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 human-avatar/skills-for-humanity --skill s4h-ethics-data-auditgit clone --depth 1 https://github.com/human-avatar/skills-for-humanityWrote 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/human-avatar/skills-for-humanity/s4h-ethics-data-audit)<a href="https://agentmods.dev/skills/human-avatar/skills-for-humanity/s4h-ethics-data-audit"><img src="https://agentmods.dev/badge/skills/human-avatar/skills-for-humanity/s4h-ethics-data-audit/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/human-avatar/skills-for-humanity/s4h-ethics-data-audit"><img src="https://agentmods.dev/badge/skills/human-avatar/skills-for-humanity/s4h-ethics-data-audit.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 | $0.00113 | $0.01527 |
| Opus 5 | $0.00056 | $0.00763 |
| Sonnet 5 | $0.00023 | $0.00305 |
| Haiku 4.5 | $0.00011 | $0.00153 |
Grade A, and why
s4h-ethics-data-audit 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 — 120 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ethics Data Audit
Legal compliance sets the floor. This audit asks whether your data practices clear a higher bar: are they ethical?
The distinction matters. GDPR-compliant practices can still be extractive. Lawful data collection can still violate trust. This audit evaluates data decisions through two lenses that legal frameworks tend to underweight: deontological (what do users have a right to, regardless of what the terms allow?) and care ethics (what do you owe the people whose data you hold, given the relationship and the vulnerability involved?).
Your Process
Step 1: Define the data practice What data is being collected, retained, shared, or used? Be specific: what fields, what volume, what purpose, who can access it, how long is it kept, where does it go?
Framing check: Confirm the specific data practice before continuing. State what you've identified — the actual data being handled, its stated purpose, and the decision or change under review — in one sentence, then use AskUserQuestion:
- Question: "I'm reading this as: [your one-sentence framing of the data practice, its purpose, and the decision being audited]. Is that right?"
- Header: "Framing"
- Options:
- Yes — proceed — framing is correct
- Adjust — one element is off; user will correct it before you continue
- Reframe — different situation than read; incorporate the correction before proceeding
Step 2: Deontological Assessment — Rights and Duties Users have rights that don't disappear because they clicked "I agree." Examine:
- Informed consent: Do users genuinely understand what's being collected and why? Would they understand if you explained it plainly, without legal language?
- Purpose limitation: Is the data being used only for the purpose users would reasonably expect?
- Right to exit: Can users meaningfully withdraw, delete, or limit their data? Is that easy or deliberately difficult?
- Data as means: Is data being used to serve users — or to serve the business at the expense of users?
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 First seen · 120 lines · 113 tokens per session scan A 0658e72b3f1d
s4h-ethics-data-audit is a skill published in the GitHub repository human-avatar/skills-for-humanity (223 stars, last pushed 1mo ago), licensed MIT. It adds 113 tokens to every session and 1,527 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 skills, from other repositories
incorporation
A guide for moving from working as an individual business owner to operating through a company in Japan. It covers company types, setup steps, officer pay, taxes, and social insurance.
revenue-recognition
Determines when and how revenue is recognized — performance obligations, contract terms that change the answer, and the deal structures that create accounting problems. Use this to work out how a contract should be recognized, review a non-standard deal before it is signed, understand deferred revenue, or spot terms…
privacy-and-data-protection
Assesses and improves how personal data is collected, used, shared, and retained — data mapping, lawful basis, consent, processor agreements, subject rights, and breach obligations. Use this before launching anything that handles personal data, when adding a vendor that will process it, when a data subject request…
benefits-and-leave
Designs and runs employee benefits and leave — health and retirement plans, leave policy, cost and renewal, and the administration that keeps them compliant. Use this to design or review a benefits package, prepare for a renewal, write leave policy, handle a leave request, or decide what benefits are worth their cost.
compensation-and-leveling
Builds and maintains the leveling framework and pay structure — level definitions, salary bands, benchmarking, pay equity, and how raises and promotions are decided. Use this to design or revise leveling, set or adjust salary bands, benchmark against market, handle a compensation request or counteroffer, run a review…
performance-management
Runs performance systems that change behavior — expectations, feedback, review cycles, calibration, and handling underperformance. Use this to design or fix a review process, run calibration, write or coach on feedback, address sustained underperformance, or work out why reviews consume weeks and change nothing.