Agent skill · DevOps & Cloud

ai-ethics-review

Conduct a structured ethical review of an AI or ML feature, model, or product. Use when preparing to deploy an AI system, assessing algorithmic risk, auditing a model for bias, or producing a responsible AI impact assessment. Produces a structured ethics review covering fairness, transparency, privacy, safety, accountability, and societal impact with a risk tier score, pre-deployment checklist, and prioritised mitigations.

mohitagw15856github.com/mohitagw15856GitHub ↗
claude-codecursorMIT
Install
npx skills add mohitagw15856/pm-claude-skills --skill ai-ethics-review --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 1
SKILL.md size: 13 KB
Bundled scripts: none
Path: skills/ai-ethics-review/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 1,255
Language: HTML

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# 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? - **H

What's inside
Steps it walks through
  1. Required Inputs
  2. Output Structure
  3. 1. Feature Summary
  4. 2. Risk Tier Assessment
  5. 3. Fairness & Bias
  6. 4. Transparency & Explainability
  7. 5. Privacy & Data
  8. 6. Safety & Reliability
  9. 7. Accountability & Governance
  10. 8. Societal Impact
  11. 9. Mitigation Priorities
  12. 10. Pre-Deployment Checklist
  13. Quality Checks
  14. Anti-Patterns
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About this skill
What does the ai-ethics-review skill do?

Conduct a structured ethical review of an AI or ML feature, model, or product. Use when preparing to deploy an AI system, assessing algorithmic risk, auditing a model for bias, or producing a responsible AI impact assessment. Produces a structured ethics review covering fairness, transparency, privacy, safety, accountability, and societal impact with a risk tier score, pre-deployment checklist, and prioritised mitigations.

How do I install it?

Run `npx skills add mohitagw15856/pm-claude-skills --skill ai-ethics-review --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From mohitagw15856/pm-claude-skills, a repository with 1,255 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

Keep going