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概要と使いどころ
当用户需要做方向性判断、评估「该往哪走/这事靠谱吗」、或想提升决策能力时调用。 核心理念: 判断力(知道行为长期后果)比努力重要; 清晰思考=从基础学科重建推理; 收集心智模型(复利/委托代理/可证伪性/基础数学)。 不适用于: 需要立即执行的具体任务步骤。 Triggers: 判断力/方向/第一性原理/心智模型/思维模型/判断靠不靠谱/judgment/mental model/first principles
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。
Hugging Face Hub CLI (`hf`) for downloading, uploading, and managing models, datasets, spaces, buckets, repos, papers, jobs, and more on the Hugging Face Hub. Use when: handling authentication; managing local cache; managing Hugging Face Buckets; running or scheduling jobs on Hugging Face infrastructure; managing Hugging Face repos; discussions and pull requests; browsing models, datasets and spaces; reading, searching, or browsing academic papers; managing collections; querying datasets; configuring spaces; setting up webhooks; or deploying and managing HF Inference Endpoints. Make sure to use this skill whenever the user mentions 'hf', 'huggingface', 'Hugging Face', 'huggingface-cli', or 'hugging face cli', or wants to do anything related to the Hugging Face ecosystem and to AI and ML in general. Also use for cloud storage needs like training checkpoints, data pipelines, or agent traces. Use even if the user doesn't explicitly ask for a CLI command. Replaces the deprecated `huggingface-cli`.
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 Transformers.js to run state-of-the-art machine learning models directly in JavaScript/TypeScript. Supports NLP (text classification, translation, summarization), computer vision (image classification, object detection), audio (speech recognition, audio classification), and multimodal tasks. Works in browsers and server-side runtimes (Node.js, Bun, Deno) with WebGPU/WASM using pre-trained models from Hugging Face Hub.
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 and publish a Gradio demo on Hugging Face Spaces for a user-provided LoRA. Use when someone asks to create, generate, ship, or publish a Space, demo, Gradio app, or playground for a LoRA — including LoRAs for Qwen-Image, Qwen-Image-Edit, LTX-Video, Wan, FLUX, SDXL, or other diffusion base models. Also triggers when someone describes a LoRA they trained or hosts on the Hub and wants to share it. Covers picking the right base pipeline and `diffusers` inference recipe, designing a UI tailored to the LoRA's task and inputs (Union/multi-task control, edit, video, image, etc.), respecting model-card recommendations (trigger words, steps, guidance, LoRA scale, example inputs), and shipping to ZeroGPU hardware as a private Space by default.
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。
AI demos and GPU compute with Gradio Spaces and Hugging Face Spaces ZeroGPU. Use when writing or reviewing code that uses `@spaces.GPU`, configuring `python_version` or `requirements.txt` for a ZeroGPU Space, or handling ZeroGPU-specific code constraints — pickle-based process isolation, `gr.State` semantics across the worker boundary, no `torch.compile` (use AoTI instead), CUDA wheel-only builds (no `nvcc` at build or runtime), large vs xlarge sizing, and dynamic duration callables. Make sure to use this skill whenever the user mentions ZeroGPU, `@spaces.GPU`, or the `spaces` Python package, or hits ZeroGPU-specific code errors like `PicklingError` across the worker boundary, `illegal duration`, or `flash-attn` wheel-build failures — even when the user does not explicitly ask for ZeroGPU coding guidance. Trigger on `import spaces` or `@spaces.GPU` in 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。
Set up an isolated Python environment for SageMaker / AWS work, with the right Python version and current boto3. Use this skill whenever Python code will be executed for a SageMaker deployment, training job, or any AWS automation — including when about to run `pip install`, when about to invoke `boto3`, when creating or activating a virtualenv, or when the user asks to "set up the environment". Never use system Python and never `pip install` into it. Always isolate. This skill prevents the most common failure modes: wrong Python version, dependency conflicts, and stale SDKs.
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。
Required reading whenever any chart_* tool is available. Teaches the one-tool embedding contract (call chart_render → live chart appears in chat AND a downloadable PNG lands in the queen session dir), the ECharts (data viz) vs Mermaid (structural diagrams) decision, the BI/financial-grade aesthetic baseline (no chartjunk, restrained palette, proper typography, single message per chart), and the canonical spec patterns for the 12 most-common chart types. Skipping this leads to 1990s-Excel charts, missing downloads, and the agent writing markdown image links by hand instead of letting chart_render drive the UI.
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。
Diagnose and draft or explicitly apply paid-ad optimizations using evidence, financial constraints, experiments, and capability-gated adapters. Use for campaign optimization, budget reallocation, bid changes, pausing or archiving ads, requests to delete campaigns, search-term or negative-keyword actions, creative rotation, or improving CPA and ROAS.
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。
Plan and review paid-media budgets, bidding, pacing, marginal return, forecasts, CPA, ROAS, MER, LTV:CAC, constraints, and allocation across supported platforms. Use for ad budget allocation, media budget, bidding strategy, scaling, spend pacing, budget forecast, ROAS target, or investment tradeoffs.
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 vendor AI terms — agreement, addendum, or ToS AI provisions — against your governance positions; flag training-on-data, liability, model changes, and AI policy consistency. Use when user says "review this AI agreement", "check OpenAI terms", "what did we agree to with [vendor]", "vendor sent an AI addendum", "is this AI contract okay", or attaches vendor AI terms.
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。
暖纸背景 + Playfair, 左侧学习目标常驻, 含 MCQ 自测页
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。
Web information retrieval expert. ALWAYS invoke this skill when the user needs to search the web, fetch a URL, or access real-time information beyond training data. Do not guess or use stale knowledge — use this skill first.
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。
Reliable fallback technique for writing large file contents when write_file and shell_agent both fail with 'unknown error' due to payload size limits — uses run_shell with a Python heredoc to bypass tool constraints, with an explicit mkdir -p step to ensure the target directory exists before writing.
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。
Reliable fallback technique for writing large file contents when write_file and shell_agent both fail with 'unknown error' due to payload size limits — uses run_shell with a Python heredoc to bypass tool constraints.
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。
Selects bounded, graph-informed source slices with Trailmark and delegates focused code analysis or patch-proposal work to a smaller subagent. Use when offloading function-, class-, caller-, callee-, call-path-, entrypoint-, or line-focused code tasks to constrained or locally hosted models without exposing the full repository.
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 designing, reviewing, or debugging how an agent's context window gets filled, pruned, or shared — choosing what loads at boot versus on demand, sizing an install or an always-loaded file, fixing an agent that drifts, repeats itself, or forgets constraints mid-task, planning compaction or summarization, deciding single-agent versus subagents, engineering handoffs between agents, or picking a tool loadout. NOT for rewording a prompt's tone, choosing which model to pin, or debugging business logic — those are adjacent moments this skill does not serve. Historical 2024–2025 snapshot; not normative for present-day frontier models — see the Status section.
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。
Submit compact gnomAD GraphQL requests for frequency, gene constraint, and variant context queries. Use when a user wants concise gnomAD summaries
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。
Prompt- and template-complete Google Docs creation and editing with explicit-instruction-authoritative structural preservation, including semantic roles, relationships, comparison dimensions, and instructed extensions; full-topology native-copy routing; source-grounded per-tab adaptation for past/example references; style-preserving hyperlink and table edits; canonical smart-chip-first authoring for dates and relevant supported people or Google resources; a file-backed advisory trusted read before existing-document writes; automatic protected-control awareness; direct connector APIs by default; DOCX-first import only when no supplied Google Doc template/reference constrains the output; and checked-in code mode only for exact native dropdown mutation. Use when Codex must create, edit, fill, adapt, redesign, or verify Google Docs without overriding explicit user/template instructions, adding unrequested document scope, or carrying stale reference facts into a new deliverable.
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。
Activate when the user explicitly names the narrator skill or requests production-ready narration in either case: (1) numbered takes fitted to fixed video windows, or (2) one continuous long-form story read in a locked voice that must be measured and retried for timbre or internal pauses. Collect missing text, voice_id, or voice_type after activation; their absence is not a reason to skip this workflow. Own pacing, speech-duration gates, retries, and ready audio delivery; never time-stretch. Do not use for ordinary TTS, "read this aloud", an unconstrained voiceover, voice browsing, music, SFX, singing, dubbing existing speech, or voice cloning.
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。
MANDATORY prerequisite — load this skill BEFORE every `generate_diagram` tool call. NEVER call `generate_diagram` directly without loading this skill first. Trigger whenever the user asks to create, generate, draw, render, sketch, or build a diagram — flowchart, architecture diagram, sequence diagram, ERD or entity-relationship diagram, state diagram or state machine, gantt chart, or timeline. Also trigger when the user mentions Mermaid syntax or wants a system architecture, decision tree, dependency graph, API call flow, auth handshake, schema, or pipeline visualized in FigJam. Routes to type-specific guidance, sets universal Mermaid constraints, and tells you when to use a different diagram type or skip the tool entirely (mindmaps, pie charts, class diagrams, etc.).
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 sandboxed applications for secure code execution. Load when building AI code execution, code interpreters, CI/CD systems, interactive dev environments, or executing untrusted code. Covers Sandbox SDK lifecycle, commands, files, code interpreter, and preview URLs. Biases towards retrieval from Cloudflare docs over pre-trained knowledge.
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。
Cloudflare Workers CLI for deploying, developing, and managing Workers, KV, R2, D1, Vectorize, Hyperdrive, Workers AI, Containers, Queues, Workflows, Pipelines, and Secrets Store. Load before running wrangler commands to ensure correct syntax and best practices. Biases towards retrieval from Cloudflare docs over pre-trained knowledge.
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。
Reviews and authors Cloudflare Workers code against production best practices. Load when writing new Workers, reviewing Worker code, configuring wrangler.jsonc, or checking for common Workers anti-patterns (streaming, floating promises, global state, secrets, bindings, observability). Biases towards retrieval from Cloudflare docs over pre-trained knowledge.
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 AI agents on Cloudflare Workers using the Agents SDK. Load when creating stateful agents, durable workflows, real-time WebSocket apps, scheduled tasks, MCP servers, or chat applications. Covers Agent class, state management, callable RPC, Workflows integration, and React hooks. Biases towards retrieval from Cloudflare docs over pre-trained knowledge.
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。