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.
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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 :
Héritées de V.2 :
PREMIÈRE QUESTION PRIORITAIRE : Lire references/usage_type_choice.md et demander à l'utilisateur de choisir entre :
Ce choix conditionne toute la suite du processus.
Puis vérifier systématiquement que l'utilisateur a fourni :
Toujours demander si l'utilisateur souhaite charger des documents de référence.
Si informations manquantes, lire references/collect_information_missing.md et demander les informations.
Principe absolu : Exactitude factuelle prime sur tout.
Recherche web OBLIGATOIRE pour :
Sources : Privilégier sources académiques, institutionnelles, gouvernementales.
Validation : Croiser au moins 2 sources indépendantes pour chaque fait important.
LECTURE CONDITIONNELLE selon type de QCM :
Si QCM LÉGER :
references/constraints_light_mcq.md (contraintes 95/60 caractères)Si QCM APPROFONDI :
references/feedback_guidelines.mdDANS TOUS LES CAS, lire obligatoirement :
references/distractor_methodology.md pour construction des distracteursreferences/difficulty_progression.md pour organisation des questionsCréer un document Markdown structuré avec pour chaque question :
Principes de rédaction :
RANDOMISATION OBLIGATOIRE :
Structure par question :
AVERTISSEMENT OBLIGATOIRE : Lire references/revision_warning.md et rappeler que :
Si l'utilisateur demande des modifications :
references/versioning_protocol.mdPuis proposer choix du format en lisant references/output_format_choice.md
Selon le format choisi et le type de QCM, lire les spécifications dans references/ :
Formats disponibles pour QCM LÉGER :
references/format_kahoot.md - Format natif optimiséreferences/format_wooclap.mdreferences/format_gift.md (sans feedbacks)references/format_xml_moodle.md (sans feedbacks)Formats disponibles pour QCM APPROFONDI :
references/format_gift.md (avec feedbacks)references/format_xml_moodle.md (avec feedbacks)
references/format_wooclap.md (feedbacks à ajouter manuellement après import)references/format_word.md (feuille questions + grille correction + corrigé détaillé)Puis fournir instructions d'utilisation : references/usage_[format].md
Note sur Wooclap :
Types NON supportés : Questions ouvertes, réponse courte, appariement, numériques pures, texte à trous, classement.
Si demandé : references/unsupported_question_type.md
Contenu problématique : Refus catégorique si factuellement faux, discriminatoire, biaisé, ou nuisible.
Si détecté : references/refuse_problematic_content.md
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Public-safe preliminary screen for challenges to the impartiality of an AI evaluator, LLM-as-a-judge, decision-support agent, or adjudicative system based on prior outputs, suspected bias, unstable reasoning, or self-reinforcing memory. Use when asked whether an AI should be recused, disqualified, replaced, audited, or escalated. Does not decide real cases or claim that human judicial-recusal doctrine automatically applies.
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Creates an Agent Authority Charter for enterprise or regulated AI agents before deployment. Use this Skill when a user needs to define what an AI agent is allowed to do, who delegated authority to it, what actions are permitted or prohibited, when human approval is required, what evidence must be preserved, and how the agent can be suspended, revoked, or escalated.
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EU AI Act Knowledge Engine — authoritative regulatory Q&A grounded in 70 official EU source documents (including the 2026 Commission draft guidelines on Art. 6 high-risk classification — general principles, Annex I, Annex III). Answers any question about the AI Act with article-level citations from the full regulation text, Commission guidelines, EDPB/EDPS opinions, codes of practice, harmonised standards, FRIA guides, incident templates, and sector-specific guidance. This skill should be used when the user asks about the "EU AI Act", "AI Act regulation", "Regulation 2024/1689", "KI-Verordnung", asks to "explain an AI Act article", "what does Article X say", "AI Act requirements", "AI Act penalties", "AI Act timeline", "GPAI obligations", "high-risk AI", "prohibited AI practices", "AI Act and GDPR", "fundamental rights impact assessment", "AI Act standards", "AI Act national implementation", "AI literacy", "serious incident reporting", "Digital Omnibus", "AI regulatory sandbox", or any question about EU artificial intelligence regulation. Also triggers on German terms: "KI-Gesetz", "KI-Verordnung", "Hochrisiko-KI", "KI-Kompetenz", "Grundrechte-Folgenabschaetzung", "GPAI-Verhaltenskodex". For assessment workflows that produce a classification decision rather than a knowledge answer, offer to run a structured risk-tier classification in-session.
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This skill builds a structured audit trail of an AI-assisted task: what the tool was asked to do, what materials it was given, what it produced, how the output was verified, and what was ultimately relied upon. It documents the workflow for supervision and later review without ruling on privilege or disclosure, so the record stays accurate rather than self-serving.
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Use this skill when the user wants an AI governance, legal-risk, privacy, compliance, procurement, or vendor-risk review of an internal AI use case, an AI product feature, an LLM workflow, or a third-party AI vendor. The skill asks intake and clarifying questions first when facts or evidence are missing, identifies required documentation and missing evidence, maps the use case to AI governance frameworks and applicable legal domains, and produces a preliminary or final governance review with scorecards, findings, owners, remediation actions, and follow-up questions.
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Turn an interpretive-ambiguity audit of a legal text — contract, statute, regulation, or judicial opinion — into a polished deliverable. Produces a multi-page website (default), a single-page interactive site, a Microsoft Word document, a PowerPoint deck, or a LaTeX/PDF. Use whenever a user has a stress-test result, ambiguity audit, or "where will this be litigated" scenarios and wants to publish, present, or share them. Triggers on "publish the audit," "make a report from this," "turn this into a website / deck / brief / Word doc," "make a Netlify site," "build slides of these ambiguities," "render the scenarios," "package this stress test," "produce a LaTeX version," and similar. Pairs with the ambiguity-stress-test skill (the underlying analysis) but works on any structured or semi-structured ambiguity analysis. Visual design is opinionated but the user can override colors, fonts, and brand label. Most formats need Python and a writable filesystem; single-file HTML works where the host has neither.
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