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 cnfeat/top-pm-skills --skill ai-ethics-reviewgit clone --depth 1 https://github.com/cnfeat/top-pm-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/cnfeat/top-pm-skills/ai-ethics-review)<a href="https://agentmods.dev/skills/cnfeat/top-pm-skills/ai-ethics-review"><img src="https://agentmods.dev/badge/skills/cnfeat/top-pm-skills/ai-ethics-review/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/cnfeat/top-pm-skills/ai-ethics-review"><img src="https://agentmods.dev/badge/skills/cnfeat/top-pm-skills/ai-ethics-review.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 | $0.00088 | $0.03107 |
| Opus 5 | $0.00044 | $0.01554 |
| Sonnet 5 | $0.00018 | $0.00621 |
| Haiku 4.5 | $0.00009 | $0.00311 |
Grade A, and why
ai-ethics-review 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 — 216 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Ethics Review Skill
This skill produces a structured ethical review of an AI or machine learning feature, model, or product. Output covers fairness, transparency, privacy, safety, accountability, and societal impact — with risk scoring, prioritised mitigations, and a checklist suitable for governance review or responsible AI documentation.
⚠️ This skill provides a structured framework for identifying and documenting ethical risks. It is not a substitute for legal advice, regulated algorithmic impact assessments, or specialist ethics review required in specific jurisdictions (e.g. EU AI Act, UK AI regulation).
Required Inputs
Ask the user for these if not provided:
- Feature or model name and what it does
- Who it affects — which users or people does the AI interact with, make decisions about, or collect data from?
- What decisions or outputs it produces — recommendations, predictions, classifications, generation, automation?
- Consequentiality — how significant are the AI's decisions? (low-stakes suggestions vs decisions that affect employment, credit, health, safety, etc.)
- Data used — what training data, user data, or third-party data is used?
- Human oversight — is there a human in the loop, and at what stage?
- Deployment context — who will use this and how? (internal tool / consumer-facing / automated pipeline)
Output Structure
AI Ethics Review: [Feature / Model Name]
Product / system: [Name and brief description] Review type: [Pre-deployment review / Post-deployment audit / Change review] Risk tier: [High / Medium / Low — based on consequentiality, scale, and affected population] Reviewer: [Name / Team] Date: [Date] Status: [Draft / Approved / Requires escalation]
1. Feature Summary
| What it does | [1–2 sentences — plain English description of the AI feature and its purpose] |
| Who uses it | [End users / internal teams / automated system] |
| Who is affected by its outputs | [May be different from who uses it — e.g. an AI hiring tool is used by HR but affects candidates] |
| Output type | [Recommendation / Classification / Prediction / Generation / Automation / Scoring] |
| Scale | [How many people affected per day/month?] |
| Consequentiality | [High: affects access to services, employment, credit, health, safety / Medium: influences decisions / Low: suggestions with easy override] |
| Human oversight level | [Full automation / Human review before action / Human can override after action / Advisory only] |
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 · 216 lines · 88 tokens per session scan A 594025b6ea4e
ai-ethics-review is a skill published in the GitHub repository cnfeat/top-pm-skills (48 stars, last pushed 3mo ago), licensed MIT. It adds 88 tokens to every session and 3,107 once invoked, about $0.0004 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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