discovery-cadrage

discovery-cadrage is a skill for Claude Code from Ludovic33Fr/product-ai-toolbox. It costs 48 tokens per session (1,650 once invoked), scanned A, original, MIT.

A French-language framework for planning user discovery, meaning research into people’s needs and behavior before building a product. It produces competing explanations, ways to test them, a target profile, an interview guide, and a method for grouping interview quotes.

In plain words
What is it for?
Use it when starting a new user-research topic and preparing interviews, hypotheses, validation signals, and a system for coding responses.
Why use it?
It helps teams begin research with clear questions instead of relying on one untested assumption. It also makes interview findings easier to organize and compare.

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 when starting a new user-research topic and preparing interviews, hypotheses, validation signals, and a system for coding responses.

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

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

Your own site · 80×15
<a href="https://agentmods.dev/skills/ludovic33fr/product-ai-toolbox/discovery-cadrage"><img src="https://agentmods.dev/badge/skills/ludovic33fr/product-ai-toolbox/discovery-cadrage.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 48 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,650 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 $0.00048 $0.01650
Opus 5 $0.00024 $0.00825
Sonnet 5 $0.00010 $0.00330
Haiku 4.5 $0.00005 $0.00165

Measured 12d ago against content hash 17a9cc90aeb0, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

discovery-cadrage 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 12d 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/discovery-cadrage/SKILL.md · 152 lines

How it starts

The opening of the file, as written. The whole thing — 152 lines — stays where its author put it; the contents beside it link to each section on GitHub.

discovery-cadrage

Fiche éditoriale

Objectif. Cadrer une discovery utilisateur avec hypothèses multiples et profil cible.

Entrées. Sujet d'investigation, données initiales, contexte stratégique.

Sorties. Cinq hypothèses, observables de validation, profil cible, guide d'entretien, grille de codage.

Cadence d'usage. À chaque nouvelle discovery démarrée.

Mode opératoire

Quand m'invoquer

L'utilisateur démarre une discovery (un nouveau sujet à investiguer auprès des utilisateurs) et veut un cadrage rigoureux avant de lancer les entretiens. Il me fournit le sujet, ce qu'il sait déjà, et le contexte stratégique qui justifie l'investigation.

Procédure

  1. Reformulation du sujet. Reformuler en une question d'investigation claire (forme : "Comment X expérimente Y dans le contexte Z ?"). Si la formulation initiale est trop vague ou trop précise, le signaler.
  2. Cinq hypothèses concurrentes. Générer cinq hypothèses réellement différentes sur ce qui se passe, pas cinq formulations de la même intuition. Les hypothèses doivent inclure au moins une qui contredit l'intuition initiale du PM.
  3. Observables. Pour chaque hypothèse, lister 2-3 observables qui la confirmeraient et 1-2 qui l'infirmeraient. Un observable est un fait directement constatable en entretien ou en data, pas une interprétation.
  4. Profil cible. Définir qui interroger : rôle, contexte, fréquence d'usage, ancienneté, signal d'éligibilité (ex : "a fait au moins 3 actions X dans les 30 derniers jours"). Justifier pourquoi ce profil et pas un autre.
  5. Guide d'entretien. Produire un guide de 30-45 minutes structuré en : ouverture, contexte personnel, situation cible (récit d'un cas récent), explorations spécifiques liées aux hypothèses, clôture. Privilégier les questions ouvertes commençant par "raconte-moi", "décris", "que s'est-il passé quand".
  6. Grille de codage. Produire une grille à 4-6 colonnes qui permettra de classer rapidement les verbatims : par hypothèse confirmée/infirmée, par catégorie thématique, par intensité du signal.
  7. Produire la sortie au format ci-dessous.

Read the full file on GitHub · 152 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. 12d ago First seen · 152 lines · 48 tokens per session scan A 17a9cc90aeb0

Subscribe to this mod's changes

discovery-cadrage is a skill published in the GitHub repository Ludovic33Fr/product-ai-toolbox (1 stars, last pushed 4mo ago), licensed MIT. It adds 48 tokens to every session and 1,650 once invoked, about $0.0002 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.