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-bias-checkgit 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-bias-check)<a href="https://agentmods.dev/skills/human-avatar/skills-for-humanity/s4h-ethics-bias-check"><img src="https://agentmods.dev/badge/skills/human-avatar/skills-for-humanity/s4h-ethics-bias-check/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-bias-check"><img src="https://agentmods.dev/badge/skills/human-avatar/skills-for-humanity/s4h-ethics-bias-check.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 | $0.00099 | $0.01390 |
| Opus 5 | $0.00049 | $0.00695 |
| Sonnet 5 | $0.00020 | $0.00278 |
| Haiku 4.5 | $0.00010 | $0.00139 |
Grade A, and why
s4h-ethics-bias-check 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 — 114 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ethics Bias Check
Algorithms that treat everyone the same can still discriminate. A ranking that optimises for engagement may systematically deprioritise certain groups. A model trained on historical data may encode historical injustice. A feature that works well on average may fail badly for users who aren't the implicit default.
This check surfaces those patterns before they ship.
Your Process
Step 1: Define the system What is the algorithm, model, or automated decision? What is its input? What is its output? Who does it make decisions about? What happens to people based on its output?
Framing check: Confirm the specific system before continuing. State what you've identified — the actual system being audited, its outputs, and the population it affects — in one sentence, then use AskUserQuestion:
- Question: "I'm reading this as: [your one-sentence framing of the system, its outputs, and who it affects]. 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: Identify the implicit default Every system has a default user in mind — often implicitly. Ask:
- Who was this optimised for?
- Whose behaviour or data was used to train or calibrate it?
- Who is absent from the training set or design process?
The implicit default is often the demographic that experiences least friction. Others bear the cost of that assumption.
Step 3: Check for direct bias Does the system use protected characteristics (age, gender, race, disability, location as proxy for race, etc.) as features, or correlates that map closely to them? Does it produce different outcomes for different demographic groups? Is that difference justified (e.g. a medical dosage model that accounts for body weight) or unjustified (e.g. a loan model that penalises postcodes that correlate with race)?
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 · 114 lines · 99 tokens per session scan A ab531616c593
s4h-ethics-bias-check is a skill published in the GitHub repository human-avatar/skills-for-humanity (223 stars, last pushed 1mo ago), licensed MIT. It adds 99 tokens to every session and 1,390 once invoked, about $0.0005 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.
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