Agent skill · Data & Analytics

MCQ Generator

AI-assisted generation of MCQs (multiple-choice questionnaires) with export to Moodle (GIFT, XML), Wooclap (Excel), Kahoot!, and Word. Supports light MCQs (gamified) and in-depth MCQs (evaluative), with single-answer and multiple-answer formats.

lawve-aigithub.com/lawve-aiGitHub ↗
claude-codeNOASSERTION
Install
npx skills add lawve-ai/awesome-legal-skills --skill qcm-generateur-christophe-quezel-ambrunaz --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 27
SKILL.md size: 7 KB
Bundled scripts: none
Version: 2026-04-10
Declared author: Christophe Quézel-Ambrunaz
Path: skills/qcm-generateur-christophe-quezel-ambrunaz/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 618
Language: Python

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# QCM Generator - Version 3.0 ## Vue d'ensemble Cette skill guide la création de QCM de qualité académique avec recherche documentaire systématique, validation factuelle rigoureuse, construction méthodique des distracteurs, et génération de fichiers prêts pour divers usages pédagogiques. **Nouveautés V.3** : - Dichotomie QCM léger (ludique, sans feedbacks) / QCM approfondi (évaluatif, avec feedbacks) - Randomisation de la position des bonnes réponses (visible dès la prévisualisation) - Échelle de niveau adaptée : Primaire, Collège, Lycée, Licence, Master - Export Kahoot! (.xlsx) pour QCM légers - Export Word (.docx) avec feuille questions + grille correction + corrigé détaillé - Précision : XML Moodle importable directement dans Wooclap **Héritées de V.2** : - Système de pénalités configurable pour réponses incorrectes - Méthodologie formalisée de construction des distracteurs (5/6 conceptuels, 1/6 linguistiques) - Progression de difficulté croissante obligatoire - Protocole de versionnement pour modifications itératives - Guidelines détaillées pour feedbacks pédagogiques de qualité ## Workflow en 5 phases ### Phase 1 : Collecte des informations **PREMIÈRE QUESTION PRIORITAIRE** :

What's inside
Steps it walks through
  1. Vue d'ensemble
  2. Workflow en 5 phases
  3. Phase 1 : Collecte des informations
  4. Phase 2 : Recherche documentaire
  5. Phase 3 : Génération du document de prévisualisation
  6. Phase 4 : Révision et validation
  7. Phase 5 : Génération des fichiers
  8. Limitations
Ships with 24 files
  • README.md
  • references/collect_information_initial.md
  • references/collect_information_missing.md
  • references/constraints_light_mcq.md
  • references/difficulty_progression.md
  • references/distractor_methodology.md
  • references/feedback_guidelines.md
  • references/format_gift.md
  • references/format_kahoot.md
  • references/format_wooclap.md
  • references/format_word.md
  • references/format_xml_moodle.md
  • references/output_format_choice.md
  • references/preview_introduction.md
  • references/refuse_problematic_content.md
  • references/research_validation.md
  • references/revision_warning.md
  • references/unsupported_question_type.md
  • references/usage_gift.md
  • references/usage_kahoot.md
  • references/usage_paper.md
  • references/usage_type_choice.md
  • references/usage_wooclap.md
  • references/usage_word.md
first 24 of 27
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About this skill
What does the MCQ Generator skill do?

AI-assisted generation of MCQs (multiple-choice questionnaires) with export to Moodle (GIFT, XML), Wooclap (Excel), Kahoot!, and Word. Supports light MCQs (gamified) and in-depth MCQs (evaluative), with single-answer and multiple-answer formats.

How do I install it?

Run `npx skills add lawve-ai/awesome-legal-skills --skill qcm-generateur-christophe-quezel-ambrunaz --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From lawve-ai/awesome-legal-skills, a repository with 618 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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