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Present project-design pathway options and orchestrate regenerative projects using backwards design, PBL, SEEDS, compassionate systems action, or civic/service pathways.
日本語の概要は準備中です。原文の説明を表示しています。
Structure a decision or design challenge through multiple perspectives before committing to action. Use as a synthesis step after scoping, mapping, and dilemma navigation when a group needs a wiser next step.
日本語の概要は準備中です。原文の説明を表示しています。
Map a scoped topic across three coexisting horizons — current system under strain (H1), preferred future (H3), and transition innovations sorted as H2+ or H2- — to understand change dynamics and identify responsible next actions.
日本語の概要は準備中です。原文の説明を表示しています。
Design a Concrete-Pictorial-Abstract learning sequence for a mathematical concept using manipulatives. Use when teaching maths through Singapore method or when students struggle with abstraction.
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Audits an existing lesson against UDL's three principles — engagement, representation, and action/expression. Identifies specific access barriers and suggests concrete modifications ranked by impact.
日本語の概要は準備中です。原文の説明を表示しています。
Orchestrate compassionate systems tools from issue or aspiration to wise action. Use when a class needs a complete inquiry workflow.
日本語の概要は準備中です。原文の説明を表示しています。
Generates multiple means of engagement, representation, and action/expression for a given learning goal. Produces specific, practical alternatives — not generic options — and recommends the highest-impact single change.
日本語の概要は準備中です。原文の説明を表示しています。
Analyse existing written feedback for quality, specificity, actionability, and impact on student learning. Use when reviewing teacher or peer feedback to improve feedback practices.
日本語の概要は準備中です。原文の説明を表示しています。
Interpret learning analytics data and translate dashboard findings into actionable teaching decisions. Use when reviewing LMS data, quiz patterns, or engagement metrics.
日本語の概要は準備中です。原文の説明を表示しています。
Design AI-supported collaborative tasks that structure group interaction and address participation problems. Use when students struggle to collaborate effectively on group tasks.
日本語の概要は準備中です。原文の説明を表示しています。
Design a service-learning project connecting genuine community need with embedded curriculum learning. Use when planning community projects, civic engagement, or social action units.
日本語の概要は準備中です。原文の説明を表示しています。
Script a multi-turn tutoring dialogue with branching responses for anticipated student difficulties. Use when designing AI tutors, chatbot interactions, or structured one-to-one support scripts.
日本語の概要は準備中です。原文の説明を表示しています。
Use when the user has run multiple evaluation skills and wants a comprehensive analysis — maturity assessment, cross-skill signals, trends, prioritized actions, and an executive summary. Also use when the user mentions eval health check, evaluation audit, ship readiness, evaluation maturity, or "how good is my evaluation system itself." This is a read-only analysis skill.
日本語の概要は準備中です。原文の説明を表示しています。
Use when the user wants to test their LLM/agent application for safety and security vulnerabilities — jailbreaks, prompt injection, PII extraction, harmful content generation, or evaluator gaming. Also use when the user mentions security testing, adversarial testing, red teaming, safety evaluation, ASR (Attack Success Rate), or "is my app safe to deploy." Outputs ASR paired with over-refusal rate and an audit document.
日本語の概要は準備中です。原文の説明を表示しています。
Edit, query, and transform Word documents (.docx) with the SuperDoc CLI. Use when the user asks to read, search, modify, format, comment, track changes, or review changes in .docx files. Triggers on any task involving Word documents — text replacement, redlining, contract markup, template filling, bulk edits, content extraction, or document review.
日本語の概要は準備中です。原文の説明を表示しています。
Pick the right LLM for CONTRACT REVIEW — reviewing an existing agreement for risk, deviations from standard, missing protections, and internal contradictions, and proposing redlines. Vendor-neutral routing grounded in mid-2026 legal benchmarks. Contract review is a COMPOSITE skill (extraction + reasoning + redlining + conflict-detection), so this routes by the dominant sub-task. Asks up to 4 quick questions (cost, speed, accuracy/stakes, privacy/jurisdiction/ language), then recommends a primary model + fallback + what to avoid + what a human must verify. Use when someone asks "which model to review this contract / NDA / MSA", "best AI to redline", "route this contract review", or is about to review an agreement without a fixed model.
日本語の概要は準備中です。原文の説明を表示しています。
Rédaction de requêtes devant le Conseil de prud'hommes pour contester un licenciement pour faute grave. Utiliser ce skill quand l'utilisateur demande de rédiger une requête CPH, contester un licenciement pour faute grave, préparer une saisine du CPH pour un litige lié à un licenciement, ou créer un document de contestation de licenciement en droit du travail français.
日本語の概要は準備中です。原文の説明を表示しています。
Determines whether a specific AI-agent action, output, recommendation, or proposed commitment remains admissible for execution or institutional reliance under current authority, delegated scope, evidence, facts, policy, risk, escalation, and revocation conditions. Use this Skill before an enterprise or regulated AI agent executes, updates records, triggers workflows, communicates externally, or before an institution relies on an agentic output in a way that creates consequence.
日本語の概要は準備中です。原文の説明を表示しています。
RAID log methodology with decision extraction from emails and meeting notes. Use when asked to identify risks, log assumptions, track issues, extract decisions from correspondence, create or update a RAID log, escalate a risk to an issue, assess project risks, validate or challenge assumptions, or capture scope-relevant decisions. Also triggers when the user pastes email chains and asks what decisions were made, or needs to find buried decisions and untested assumptions in correspondence. Trigger on: 'risk report', 'RAID log', 'what are the risks', 'what decisions were made', 'update the risk register', 'what assumptions are we making', 'what could go wrong', 'flag any issues', 'extract decisions from these emails'.
日本語の概要は準備中です。原文の説明を表示しています。
Builds compliance checklists for business scenarios involving GDPR, PIPL, or advertising/data laws. Outputs a structured checklist with check items, legal basis, risk levels, and actionable recommendations. Triggered by requests like "run a compliance check," "GDPR/PIPL compliance," "pre-launch review," "privacy impact assessment (PIA/DPIA)," or asking if a feature is compliant.
日本語の概要は準備中です。原文の説明を表示しています。
First-pass framework for reading, interpreting, and structuring statutory analysis of US federal, state, and local law. Produces draft analysis for attorney review — not legal advice. Use this skill whenever the user references a specific US statute, regulation, ordinance, or rule by citation, asks "what does [statute X] require," asks for compliance scoping, applicability thresholds, requirement extraction, exemption analysis, definitional analysis, federal preemption analysis, or multi-state comparison — even if they don't explicitly ask for "statutory analysis." Halts and asks for missing inputs rather than guessing. Out of scope for non-US law.
日本語の概要は準備中です。原文の説明を表示しています。
Screen a client, counterparty, payer or corporate against the sanctions lists published by the designating authorities themselves — the UK Sanctions List (FCDO), OFSI, the UN Security Council, the EU consolidated list and OFAC's SDN and non-SDN lists. Use when onboarding, before taking a payment, before completing a transaction, or whenever a name must be checked against sanctions. Produces candidates and provenance, never a clearance: the record names every list searched with its publication date and count, states what was NOT searched, and ends at a blank decision block. Fails closed — a cache that is missing, damaged, truncated or stale is reported NOT SEARCHED and the process exits non-zero, so nothing can read an incomplete run as clean. Matches across transliteration, diacritics, homoglyphs and initials, because nobody spells a name the way the publisher does. Standard library only; screening opens no network connection.
日本語の概要は準備中です。原文の説明を表示しています。
Apply the NIST AI Risk Management Framework (NIST AI 100-1 + the NIST AI 600-1 Generative AI Profile) to a specific AI system, governance question, or impact assessment. Three modes — consult, governance plan, full assessment — all cite Subcategories (`GOVERN 1.1`) and Profile Action IDs (`GV-1.2-001`) verbatim. Use when the user mentions the AI RMF, NIST RMF, NIST AI 100-1, NIST AI 600-1, GenAI Profile, the four functions (Govern / Map / Measure / Manage), the trustworthy AI characteristics, the 12 GAI risks (confabulation, harmful bias, information integrity, CBRN, data privacy, etc.), or asks "what does NIST say about X" for an AI system.
日本語の概要は準備中です。原文の説明を表示しています。
Edit mathematically, empirically, or technically dense papers for readers outside the author’s specialty, including empirical legal scholarship, law-and-economics models, and legal-technology research. Clarifies terminology, antecedents, study design, and claim–statistic relationships while checking equations, numbers, quotations, and citations against the source. Use to clarify exposition or de-jargonize a technical draft; not to rewrite briefs or doctrinal arguments. Produces an edited draft and audit memo. Full integrity checks and original-format rendering require suitable extraction, code execution, and document tools; otherwise provides proposed edits with verification limits disclosed.
日本語の概要は準備中です。原文の説明を表示しています。