committee-prep

committee-prep is a skill for Claude Code from Ludovic33Fr/product-ai-toolbox. It costs 54 tokens per session (1,436 once invoked), scanned A, original, MIT.

A French-language preparation guide for an arbitration or steering committee, meaning a meeting where people review options and make a decision.

In plain words
What is it for?
Use it to summarize available information, rank counterarguments, predict each participant's objections, propose a middle-ground wording, and define how the decision should be judged.
Why use it?
It helps participants anticipate disagreements and arrive with a clear summary, evidence, responses, and decision criteria.

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 summarize available information, rank counterarguments, predict each participant's objections, propose a middle-ground wording, and define how the decision should be judged.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/ludovic33fr/product-ai-toolbox/committee-prep
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 committee-prep
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 committee-prep

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

Your own site · 80×15
<a href="https://agentmods.dev/skills/ludovic33fr/product-ai-toolbox/committee-prep"><img src="https://agentmods.dev/badge/skills/ludovic33fr/product-ai-toolbox/committee-prep.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 54 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,436 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 $0.00054 $0.01436
Opus 5 $0.00027 $0.00718
Sonnet 5 $0.00011 $0.00287
Haiku 4.5 $0.00005 $0.00144

Measured 11d ago against content hash c46d57317334, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

committee-prep 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/committee-prep/SKILL.md · 140 lines

How it starts

The opening of the file, as written. The whole thing — 140 lines — stays where its author put it; the contents beside it link to each section on GitHub.

committee-prep

Fiche éditoriale

Objectif. Préparer un comité d'arbitrage avec dossier de fond et anticipation des objections.

Entrées. Sujet, recommandation provisoire, données disponibles, liste des participants.

Sorties. Synthèse des données, contre-arguments classés, objections anticipées par participant, formulation médiane, critères de décision.

Cadence d'usage. Avant chaque comité d'arbitrage majeur.

Mode opératoire

Quand m'invoquer

L'utilisateur prépare un comité d'arbitrage (steerco, comité produit, instance de décision) et me fournit le sujet, sa recommandation provisoire, les données qu'il a en main, et la liste des participants attendus. Il veut un dossier qui anticipe la dynamique de la réunion.

Procédure

  1. Synthèse des données : reformuler ce qui est documenté en 5 à 8 puces factuelles. Distinguer faits observés et hypothèses.
  2. Contre-arguments : générer les trois meilleurs contre-arguments à la recommandation, classés par force décroissante. Pour chacun, donner ce qui le réfute si possible.
  3. Cartographie des participants : pour chaque participant, identifier sa position probable (favorable / neutre / opposé) et son angle de préoccupation principal (technique, commercial, financier, RH, juridique, etc.). Si les positions ne sont pas connues, le dire.
  4. Objections anticipées : pour chaque participant identifié comme neutre ou opposé, formuler 1 à 2 objections probables et la réponse préparée.
  5. Formulation médiane : proposer une version intermédiaire de la recommandation, qui pourrait émerger comme compromis si la position initiale n'est pas validée. Préciser ce qu'on perd et ce qu'on gagne.
  6. Critères de décision : expliciter les critères sur lesquels la décision devrait se prendre, et signaler s'ils sont implicites ou divergents entre participants.
  7. Produire la sortie au format ci-dessous.

Format de sortie

# Dossier comité — {sujet}
*{date du comité} — préparé le {date du jour}*

## Recommandation soumise

> {1-3 phrases — recommandation à arbitrer}

## Synthèse des données (5-8 puces)

- {fait factuel} *(source : {référence})*
- ...

## Contre-arguments (par force décroissante)

### 1. {contre-argument principal}
{explication courte}
**Réfutation possible** : {élément qui désamorce}

### 2. ...
### 3. ...

## Cartographie des participants

| Participant | Position probable | Angle de préoccupation |
|-------------|-------------------|------------------------|
| {nom} | favorable / neutre / opposé / inconnu | technique / commercial / financier / juridique / RH / autre |

## Objections anticipées et réponses

### {participant 1}
- **Objection** : {formulation probable}
- **Réponse préparée** : {1-2 phrases}

### {participant 2}
...

## Formulation médiane

> {version compromis de la recommandation}

**Ce qu'on gagne** : {bénéfice de la médiane}
**Ce qu'on perd** : {compromis assumé}

## Critères de décision

- {critère 1} — {explicite / implicite ; consensus / divergent}
- ...

Read the full file on GitHub · 140 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 · 140 lines · 54 tokens per session scan A c46d57317334

Subscribe to this mod's changes

committee-prep is a skill published in the GitHub repository Ludovic33Fr/product-ai-toolbox (1 stars, last pushed 4mo ago), licensed MIT. It adds 54 tokens to every session and 1,436 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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