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 stefanoskarakasis/Product-Marketing-Skills --skill privacy-policygit clone --depth 1 https://github.com/stefanoskarakasis/Product-Marketing-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/stefanoskarakasis/product-marketing-skills/privacy-policy)<a href="https://agentmods.dev/skills/stefanoskarakasis/product-marketing-skills/privacy-policy"><img src="https://agentmods.dev/badge/skills/stefanoskarakasis/product-marketing-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/stefanoskarakasis/product-marketing-skills/privacy-policy"><img src="https://agentmods.dev/badge/skills/stefanoskarakasis/product-marketing-skills/privacy-policy.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00084 | $0.03987 |
| Opus 5.5 | $0.00034 | $0.01595 |
| Sonnet 5.5 | $0.00017 | $0.00797 |
| Haiku 4.5 | $0.00008 | $0.00399 |
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 8d 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 — 365 lines — stays where its author put it; the contents beside it link to each section on GitHub.
privacy-policy
A drafting engine for PMMs and Product Managers who need a rigorous, jurisdiction-aware privacy policy ready for legal review — not a generic template.
The contract of this skill: this skill drafts. It does not certify.
Every output is a structured first draft requiring qualified legal review before publication.
High-risk clauses are marked [⚠️ LEGAL REVIEW REQUIRED] throughout — always.
Trigger
- When: Creating, updating, auditing, or reviewing a privacy policy or other data protection documentation, or answering what a product needs to comply with applicable privacy law.
- Not for: N/A — this skill has no overlap with another skill in this stack; overlap was considered and there is none.
- Example prompts:
- "Draft a privacy policy"
- "Are we GDPR compliant?"
- "Update our cookie policy"
- "Review our data retention policy"
- "What does our product need for CCPA compliance?"
Inputs
- Args: Product name, company legal name and address, privacy contact, user location(s), data types collected, third-party tools in use, and optionally an existing policy to refresh. Free format — Step 1 (Intake) fills gaps conversationally.
- Defaults: If the user skips intake, default to broadest jurisdiction coverage and flag all inferred inputs.
- Context keys:
/foundation/brain.md— optional. Product name/description, company name/address, primary market, data types, and ICP can all be inferred from it.- Brain contract: Reads brain content opportunistically (no fixed section numbers — this skill infers from whatever's present). Writes: none.
Pre-flight
Read /foundation/brain.md if available. Extract silently:
- Product name and description
- Company name and registered address
- Primary market / geographic focus → infer likely jurisdiction(s)
- Data types mentioned in the product description
- ICP → infer B2C vs B2B exposure
If missing, proceed without it and collect everything through Intake instead.
What ships with it
1 file 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.
- 8d ago Changed · +5 lines · -3 tokens per session a0aa4be487bd
- 16d ago Changed e01807b55253
- 1mo ago First seen · 360 lines · 87 tokens per session scan A d1072146994a
privacy-policy is a skill published in the GitHub repository stefanoskarakasis/Product-Marketing-Skills (8 stars, last pushed today), licensed MIT. It adds 84 tokens to every session and 3,987 once invoked, about $0.0003 per session on Opus 5.5. 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.
Other skills, from other repositories
privacy-policy
Draft a detailed privacy policy covering data types, jurisdiction, GDPR and compliance considerations, and clauses needing legal review. Use when creating a privacy policy, updating data protection documentation, or preparing for compliance.
draft-nda
Draft a detailed Non-Disclosure Agreement between two parties covering information types, jurisdiction, and clauses needing legal review. Use when creating confidentiality agreements or preparing an NDA for a partnership.
pestle-analysis
Perform a PESTLE analysis covering Political, Economic, Social, Technological, Legal, and Environmental factors. Use when assessing the macro environment, doing strategic planning, or evaluating external factors affecting your business.
kelly-legal-casebase-ingest
A review workspace for turning court judgments and arbitration awards into anonymized, structured internal case records. Court judgments and arbitral awards are written decisions that explain disputes and their outcomes.
kelly-legal-contracts
A review desk for business contracts such as non-disclosure agreements, service agreements, data-processing agreements, and statements of work. It compares clauses with a company's approved playbook and records review decisions.
kelly-legal-firm-radar
A dashboard for analyzing anonymized law-firm case information, lawyer capabilities and business performance. It prepares management insights and reports for review before export.