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概要と使いどころ
Define, validate, run, resume, and audit repo-local multi-step automations with current `asc workflow` and `.asc/workflow.json`, including step outputs and safe release/TestFlight workflows.
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。
iOS design system tokens for Pencil MCP: SF Pro typography scale, iOS semantic colors (light + dark mode hex values), spacing grid, touch targets, corner radii, and component heights. Use when initializing a .pen file for iOS design or setting up design variables.
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 3D scenes and visualizations using SceneKit. Use when creating 3D views with SCNView and SCNScene, building node hierarchies with SCNNode, applying materials and lighting, animating with SCNAction, simulating physics with SCNPhysicsBody, loading 3D models (.usdz, .scn), adding particle effects, or embedding SceneKit in SwiftUI with SceneView. Note: SceneKit was deprecated at WWDC 2025 and is in maintenance mode; RealityKit is recommended for new projects.
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。
Display and manipulate PDF documents using PDFKit. Use when embedding PDFView to show PDF files, creating or modifying PDFDocument instances, adding annotations (highlights, notes, signatures), extracting text with PDFSelection, navigating pages, generating thumbnails, filling PDF forms, or wrapping PDFView in SwiftUI.
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。
Access research-grade sensor data using SensorKit. Use when reading ambient light levels, accelerometer data, rotation rates, device usage patterns, keyboard metrics, or media events for approved research studies. Requires SensorKit entitlement and research study authorization.
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。
Applies depth-driven layered color and shadow principles to elevate average UI into polished, readable interfaces for both light and dark themes. Use when improving or reviewing UI components, setting up a design token system, applying consistent depth and shadow to an interface, or making a flat or inconsistent UI feel more refined without a full redesign.
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。
Review a feature and enhance it with purposeful animations, micro-interactions, and motion effects that improve usability and delight. Use when the user mentions adding animation, transitions, micro-interactions, motion design, hover effects, or making the UI feel more alive.
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。
日照センサーの特性、配置方法、および操作に関するガイドラインです。
避雷針の特性、配置方法、および操作に関するガイドラインです。
Use when: assumptions audit, audit assumptions, check for hidden assumptions, plan/ticket has ambiguous scope edges, pre-flight before finalizing a ticket, unstated assumptions, or 'what am I assuming'. Surfaces unstated assumptions, ambiguous scope edges, and untested preconditions in a finished plan/ticket/ADR before it is handed to Builder. Optional pass — Architect judges when to apply it; not a mandatory gate on every ticket.
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 App Intents, app entities, and App Shortcuts for iOS system surfaces. Use when exposing app actions or content to Shortcuts, Siri, Spotlight, widgets, or controls.
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。
Master REST and GraphQL API design principles to build intuitive, scalable, and maintainable APIs that delight developers and stand the test of time.
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。
Lightweight contract lifecycle management App-in-Skill (Busabase App-in-Skill) for contract inventory, lifecycle status, owners, obligations, renewal notices, and simple approval reminders. Use when the user invokes $kelly-clm or /kelly-clm, mentions CLM, contract lifecycle management, 合同管理, 合同台账, contract repository, obligation tracking, renewal reminders, notice deadlines, contract owners, signature readiness, or wants a simple UI to review contract status without doing detailed legal redlines.
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。
Run a Busabase-backed sandbox desk for scoring and triaging course-instructor ("讲师") candidates found on a recruiting-style platform. Use whenever the user invokes $kelly-instructor-sourcing or /kelly-instructor-sourcing, wants a lightweight instructor candidate tracker, needs to score sourcing-platform leads against a three-axis rubric (background endorsement, expertise depth/breadth, teaching-service ability) before reaching out, or wants to log a WeChat add without building a live-connected outreach tool. Three subcommands - setup, criteria, review - plus the desk itself. v1 is sandbox-only — every dataset is deterministic mock data, it never connects to a real sourcing-platform or messaging account, and it never sends a message or automates a WeChat add - that stays entirely human-performed outside the app.
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。
Busabase-backed App-in-Skill for one thing only — record who you need to follow up with after a meeting, see what is due today, mark it done. Use when the user invokes $kelly-followups or /kelly-followups, mentions a followup, follow-up, meeting action item, or wants a lightweight daily reminder of who to check in with, without a full project-management tool.
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。
MUST USE when the user provides a photo ZIP, photo folder, or still-image event material and asks for photo selection, album selection, highlights, digest photos, curation, or quality filtering. Use this instead of analyze-footage when the source is primarily still photos and the requested work is selecting images rather than editing or video planning.
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。
Toggle between light and dark mode in Cursor's browser, screenshot both states, and flag missing token mappings or contrast issues.
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。
Language-independent tokenizer treating text as raw Unicode. Supports BPE and Unigram algorithms. Fast (50k sentences/sec), lightweight (6MB memory), deterministic vocabulary. Used by T5, ALBERT, XLNet, mBART. Train on raw text without pre-tokenization. Use when you need multilingual support, CJK languages, or reproducible tokenization.
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。
Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias or fairness, comparing models, or implementing explainable AI. Works with tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box 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。
Metabolomics research — metabolite identification, study analysis, and database searches across HMDB, MetaboLights, Metabolomics Workbench, KEGG. Use for annotating mass-spec features to known metabolites, finding metabolomics studies of a disease, and structured metabolomics research reports with metabolite-pathway 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。
Publication-quality molecular visualization. 2D structure drawings (PNG/SVG), molecule grids with property annotations, scaffold highlighting, protein-ligand interaction diagrams, and interactive 3D views.
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 workflow — create design systems, DESIGN.md, and design tokens with anti-pattern enforcement and accessibility checks
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。
Ultrawork - high-quality 5-phase development workflow with 11 review steps out of 17
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。
Configure development tasks, git hooks, CI/CD, or release automation. Use when creating or fixing a repository development 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。