feature-ethics-audit

feature-ethics-audit is a skill for Claude Code from Ludovic33Fr/product-ai-toolbox. It costs 58 tokens per session (1,665 once invoked), scanned A, original, MIT.

A feature review that examines whether a proposed product feature could manipulate people, reinforce bias, exploit weaknesses, or misuse data.

In plain words
What is it for?
Use it to assess features aimed at engagement, retention, or conversion, especially when they handle sensitive user data.
Why use it?
It makes ethical risks visible before a feature is built or launched and suggests less exposed alternatives.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the pm-augmente plugin — 12 skills, 6 agents shipped together

Good fit Use it to assess features aimed at engagement, retention, or conversion, especially when they handle sensitive user data.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/ludovic33fr/product-ai-toolbox/feature-ethics-audit
Install

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.

Any agent
npx skills add Ludovic33Fr/product-ai-toolbox --skill feature-ethics-audit
Clone the repo
git clone --depth 1 https://github.com/Ludovic33Fr/product-ai-toolbox

Made for: Claude Code.

Or install pm-augmente, the plugin that ships this one along with the rest of its 12 skills, 6 agents.

Wrote 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.

agentmods badge for feature-ethics-audit

README.md
[![agentmods](https://agentmods.dev/badge/skills/ludovic33fr/product-ai-toolbox/feature-ethics-audit/github.svg)](https://agentmods.dev/skills/ludovic33fr/product-ai-toolbox/feature-ethics-audit)
Your own site
<a href="https://agentmods.dev/skills/ludovic33fr/product-ai-toolbox/feature-ethics-audit"><img src="https://agentmods.dev/badge/skills/ludovic33fr/product-ai-toolbox/feature-ethics-audit/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.

agentmods 80×15 button for feature-ethics-audit

Your own site · 80×15
<a href="https://agentmods.dev/skills/ludovic33fr/product-ai-toolbox/feature-ethics-audit"><img src="https://agentmods.dev/badge/skills/ludovic33fr/product-ai-toolbox/feature-ethics-audit.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 58 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,665 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00058 $0.01665
Opus 5 $0.00029 $0.00833
Sonnet 5 $0.00012 $0.00333
Haiku 4.5 $0.00006 $0.00167

Measured 11d ago against content hash aa15727f73ee, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

feature-ethics-audit 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 11d 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.

skills/feature-ethics-audit/SKILL.md · 144 lines

How it starts

The opening of the file, as written. The whole thing — 144 lines — stays where its author put it; the contents beside it link to each section on GitHub.

feature-ethics-audit

Fiche éditoriale

Objectif. Auditer une feature envisagée sur ses risques éthiques (manipulation, biais, données).

Entrées. Description fonctionnelle, profil utilisateur cible, mécanisme de la feature.

Sorties. Diagnostic d'exploitation de vulnérabilité, alternatives moins exposées, recommandation explicite.

Cadence d'usage. À toute proposition de feature à fort potentiel d'engagement.

Mode opératoire

Quand m'invoquer

L'utilisateur me soumet une feature à auditer, particulièrement si elle vise l'engagement, la rétention, la conversion, ou si elle traite des données utilisateurs sensibles. Il me fournit la description fonctionnelle, le profil cible, et le mécanisme (ce qui s'active, quand, comment).

Procédure

  1. Reformuler la feature sans interpréter, en isolant le mécanisme central.
  2. Identifier la vulnérabilité activée : quel biais cognitif, quel besoin émotionnel, quelle asymétrie d'information la feature mobilise pour fonctionner ? Toute feature à fort engagement en active au moins un.
  3. Évaluer l'intensité : la feature est-elle conçue pour rendre l'arrêt difficile (sunk cost, FOMO, intermittence variable), ou laisse-t-elle l'utilisateur libre de partir sans coût psychologique ?
  4. Identifier les segments à risque : quels utilisateurs sont disproportionnellement exposés (mineurs, personnes vulnérables, utilisateurs en situation de stress, biais socio-économiques) ?
  5. Vérifier les données : la feature collecte/utilise-t-elle des données nouvelles ? Sont-elles strictement nécessaires ? Sont-elles cohérentes avec la finalité initialement consentie ?
  6. Générer 2-3 alternatives qui atteignent ~80% du bénéfice business sans le mécanisme problématique.
  7. Recommandation explicite : continuer / continuer avec garde-fous / ne pas continuer. Justifier en 2-3 phrases.

Format de sortie

# Audit éthique — {nom de la feature}

## Description reformulée

> {description en 2-3 phrases, neutre}

## Vulnérabilité activée

- **Biais ou besoin mobilisé** : {nom du biais ou besoin}
- **Intensité d'activation** : faible / moyenne / forte
- **Mécanisme** : {comment la feature exploite ce biais}

## Segments à risque

- {segment 1} — {raison de l'exposition disproportionnée}
- {segment 2} — ...

## Données

- Données collectées : {liste}
- Données strictement nécessaires : {liste}
- Cohérence avec la finalité consentie : oui / partielle / non

## Drapeaux levés

- {drapeau 1}
- {drapeau 2}

## Alternatives moins exposées

### A1 — {nom de l'alternative}
{description courte}
Bénéfice business attendu vs original : ~{x}%
Risque éthique vs original : {réduit / similaire / aggravé}

### A2 — ...

## Recommandation

**{continuer | continuer avec garde-fous | ne pas continuer}.**

{2-3 phrases qui justifient. Si "continuer avec garde-fous", lister les garde-fous obligatoires.}

Read the full file on GitHub · 144 lines

Changes

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.

  1. 11d ago First seen · 144 lines · 58 tokens per session scan A aa15727f73ee

Subscribe to this mod's changes

feature-ethics-audit is a skill published in the GitHub repository Ludovic33Fr/product-ai-toolbox (1 stars, last pushed 4mo ago), licensed MIT. It adds 58 tokens to every session and 1,665 once invoked, about $0.0003 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 2026-08-31.

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