485 件 ・ 関連度順
概要と使いどころ
Deep dive into SwiftData design patterns and best practices. Covers schema design, query patterns, repository pattern, and performance optimization. Use when designing data models or improving SwiftData usage.
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 technical architecture specification from PRD. Covers architecture pattern, tech stack, data models, and app structure. Use when creating ARCHITECTURE.md or designing system architecture.
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。
Guide to macOS 26 Tahoe APIs and features. Covers Apple Intelligence (FoundationModels), MLX framework, and Continuity. Use when implementing macOS 26 specific features.
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。
Core ML, Create ML, Vision framework, Natural Language framework, on-device ML integration. Use when user wants image classification, text analysis, object detection, sound classification, model optimization, or custom model integration. Covers Core ML vs Foundation Models decision.
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。
Security category index for Apple platforms. Routes to the privacy-manifests skill. General secure-storage/biometrics/ATS guidance was retired in the 2026 blind-test audit — current models cover it natively.
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。
Apple Intelligence skills for on-device AI features including Foundation Models, Visual Intelligence, App Intents, and intelligent assistants. Use when implementing AI-powered features.
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。
SwiftData skills — class inheritance patterns for hierarchical models, type-based querying, polymorphic relationships, and inheritance-vs-enum decisions. Use when designing SwiftData model hierarchies.
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。
Train and fine-tune transformer language models using TRL (Transformers Reinforcement Learning). Supports SFT, DPO, GRPO, KTO, RLOO and Reward Model training via CLI commands.
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 Hugging Face models in JavaScript or TypeScript with Transformers.js, WebGPU, or WASM across browser, Node.js, Bun, and Deno. Use for client-side or JS-runtime inference, not Python training.
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 or select a SageMaker execution role before creating models, endpoints, or training jobs. Use when a role ARN is missing or IAM access fails; inspect existing roles before proposing role creation.
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。
Look up and read Hugging Face paper pages in markdown, and use the papers API for structured metadata such as authors, linked models/datasets/spaces, Github repo and project page. Use when the user shares a Hugging Face paper page URL, an arXiv URL or ID, or asks to summarize, explain, or analyze an AI research paper.
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 evaluations for Hugging Face Hub models using inspect-ai and lighteval on local hardware. Use for backend selection, local GPU evals, and choosing between vLLM / Transformers / accelerate. Not for HF Jobs orchestration, model-card PRs, .eval_results publication, or community-evals automation.
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。
Publish and manage research papers on Hugging Face Hub. Supports creating paper pages, linking papers to models/datasets, claiming authorship, and generating professional markdown-based research articles.
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 and configure Claude Code sub-agents with custom prompts, tools, and models
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 ML.NET to train, evaluate, or integrate machine-learning models into .NET applications with realistic data preparation, inference, and deployment expectations. USE FOR: ML.NET integration; local model training or retraining; inference pipelines, model loading, evaluation, and deployment review. DO NOT USE FOR: unrelated stacks; generic tasks that do not need this specific guidance. INVOKES: inspect the repository context, edit targeted files, and run relevant build, test, lint, or validation commands when changes are made.
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。
Manual QA harness for the senpi coding agent itself. MUST USE after changing packages/ai, packages/agent, packages/coding-agent, or packages/tui — a green typecheck and `npm test` are NOT QA. Drives the real CLI from source in an isolated sandbox (never touches ~/.senpi or real credentials) across four channels: remote RPC (--mode rpc JSONL stdio), TUI smoke (node-pty on Windows, tmux on POSIX), mock loop (a local fake model server for deterministic, zero-token agent-loop runs), and CLI smoke (--help/--print/--list-models). Every helper ships a --self-test. Use whenever someone says qa senpi, test the agent, verify my change, rpc qa, tui qa, mock-loop qa, smoke the cli, or needs evidence that an agent-loop, tool, keybinding, or provider change works end to end. Capture evidence to local-ignore/qa-evidence/.
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。
Cyber threat intelligence production — the intelligence cycle, IOC extraction/normalization/enrichment, STIX/TAXII and MISP, structured analytic models (Diamond, Kill Chain, ATT&CK), source scoring, actor/campaign tracking, and finished intelligence reporting
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。
Execute Gemini CLI for AI-powered code analysis and generation. Use when you need to leverage Google's Gemini models for complex reasoning 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。
Stop and consult this skill whenever your response would include specific facts about Anthropic's products. Covers: Claude Code (how to install, Node.js requirements, platform/OS support, MCP server integration, configuration), Claude API (function calling/tool use, batch processing, SDK usage, rate limits, pricing, models, streaming), and Claude.ai (Pro vs Team vs Enterprise plans, feature limits). Trigger this even for coding tasks that use the Anthropic SDK, content creation mentioning Claude capabilities or pricing, or LLM provider comparisons. Any time you would otherwise rely on memory for Anthropic product details, verify here instead — your training data may be outdated or wrong.
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。
Query UCSC Genome Browser REST API for DNA sequences, tracks, gene models, and conservation across 100+ assemblies. Retrieve sequence by region, list/fetch BED/bigWig tracks, chromosome sizes, RefSeq/GENCODE gene structures, PhyloP/PhastCons scores. Use for UCSC annotations; Ensembl REST API for Ensembl gene IDs and VEP variant annotation.
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。
Symbolic math in Python: exact algebra, calculus (derivatives, integrals, limits), equation solving, symbolic matrices, ODEs, code gen (lambdify, C/Fortran). Use for exact symbolic results. For numerical use numpy/scipy; for stats use statsmodels.
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 GitHub Copilot CLI task delegation in non-interactive mode with multi-model support (Claude, GPT, Gemini), permission controls, output sharing, and session resume. Use when users ask to hand work to Copilot, compare models, or run Copilot programmatically from Claude 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 Gemini CLI delegation workflows for large-context analysis and complex reasoning using Gemini 3.0 Flash and Gemini 3.0 Pro models, including English prompt formulation, execution flags, and safe result handling. Use when the user explicitly asks to use Gemini for tasks such as broad codebase analysis, fast iterations with Gemini 3 Flash, or deep architectural reasoning with Gemini 3 Pro. Triggers on "use gemini", "delegate to gemini", "run gemini cli", "ask gemini", "use gemini for this task", "use gemini 3 flash", "use gemini 3 pro".
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 data needs statistical analysis. Runs reproducible Python/R code for Table 1, diagnostic accuracy, agreement, regression, survival, propensity score, survey-weighted and repeated-measures models, with publication tables. Sample size is /calc-sample-size; pooling studies is /meta-analysis.
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。