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概要と使いどころ
Select the right formative assessment technique for a specific learning moment, purpose, and age group. Use when choosing how to check understanding during, between, or after lessons.
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。
Classify curriculum content into Know, Understand, and Do categories to align teaching and assessment approaches. Use when planning units, writing objectives, or selecting assessment methods.
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。
Agentic eyes for Godot work: cross-platform screenshot CLI (asset/region/window/screen) to budgeted WebP, TEMP editor viewport bridge under .gdskills/, and Taste Receptor Atlas v2 (~237 micro-receptors × 0–2: hierarchy, contrast, typography×41, affordance, space, color, icon, composition, light/FX, motion-tells, anti-slop, originality, identity). Use when agents must see UI, assets, or the editor — not for in-game screenshot Autoloads. Keywords: agent vision, screenshot, WebP, visual QA, taste receptors, typography, UI review, asset sheet, editor viewport, anti-slop.
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。
Bevy ECS skill for assets and scene spawning. Keywords: Bevy, AssetServer, Handle, Scene, bsn, spawn_scene.
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。
Comprehensive App Store asset specifications and guidelines for all promotional artwork — app icons, screenshots, app previews, event cards, subscription images, and featuring artwork. Use when preparing assets for App Store Connect upload.
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。
Universal LaTeX document skill: create, compile, and convert any document to professional PDF with PNG previews. Supports resumes, reports, cover letters, invoices, academic papers, theses/dissertations, academic CVs, presentations (Beamer), scientific posters, formal letters, exams/quizzes, books, cheat sheets, reference cards, exam formula sheets, fillable PDF forms (hyperref form fields), conditional content (etoolbox toggles), mail merge from CSV/JSON (Jinja2 templates), version diffing (latexdiff), charts (pgfplots + matplotlib), tables (booktabs + CSV import), images (TikZ), Mermaid diagrams, AI-generated images, watermarks, landscape pages, bibliography/citations (BibTeX/biblatex), multi-language/CJK (auto XeLaTeX), algorithms/pseudocode, colored boxes (tcolorbox), SI units (siunitx), Pandoc format conversion (Markdown/DOCX/HTML ↔ LaTeX), and PDF-to-LaTeX conversion of handwritten or printed documents (math, business, legal, general). Compile script supports pdflatex, xelatex, lualatex with auto-detection, latexmk backend, texfot log filtering, PDF/A output, and verbosity control (--verbose/--quiet). Empirically optimized scaling: single agent 1-10 pages, split 11-20, batch-7 pipeline 21+. Use when user asks to: (1) create a resume/CV/cover letter, (2) write a LaTeX document, (3) create PDF with tables/charts/images, (4) compile a .tex file, (5) make a report/invoice/presentation, (6) anything involving LaTeX or pdflatex, (7) convert/OCR a PDF to LaTeX, (8) convert handwritten notes, (9) create charts/graphs/diagrams, (10) create slides, (11) write a thesis or dissertation, (12) create an academic CV, (13) create a poster, (14) create an exam/quiz, (15) create a book, (16) convert between document formats (Markdown, DOCX, HTML to/from LaTeX), (17) generate Mermaid diagrams for LaTeX, (18) create a formal business letter, (19) create a cheat sheet or reference card, (20) create an exam formula sheet or crib sheet, (21) condense lecture notes/PDFs into a cheat sheet, (22) create a fillable PDF form with text fields/checkboxes/dropdowns, (23) create a document with conditional content/toggles (show/hide sections), (24) generate batch/mail-merge documents from CSV/JSON data, (25) create a version diff PDF (latexdiff) highlighting changes between documents, (26) create a homework or assignment submission with problems and solutions, (27) create a lab report with data tables, graphs, and error analysis, (28) encrypt or password-protect a PDF, (29) merge multiple PDFs into one, (30) optimize/compress a PDF for web or email, (31) lint or check a LaTeX document for common issues, (32) count words in a LaTeX document, (33) analyze document statistics (figures, tables, citations), (34) fetch BibTeX from a DOI, (35) convert a Graphviz .dot file to PDF/PNG, (36) convert a PlantUML .puml file to PDF/PNG, (37) create a one-pager/fact sheet/executive summary, (38) create a datasheet or product specification sheet, (39) extract pages from a PDF (page ranges, odd/even), (40) check LaTeX package availability before compiling, (41) analyze citations and cross-reference with .bib files, (42) debug LaTeX compilation errors, (43) make a document accessible (PDF/A, tagged PDF), (44) create lecture notes or course handouts, (45) fill an existing PDF form (fillable fields or non-fillable with annotations), (46) extract text or tables from a PDF (pdfplumber, pypdf), (47) OCR a scanned PDF to text (pytesseract), (48) create a PDF programmatically with reportlab (Canvas, Platypus), (49) rotate or crop PDF pages (pypdf), (50) add a watermark to an existing PDF, (51) extract metadata from a PDF (title, author, subject).
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。
Expert knowledge for Azure Kubernetes Service Edge Essentials development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when managing AKS Edge/Hybrid with Arc, GitOps, IoT/OPC/ONVIF, TPM/Key Vault secrets, or AI model workloads, and other Azure Kubernetes Service Edge Essentials related development tasks. Not for Azure Kubernetes Service (AKS) (use azure-kubernetes-service), Azure IoT Edge (use azure-iot-edge), Azure Stack Edge (use azure-stack-edge), Azure Container Apps (use azure-container-apps).
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。
Write high-quality JUnit 5 tests with AssertJ assertions. Use when user says "add tests", "write tests", "improve test coverage", or when reviewing/creating test classes for Java code.
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 Glasser to retrieve live marketing data through one prepaid account when a task needs SERPs, keyword metrics, ad libraries, community posts, company data, or another external API and no suitable free source or existing integration is available. Prefer the user's own integrations; Glasser fills data gaps and every run has a disclosed cost.
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 one master design has to become every size a campaign needs — feed, story, reel, cover, and ad placements: the standard size matrix, three strategies of scale-and-crop, re-compose, or rebuild, safe-zone rules, platform crop behavior, and file naming. Trigger on 'resize this design', 'all the sizes', 'adapt to other placements', 'safe zone', 'standard size chart', 'the text gets cut off in stories'. Also use when one visual exists and six placements are waiting on it. Not for — the original concept brief, see `42-image-brief-global`; production steps in Canva or Figma, see `45-design-tool-brief-global`; the full campaign asset list, see `41-campaign-asset-list-global`; an urgent new creative, see `48-quick-visual-brief-global`.
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。
Write idiomatic Kotlin 1.9+ with null safety, sealed classes, data classes, extension functions, delegates, collections, inline/reified generics, and expression syntax. Use for Kotlin language constructs, Java-to-Kotlin migration, lazy delegates, data classes, backing properties, or inline/reified generics; keep language constructs in scope even inside response models, and defer coroutine configuration, Android Context/framework, Retrofit/OkHttp client setup, and other library-specific recipes.
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 existing tests in any language using formal, research-backed test smell names and the testsmells.org 19-smell academic taxonomy. Use when the caller asks for an academic or citable test-smell review, named smell categories, or a formal severity-ranked smell assessment. Covers Assertion Roulette, Conditional Test Logic, Mystery Guest, Eager Test, Sleepy Test, Unknown Test, Sensitive Equality, and the rest of the catalog across .NET, Python, JavaScript/TypeScript, Java, Go, Ruby, Rust, Swift, Kotlin, PowerShell, and C++. DO NOT USE FOR a quick pragmatic test review (use test-anti-patterns), writing or running tests, framework migration, coverage, or assertion-diversity 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。
Compresses and uncompresses file objects stored in Volcengine TOS through TOS data-process async jobs. Use this skill when the user needs to zip files, unzip archives, create tar or zstd archives, extract compressed files, pack multiple TOS objects into a single archive, bundle files together, or distribute files as a package — even if they don't explicitly mention 'compression', 'archive', '打包', or '压缩'.
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。
Assess project-wide structural technical debt: complexity hotspots, deprecated API usage, duplication clusters, architecture rot, and low-value tests. Ranks findings by impact and refactor effort into a report at .turbo/technical-debt.md. Use when the user asks to "assess technical debt", "find technical debt", "review technical debt", "what should we refactor", "find refactoring candidates", "where is the code rot", "what's our worst code", "find low-value tests", or "which tests can we delete". Analysis-only — does not modify code.
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。
Provides patterns for testing utility classes, static methods, and helper functions. Validates pure functions, null handling, edge cases, and boundary conditions. Generates AssertJ assertions and @ParameterizedTest for string utils, math utils, validators, and collection helpers. Use when testing utils, test helpers, helper functions, static methods, or verifying utility code correctness.
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。
Evaluates which SaaS tools can be replaced with AI agents. Takes a list of current SaaS subscriptions with costs, assesses replacement feasibility, estimates build vs buy economics, identifies Claude+MCP alternatives, and generates a comprehensive replacement plan with priority matrix, ROI analysis, implementation timeline, and risk assessment.
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。
Assesses how ready a business is for AI adoption across six dimensions. Evaluates data maturity, tech stack, team skills, process documentation, budget, and culture. Generates a comprehensive ai-readiness-report.md with scores, gap analysis, and recommended starting points. Aligned with OneWave AI's audit methodology.
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。
Asset and IT register: serial, model, condition, assignee, location, purchase value, warranty expiry and return date, as CSV, SQL, JSON Schema or Notion on request. Use for asset audits.
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。
Maintain IT asset inventory and configuration management database. Track hardware, software, and cloud resources. Use when managing IT assets.
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。
Guides privacy law change monitoring and impact assessment for multi-jurisdiction organisations. Covers regulatory tracking sources, change classification, impact scoring methodology, and implementation prioritisation. Keywords: law monitoring, regulatory tracking, change management, impact assessment, implementation priority.
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。
Links RoPA entries to Data Protection Impact Assessments and lawful basis assessments. Covers cross-reference systems, dependency tracking, and update cascade triggers between RoPA, DPIA register, and lawful basis documentation. Activate for RoPA-DPIA link, cross-reference, dependency tracking, impact assessment linkage, cascade updates.
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。
Guides conducting privacy law gap analysis for market entry into new jurisdictions. Covers target jurisdiction assessment, existing compliance mapping, remediation effort estimation, and implementation timeline planning. Keywords: gap analysis, market entry, jurisdiction assessment, remediation planning, compliance mapping.
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。
Conducts HIPAA risk analysis per 45 CFR §164.308(a)(1) following OCR guidance methodology. Covers threat identification, vulnerability assessment, likelihood and impact determination, risk scoring, and mitigation planning for electronic protected health information. Keywords: HIPAA risk analysis, OCR guidance, threat assessment, vulnerability, risk management, ePHI.
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。
Assesses children's data protection in educational technology. Covers COPPA school exception under Section 312.5(c)(4), FERPA intersection, parental rights, teacher consent authority, data deletion at year-end, and Student Privacy Pledge compliance. Keywords: edtech, COPPA school exception, FERPA, student privacy, teacher consent, educational data.
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。