
Workflow
- Research the product -- Visit the product website or review the product description. Identify all data collection methods, third-party integrations, and primary features that involve personal data.
- Map data collection -- Categorize all data into: directly provided (forms, account creation), automatically collected (cookies, device info, usage data, IP addresses), third-party sources, and special/sensitive categories. Build a structured data inventory.
- Identify applicable laws -- Based on where users are located and where the company operates, determine which privacy frameworks apply: GDPR, CCPA/CPRA, state privacy laws, COPPA, industry-specific regulations. Note specific obligations per jurisdiction.
- Structure the policy -- Organize using the 15-section template below. Write in plain language at an 8th-grade reading level. Be specific about actual practices -- say "We collect your email address when you sign up" rather than "We may process identifiers."
- Flag legal review areas -- Mark sections requiring attorney review with
[LEGAL REVIEW REQUIRED]notation. These include legal basis determinations, international transfer mechanisms, and jurisdiction-specific rights. - Provide implementation context -- Explain why each section matters, what company decisions are needed, and what compliance considerations apply. Include a pre-publication checklist.
- Generate compliance summary -- Produce a separate document with data inventory table, jurisdiction applicability matrix, risk flags, and implementation checklist.
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.
- 12d ago First seen · 115 lines · 31 tokens per session scan A 34f8bcea8b46
privacy-policy is a skill published in the GitHub repository shawnpang/startup-founder-skills (321 stars, last pushed 5mo ago), licensed MIT. It adds 31 tokens to every session and 1,735 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 2026-08-30.
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Use this skill when the user says 'GDPR', 'data protection', 'privacy compliance', 'DPA', 'DSAR', 'data subject request', 'cookie consent', 'privacy audit', 'CCPA', or asks 'do I need GDPR for this repo'. Scans the repository to detect what personal data is collected, classifies sensitivity, determines whether GDPR…
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Use when the user uploads or pastes a non-disclosure agreement and asks for review, redline, risk assessment, or a recommendation on whether to sign. Identifies missing standard protections, one-sided or unusual provisions, and operational issues; produces a structured report with severity ratings and citations to…