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-impact-scangit 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-impact-scan)<a href="https://agentmods.dev/skills/human-avatar/skills-for-humanity/s4h-ethics-impact-scan"><img src="https://agentmods.dev/badge/skills/human-avatar/skills-for-humanity/s4h-ethics-impact-scan/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-impact-scan"><img src="https://agentmods.dev/badge/skills/human-avatar/skills-for-humanity/s4h-ethics-impact-scan.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 | $0.00105 | $0.01285 |
| Opus 5 | $0.00053 | $0.00642 |
| Sonnet 5 | $0.00021 | $0.00257 |
| Haiku 4.5 | $0.00011 | $0.00128 |
Grade A, and why
s4h-ethics-impact-scan 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 Impact Scan
A pre-ship ethical scan. Not a deep council — a structured sweep that forces you to see who's in the blast radius before you commit.
It runs two lenses: utilitarian (net effect on aggregate wellbeing) and justice/fairness (whether benefits and burdens are distributed equitably). These two together catch the most common pre-ship blind spots: harm that's small-per-person but large-in-aggregate, and harm that falls disproportionately on people with the least power.
Your Process
Step 1: Clarify the subject State what is being scanned — a feature, change, product decision, or policy. If the subject is vague, ask one clarifying question before proceeding.
Framing check: Confirm the specific ethical situation before continuing. State what you've identified — the actual thing being scanned and the parties most likely affected — in one sentence, then use AskUserQuestion:
- Question: "I'm reading this as: [your one-sentence framing of the specific feature/decision 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: Map the stakeholder field Before applying any lens, identify everyone affected:
- Direct users (who uses this feature and how?)
- Indirect parties (who is affected by users' use of this feature?)
- Third parties (suppliers, partners, communities)
- Non-users (people who don't opt in but are still affected)
- Future parties (people who will be affected by the precedent this sets)
Don't skip non-users and future parties. They are the most commonly missed.
Step 3: Apply the Utilitarian Lens For each stakeholder group:
- What is the likely benefit?
- What is the likely harm?
- What is the scale (how many people, how significantly)?
Then: Is the net effect positive? Who bears disproportionate cost to generate that net positive?
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 · 105 tokens per session scan A 631ce9527153
s4h-ethics-impact-scan is a skill published in the GitHub repository human-avatar/skills-for-humanity (223 stars, last pushed 1mo ago), licensed MIT. It adds 105 tokens to every session and 1,285 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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