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概要と使いどころ
Assessing standards, regulatory controls, and legal-document coverage with cited evidence and proposed wording. Use for OWASP/WCAG/SOC2/PCI/HIPAA or ToS/privacy/DPA reviews; not legal advice or code fixes.
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。
Designing and evaluating AI/ML systems: prompt engineering, RAG design, LLM application patterns, AI safety, evaluation frameworks, MLOps, cost optimization. Use for AI pipelines or eval harnesses.
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。
Shift-left safety net for dbt/SQL model edits. Runs change impact assessment before edits, generates SQL validation queries after, and executes them via `/mc-validate run`. Delegates health and monitor creation to peer skills.
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。
Rapid codebase assessment — lightweight alternative to /gsd:map-codebase
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。
Create PR, run review, and prepare for merge after verification passes
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, preview, implement, and assess mini-program interfaces against an approved prototype, screenshot, visual reference, design system, or bounded UI request while preserving product semantics and existing accepted behavior. Use when users ask to reproduce a mini-program screen, refine visual hierarchy, adapt layouts across device sizes, handle safe areas or keyboards, inspect touch targets, scrolling, media, or gesture conflicts, compare an implementation with a reference, or prepare a device-verification matrix. Separates proposed visual previews, user approval, source integration, local checks, and real-device evidence; never invents product functionality for visual completeness or claims device validation without device-specific proof.
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。
Verify mini-program implementations and fixes with risk-calibrated evidence across static checks, unit tests, integration tests, state matrices, simulators, real devices, cloud environments, and release artifacts. Use when users ask to test, validate, accept, regression-check, quality-check, confirm readiness, or determine whether a mini-program feature is actually complete. Binds results to a source and build fingerprint, records commands and observable evidence, separates passed, failed, blocked, and not-run layers, prioritizes the next highest-information check, and never converts local success into device, cloud, release, or formal acceptance claims.
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。
Implement bounded features and fixes in existing or new mini-program codebases while preserving repository rules, user-owned changes, approved behavior, assets, framework conventions, and evidence boundaries. Use when users ask to write or modify mini-program code, implement a confirmed specification or architecture, add a scoped feature, remove behavior semantically, update internal documentation required by a change, or carry out a well-defined small fix that does not require root-cause investigation. Establishes a baseline and change boundary, applies test-driven small steps, distinguishes source from generators and build artifacts, verifies the affected contract, and never reports source completion as device validation, formal acceptance, upload, or release.
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。
Audits and optimizes websites for search engine visibility (SEO) and AI search citation (GEO), covering technical health, E-E-A-T content scoring, domain authority, structured data, rich results, and entity signals. Use when running SEO audits, diagnosing traffic drops or ranking losses, generating Schema.org JSON-LD, checking Core Web Vitals, crawlability, robots.txt, sitemaps, hreflang, backlinks, planning content strategy or site migrations, fixing indexing issues, or optimizing for AI Overviews, ChatGPT, and Perplexity. NOT for paid ads (PPC/SEM), social media strategy, email marketing, or general web development unrelated to search.
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 this when reading, writing, moving, copying, modifying, deleting, or generating data files — including any operation that touches data/raw, data/external, data/interim, data/processed, or outputs directories.
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 worked example fading sequence from fully worked examples through to independent practice. Use when teaching procedures, algorithms, or multi-step processes to novice learners.
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 text complexity across quantitative, qualitative, and reader-task dimensions with scaffolding recommendations. Use when selecting texts, assessing readability, or planning reading support.
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。
Generate retrieval practice questions at varied difficulty levels for a topic or concept. Use when creating quiz starters, revision activities, or low-stakes testing materials.
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。
Redesign a classroom as a prepared environment optimised for independent learning, calm transitions, and material access. Use when classroom layout hinders independence or self-directed work.
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。
Modify a lesson plan using SIOP sheltered instruction principles to support language learners across all four skills. Use when planning lessons for classes that include EAL students.
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 diagnostic hinge question that reveals whether students understand enough to move on. Use when planning key checkpoints mid-lesson during explicit or direct instruction.
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。
Design an error analysis protocol to diagnose the root cause of student mistakes and misconceptions. Use when error patterns appear in student work and targeted feedback is needed.
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。
Authors or reviews Know/Understand/Do charts for competency-based learning targets across developmental bands. Handles seven input types from raw curriculum documents to existing LT sets. Routes to upstream skills when stronger inputs are available.
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 student work against criteria to identify specific gaps between current performance and learning objectives. Use when reviewing submissions, planning feedback, or diagnosing learning needs.
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 document set for a document-based lesson — selecting and sequencing sources for analytical tension around a central question. Use when assembling sources for a new lesson or when an existing set produces flat responses.
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 the language demands of a classroom task to identify barriers for EAL and multilingual learners. Use when adapting tasks, planning support, or assessing linguistic accessibility.
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。
Build a five-level rubric with coherent logic for a learning target within a developmental band. Use for Manning methodology programmes where Competent = success. For general curriculum rubrics, use criterion-referenced-rubric-generator instead.
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。