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Agent skill

AI Ethics Review

by mohitagw15856 in mohitagw15856/pm-claude-skills

Conduct a structured ethical review of an AI or ML feature, model, or product.

MITAuto-check passedLegal & Compliance

Install AI Ethics Review

skills CLI
$ npx skills add mohitagw15856/pm-claude-skills --skill ai-ethics-review -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install mohitagw15856/pm-claude-skills ai-ethics-review --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/mohitagw15856/pm-claude-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/ai-ethics-review .claude/skills/ai-ethics-review && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
ai-ethics-review
GitHub stars
1.4k
Token cost
~3.4k tokens
SKILL.md length
1,639 words
Files
1
Skills in repo
1,348
Repo updated
First seen
Licence
MIT

At a glance

Conduct a structured ethical review of an AI or ML feature, model, or product.

  • Works in 10 steps: Feature Summary → Risk Tier Assessment → Fairness & Bias → …
  • Preparing to deploy an AI system
  • SKILL.md covers Required Inputs, Output Structure, 1. Feature Summary and 2. Risk Tier Assessment, plus 11 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

AI Ethics Review is an agent skill from mohitagw15856/pm-claude-skills. 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.

Its SKILL.md is about 3.4k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Legal & Compliance, covering AI governance, LLM guardrails and Deployment. The repository describes itself as: 1255 professional Agent Skills for Claude, ChatGPT, Gemini, Cursor & Codex — PRDs, postmortems, leases, medical bills, layoffs, go-bags, new countries. Plain markdown, MIT, in… The licence is MIT.

When your agent uses it

  • Preparing to deploy an AI system
  • Assessing algorithmic risk
  • Auditing a model for bias
  • Producing a responsible AI impact assessment

Example prompts

  • “/ai-ethics-review”

Workflow steps

10 steps, taken from the step headings in SKILL.md.

  1. Feature Summary
  2. Risk Tier Assessment
  3. Fairness & Bias
  4. Transparency & Explainability
  5. Privacy & Data
  6. Safety & Reliability
  7. Accountability & Governance
  8. Societal Impact
  9. Mitigation Priorities
  10. Pre-Deployment Checklist

What it can do on your machine

Read from SKILL.md and the folder at commit 1cbf1f0. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

AI Ethics Review loads about 3.4k tokens when it runs. Until then it costs about 111 tokens; SKILL.md has 1,639 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~111
When it runs · the whole SKILL.md, loaded when a task matches
~3.4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from mohitagw15856/pm-claude-skills at commit 1cbf1f0, republished under its MIT licence (© mohitagw15856). 1,639 words, ~3,412 tokens.

Download SKILL.mdSave it as .claude/skills/ai-ethics-review/SKILL.md (or your agent's skills folder).
name
ai-ethics-review
description
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.

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]

2. Risk Tier Assessment

FactorScore (1–3)Rationale
Consequentiality (impact on individuals)[1=low, 3=high][e.g. 3 — model output influences hiring decisions]
Scale (number of people affected)[1=few, 3=many][e.g. 2 — internal tool used for ~500 candidates/year]
Reversibility (can harm be undone?)[1=reversible, 3=irreversible][e.g. 2 — unfair rejection can be appealed but may not be caught]
Vulnerability of affected group[1=general population, 3=protected or vulnerable group][e.g. 2 — includes protected characteristics in the decision context]
Transparency (do affected people know?)[1=informed, 3=opaque][e.g. 3 — candidates are not told AI is used in screening]

Composite risk tier: [High (12–15) / Medium (7–11) / Low (3–6)]

Risk tier implications:

  • High: Mandatory senior ethics review, DPA/DPIA required, human-in-loop for all consequential decisions, ongoing monitoring required
  • Medium: Ethics review recommended, document mitigations, quarterly monitoring
  • Low: Standard review, document assumptions, annual review

3. Fairness & Bias

Does the AI treat people equitably across groups?

Protected characteristics relevant to this feature: [List applicable protected characteristics — age, gender, race/ethnicity, disability, religion, national origin, etc.]

RiskAnalysisMitigation
Training data bias[Does the training data reflect historical discrimination? e.g. hiring data that reflects past biases in who was hired][Audit training data for demographic representation / use debiasing techniques / document data lineage]
Proxy discrimination[Could the model use a proxy for a protected characteristic? e.g. using postcode as a proxy for race][Identify proxy features / test for disparate impact using adversarial debiasing]
Differential performance[Does the model perform differently across demographic groups? — e.g. lower accuracy for underrepresented groups][Disaggregate performance metrics by group / set minimum performance thresholds per group]
Feedback loops[Does the model's output reinforce existing disparities? e.g. recommending content that keeps disadvantaged groups in lower-engagement patterns][Monitor outcome distributions over time / implement feedback loop detection]

Fairness evaluation method: [What method will be used to measure fairness — statistical parity / equalised odds / individual fairness? Who is responsible for running it and how often?]

4. Transparency & Explainability

Can affected people understand how the AI makes decisions?

DimensionCurrent stateRequired stateGap
User disclosure[Are users told they're interacting with AI?][Yes — required for trust and regulation][e.g. No disclosure on current UI]
Decision explanation[Can the system explain why it reached a conclusion?][For high-stakes decisions: yes][e.g. Black-box model — no feature attribution available]
Right to know[Can affected people ask how a decision was made?][Yes — required under GDPR Art. 22 for automated decisions][e.g. No process exists]
Confidence calibration[Does the model express appropriate uncertainty?][Yes — overconfident models cause over-reliance][e.g. Model outputs binary label without confidence score]

Explainability approach: [LIME / SHAP / rule-based surrogate / LLM-generated rationale / none — and why]

5. Privacy & Data

Is personal data used responsibly and lawfully?

RiskAnalysisMitigation
Data minimisation[Does the model use more personal data than necessary?][Audit input features — remove any that don't improve performance and involve unnecessary data collection]
Data retention[How long is personal data retained for training and inference?][Define retention policy aligned to GDPR / CCPA / sector requirements]
Re-identification risk[Could model outputs or training data be used to identify individuals?][Differential privacy / k-anonymity / output rate limiting]
Third-party data[Is data from third parties used? Is it licensed for this use?][Audit data licensing / get legal sign-off on each third-party source]
Cross-border data transfer[Is personal data transferred across jurisdictions?][Legal review — Standard Contractual Clauses or equivalent]

DPIA required? [Yes / No / Uncertain — for High tier or whenever processing is likely to result in high risk to individuals under GDPR Art. 35]

6. Safety & Reliability

What happens when the AI gets it wrong?

Failure modeLikelihoodImpactMitigation
False positives[H/M/L][e.g. Flagging a legitimate transaction as fraud — customer locked out][Set threshold conservatively; human review for edge cases]
False negatives[H/M/L][e.g. Missing a real fraud case — financial loss][Monitor false negative rate; set minimum recall threshold]
Out-of-distribution inputs[H/M/L][Model behaves unpredictably on inputs outside training distribution][Input validation; confidence thresholding — route uncertain inputs to human review]
Model degradation[M][Performance degrades as data distributions shift post-deployment][Scheduled performance monitoring; drift detection alerts]
Adversarial inputs[L/M][Deliberate manipulation of inputs to game the model][Adversarial testing; rate limiting; anomaly detection on inputs]
Single point of failure[L/M][Model outage causes downstream system failure][Graceful degradation — define fallback behaviour when model is unavailable]

Fallback behaviour: [What happens if the AI is unavailable or returns low-confidence output? — e.g. route to human review / use rule-based fallback / block the action]

Show full SKILL.md (578 more words)Show less

7. Accountability & Governance

Who is responsible when things go wrong?

QuestionAnswer
Who owns this AI feature?[Team or individual with end-to-end accountability]
Who approved deployment?[Name and role — must be documented]
Who is responsible for ongoing monitoring?[Team and cadence]
Who can shut it down?[Who has kill-switch authority and under what conditions?]
How are incidents reported?[Internal escalation path + external disclosure process if required]
Is this subject to regulation?[EU AI Act / UK AI regulation / sector-specific rules — FINRA, FDA, FCA, etc.]

Incident response plan: [Link to or describe what happens if the model causes harm — detection, escalation, remediation, disclosure]

8. Societal Impact

Beyond individual users — what are the broader effects?

Impact areaRiskMitigation
Labour displacement[Does this AI automate tasks that currently employ people?][Transition plan / human-AI collaboration framing / skills retraining commitment]
Environmental impact[What is the carbon cost of training and inference?][Measure and offset; prefer efficient architectures; use renewable-energy infrastructure where possible]
Power concentration[Does this AI give the deploying organisation disproportionate power over individuals?][Ensure right to opt out; avoid lock-in; consider open alternatives]
Information ecosystem[Could this AI contribute to misinformation, filter bubbles, or manipulation?][Provenance labelling / content policies / algorithmic diversity requirements]

9. Mitigation Priorities

#RiskSeverityActionOwnerDeadline
1[Highest risk — e.g. No disclosure to affected candidates]Critical[Add AI disclosure to UI and candidate-facing documentation][PM + Legal][Before launch]
2[e.g. No fairness evaluation across demographic groups]High[Commission third-party fairness audit using [method]][ML team + external auditor][Within 30 days of launch]
3[e.g. No model monitoring in place]High[Deploy performance and drift monitoring dashboard][ML Ops][Launch day]
4[e.g. DPIA not completed]High[Complete DPIA with DPO before deployment][Legal / DPO][Before launch]

10. Pre-Deployment Checklist

  • Ethics review completed and approved by required reviewers
  • DPIA completed (if required)
  • Fairness evaluation completed and results documented
  • AI disclosure is in place wherever required
  • Human oversight mechanism is defined and tested
  • Kill-switch and escalation path is documented and tested
  • Model monitoring is deployed and alerting is configured
  • Data lineage and training data audit documented
  • Legal sign-off obtained on data licensing and cross-border transfers
  • Incident response plan in place

Quality Checks

  • "Who is affected" includes people the AI makes decisions about, not just who uses the product
  • Fairness analysis names specific protected characteristics, not just "diverse groups"
  • Safety section covers both false positive and false negative failure modes
  • Accountability section names real people, not teams or roles
  • Mitigations are specific and time-bound — not "monitor and review"

Anti-Patterns

  • Do not limit the affected-population analysis to users of the product — AI that makes decisions about people (hiring, credit, content moderation) affects non-users who have no opt-out
  • Do not accept "we will monitor" as a mitigation without specifying what is monitored, at what threshold, and who acts
  • Do not assign fairness analysis to the model team alone — protected characteristic analysis requires input from legal, HR, or a subject-matter expert
  • Do not defer the DPIA to post-launch — for high-risk tier systems, a DPIA is a pre-requisite for lawful deployment under GDPR
  • Do not conflate statistical accuracy with fairness — a model can be 95% accurate overall while performing significantly worse for a protected group

Example Trigger Phrases

  • "Run an AI ethics review for [feature]"
  • "Conduct an ethical impact assessment for our new ML model"
  • "Review the AI risks for our hiring / credit / recommendation system"
  • "Build a responsible AI checklist for our product"
  • "What are the ethical risks of using AI for [use case]?"

© mohitagw15856, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/ai-ethics-review of mohitagw15856/pm-claude-skills.

Open the folder on GitHubat commit 1cbf1f0

Compare with similar skills

AI Ethics Review next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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AI Ethics Review this skillmohitagw15856/pm-claude-skills1.4k—~3.4kAutomated safety check: PassMIT
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Iso42001Sushegaad/Claude-Skills-Governance-Risk-and-Compliance9461 repos~3.7kAutomated safety check: PassMIT
AI GovernanceHack23/cia239—~1.4kAutomated safety check: PassApache-2.0
Eu AI Act Tieringahmadvh/octochains377—~303Automated safety check: PassCustom licence

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Categories

Questions about AI Ethics Review

What does AI Ethics Review do?

Conduct a structured ethical review of an AI or ML feature, model, or product. AI Ethics Review is an agent skill from mohitagw15856/pm-claude-skills. Conduct a structured ethical review of an AI or ML feature, model, or product.

When should I use AI Ethics Review?

AI Ethics Review fits situations like: preparing to deploy an AI system; assessing algorithmic risk; auditing a model for bias; producing a responsible AI impact assessment.

How do I install AI Ethics Review in Claude Code?

Run `npx skills add mohitagw15856/pm-claude-skills --skill ai-ethics-review -a claude-code`. Or copy the skill folder (skills/ai-ethics-review in mohitagw15856/pm-claude-skills) into .claude/skills/ai-ethics-review in your project. Claude Code loads it when a task matches its description.

How do I install AI Ethics Review in Codex?

Run `npx skills add mohitagw15856/pm-claude-skills --skill ai-ethics-review -a codex`. Or copy the skill folder (skills/ai-ethics-review in mohitagw15856/pm-claude-skills) into .agents/skills/ai-ethics-review in your project. Codex loads it when a task matches its description.

Can I use AI Ethics Review in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add mohitagw15856/pm-claude-skills --skill ai-ethics-review -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/ai-ethics-review, .gemini/skills/ai-ethics-review, .github/skills/ai-ethics-review and .opencode/skills/ai-ethics-review in your project.

What does AI Ethics Review need to run?

SKILL.md names no scripts, command-line tools or credentials: AI Ethics Review is instructions for the agent only.

Does AI Ethics Review access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is AI Ethics Review safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does AI Ethics Review use?

AI Ethics Review is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does AI Ethics Review use?

About 3.4k tokens (SKILL.md is roughly 14k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to AI Ethics Review?

Skills that share tags, products or a category with AI Ethics Review: Facct Topic Selection (brycewang-stanford/Awesome-Journal-Skills, 1.2k stars), China AI Compliance Audit (jnMetaCode/shellward, 140 stars), Iso42001 (Sushegaad/Claude-Skills-Governance-Risk-and-Compliance, 946 stars) and AI Governance (Hack23/cia, 239 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains AI Ethics Review?

mohitagw15856 (a GitHub user) maintains it in mohitagw15856/pm-claude-skills, which has 1,434 GitHub stars. The repository holds 1,348 skills in this directory. The repository was last updated on October 9, 2026.

Source: mohitagw15856/pm-claude-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.