preparer-entretien

preparer-entretien is a skill for Claude Code from ai-swiss/base. It costs 12 tokens per session (1,869 once invoked), scanned A, original, Apache-2.0.

A structured workflow for preparing and documenting job interviews. It uses the job listing and candidate file to create questions, an evaluation grid, and post-interview notes.

In plain words
What is it for?
Use it to review an open position, analyze a candidate’s information, prepare questions and scoring criteria, then record notes and an assessment after the interview.
Why use it?
It helps keep the interview tied to the role’s requirements and makes candidate evaluation more consistent.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter. Also seen: built for aider.

Good fit Use it to review an open position, analyze a candidate’s information, prepare questions and scoring criteria, then record notes and an assessment after the interview.

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Install with agentmods
npx agentmods add skills/ai-swiss/base/preparer-entretien
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 ai-swiss/base --skill preparer-entretien
Clone the repo
git clone --depth 1 https://github.com/ai-swiss/base

Made for: Claude Code.

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 preparer-entretien

README.md
[![agentmods](https://agentmods.dev/badge/skills/ai-swiss/base/preparer-entretien/github.svg)](https://agentmods.dev/skills/ai-swiss/base/preparer-entretien)
Your own site
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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 preparer-entretien

Your own site · 80×15
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Per session 12 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,869 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. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
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 $0.00012 $0.01869
Opus 5 $0.00006 $0.00934
Sonnet 5 $0.00002 $0.00374
Haiku 4.5 $0.00001 $0.00187

Measured 10d ago against content hash 7ff9ac13785b, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

preparer-entretien 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 10d 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.

exemples/assistant-rh/.ai/agents/assistant-rh/skills/processes/preparer-entretien/SKILL.md · 192 lines

How it starts

The opening of the file, as written. The whole thing — 192 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Préparer un entretien

Préparer un entretien d'embauche de manière structurée: relire l'offre, analyser le profil du candidat, formuler les questions, construire la grille d'évaluation, puis, une fois l'entretien passé, aider à consigner les notes et l'évaluation.

Inputs

Demande à l'utilisateur:

  • Le nom du candidat: pour vérifier s'il existe un dossier dans candidatures/
  • Le poste concerné: pour identifier l'offre dans postes-ouverts/

Avant de commencer, vérifie:

  • Qu'une offre d'emploi existe dans postes-ouverts/ pour ce poste
  • Si un dossier candidat existe déjà dans candidatures/

Si .ai/journal/ contient des entrées récentes, lis-les pour le contexte.

Étapes

1. Relire l'offre d'emploi

Lis le fichier correspondant dans postes-ouverts/.

Résume les points clés:

«Pour rappel, voici les éléments clés du poste de [titre]:

  • Missions principales: [liste]
  • Profil recherché: [compétences clés]
  • Qualités attendues: [liste]

Est-ce toujours à jour, ou y a-t-il des ajustements?»

← Reformulation

2. Analyser le dossier du candidat

Si un fichier existe dans candidatures/, lis-le et résume:

«Voici ce que je sais de [nom du candidat]:

  • Formation: [résumé]
  • Expérience: [résumé]
  • Points forts par rapport au poste: [liste]
  • Points à explorer en entretien: [liste] »

Si aucun fichier n'existe, demande:

«Je n'ai pas encore de dossier pour ce candidat. Pouvez-vous me décrire son profil en quelques mots? (formation, expérience, ce qui a retenu votre attention dans sa candidature)»

← Reformulation

3. Préparer les questions d'entretien

Lis skills/competences/metier-rh/SKILL.md pour les bonnes pratiques (méthode STAR, questions interdites).

Propose des questions réparties en catégories:

«Voici les questions que je propose pour l'entretien:»

Compétences techniques (liées au poste):

  1. [Question adaptée à la compétence 1]
  2. [Question adaptée à la compétence 2]
  3. [Question adaptée à la compétence 3]

Compétences comportementales (méthode STAR: Situation, Tâche, Action, Résultat):

  1. «Décrivez une situation où vous avez dû [compétence comportementale]. Quel était le contexte, qu'avez-vous fait, et quel a été le résultat?»
  2. «Racontez-moi un moment où [compétence comportementale].»
  3. «Comment avez-vous géré [situation typique du poste]?»

Adéquation culturelle:

  1. «Qu'est-ce qui vous attire dans notre entreprise?»
  2. «Décrivez votre environnement de travail idéal.»
  3. «Comment décrivez-vous votre façon de collaborer en équipe?»

Motivation et projection:

  1. «Pourquoi ce poste vous intéresse-t-il?»
  2. «Où vous voyez-vous dans 2-3 ans?»
  3. «Qu'attendez-vous de ce nouveau poste?»

«Souhaitez-vous modifier, ajouter ou retirer des questions?»

Read the full file on GitHub · 192 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. 10d ago First seen · 192 lines · 12 tokens per session scan A 7ff9ac13785b

Subscribe to this mod's changes

preparer-entretien is a skill published in the GitHub repository ai-swiss/base (45 stars, last pushed 6d ago), licensed Apache-2.0. It adds 12 tokens to every session and 1,869 once invoked, about $0.0001 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 2026-08-30.

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