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概要と使いどころ
Train or fine-tune language models with TRL or Unsloth on Hugging Face Jobs, including SFT, DPO, GRPO, reward models, and GGUF conversion. Use for cloud LLM training; use huggingface-vision-trainer for vision 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。
Train object-detection, image-classification, or SAM segmentation models on Hugging Face Jobs. Use for vision fine-tuning and evaluation; use huggingface-llm-trainer for language 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。
Analyze images, screenshots, diagrams, and visual content - Use when you need to understand visual content like screenshots, architecture diagrams, UI mockups, or error screenshots.
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 for social science academic paper drafting, outlining, diagnosis, review, and revision. Trigger for topic diagnosis, research question refinement, literature review planning, CNKI/Google Scholar literature search-to-Zotero workflows, structured paper reading notes, theoretical framework design, research design critique, draft review, section or paragraph revision, citation and evidence risk checking, causal-claim checking, and pre-submission review. Do not use for purely creative writing or non-academic editing.
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 best practices for robot perception systems covering cameras, LiDARs, depth sensors, IMUs, and multi-sensor setups. Use this skill when working with RGB image processing, depth maps, point clouds, sensor calibration (intrinsic, extrinsic, hand-eye), object detection, semantic segmentation, 3D reconstruction, visual servoing, or perception pipeline optimization. Trigger whenever the user mentions OpenCV, Open3D, PCL, RealSense, ZED, OAK-D, camera calibration, AprilTags, ArUco markers, stereo vision, RGBD, point cloud filtering, ICP registration, coordinate transforms, camera intrinsics, distortion correction, image undistortion, sensor streaming, frame synchronization, or any computer vision task in a robotics context. Also covers multi-camera rigs, time synchronization across sensors, perception latency budgets, and production deployment of perception pipelines.
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。
スクリーンショットからGemini Vision APIで操作チュートリアルを自動生成する。 「操作マニュアルを作って」「スクショから手順書を生成」「使い方ガイドを作成」等で発動。
This skill should be used when the user asks about "pantsing", "discovery write", "write without an outline", "discovery draft", "write into the dark", "story kernel", "reconcile a chapter", "dead end", "drafting sprint", "writing cadence", or wants to draft a story by discovery with retrospective bible reconciliation. NOT for outline-first drafting, or mysteries and other clue-dependent genres where setup must come before payoff (use chapter-writing), or a reverse outline of an existing linear draft, a subplot cut, or another revision pass on existing chapters (use revision-continuity).
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。
This skill should be used when the user asks about "mystery", "fair play", "clue", "red herring", "romance beats", "HEA", "thriller", "ticking clock", "horror", "dread", "MG", "YA", "middle grade", "young adult", "science fiction", "sci-fi", "fantasy", "quest", "chosen one", "historical fiction", "period voice", "anachronism", "comedy", "humour", "humor", "romcom", "farce", "satire", "running gag", "serial", "episodic", "web serial", "genre conventions", or wants genre-specific structural craft for a story project. NOT for a clue check as a revision pass (use revision-continuity).
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。
This skill should be used when the user asks to "create a plot arc", "story structure", "add a plot point", "story timeline", "track foreshadowing", "pacing", "sagging middle", "act structure", "story arc", "plot outline", "snowflake method", or wants to plan and manage the narrative structure of a story. It owns book-level pacing; NOT for the craft of scene outcomes or chapter hooks, which this skill only plans in the outline (use scene-craft), or a pacing check as a revision pass (use revision-continuity).
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。
Implements HIPAA Privacy Rule requirements for research uses of protected health information under 45 CFR §164.512(i). Covers IRB and Privacy Board waivers of authorization, individual authorization for research, limited data set and data use agreements, preparatory to research provisions, and decedent research provisions. Keywords: HIPAA research, IRB waiver, Privacy Board, authorization, limited data set, preparatory research, de-identification, Common Rule.
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。
Plans an architecture-driven change to code that already exists: module boundaries, type responsibilities, layering, coupling, and the public surface a revision moves. Produces a buildable change plan that names the types, modules, and methods involved and records the surface delta each change makes. Use when the user wants to plan, scope, or sequence a restructure, extraction, split, consolidation, responsibility shift, or API revision of existing code, including "these responsibilities are wrong, plan the fix". Does not assess an area and stop at findings — use architectural-analysis. Does not specify new behavior — use plan-a-feature. Does not diagnose a bug — use investigate. Does not write the code — use refactor to restructure it or tdd to build it.
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。
Systematic step-by-step reasoning with revision and branching. Use for complex problems, multi-stage analysis, design planning, problem decomposition, or encountering unclear scope, alternative approaches needed, revision requirements.
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 skill when an agent needs Google Cloud Vision API for still-image understanding: labels, object localization, OCR, document text, SafeSearch, image properties, crop hints, web detection, batch annotation, Cloud Storage based pipelines, confidence evaluation, privacy, quotas, cost, and QA. Do not use it for Gemini multimodal reasoning, video analysis, image generation, custom model training, product catalog search design, or human reference/authenticity review.
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。
Provider-independent music supervision and scoring direction for AI agents producing generated videos, ads, trailers, explainers, product films, social clips, documentaries, game trailers, and branded content. Use when planning, prompting, selecting, licensing, mixing, repairing, or quality-reviewing music, score, temp tracks, needle drops, AI-generated tracks, cue sheets, stems, loudness, loopability, platform music constraints, and rights-aware audio handoff for visual media.
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。
Designs system architecture and selects technology stack based on vision analysis. Use after vision analysis for technical decisions. Triggers on: design architecture, select tech stack, choose framework.
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 cloud backend applications with Encore — type-safe backend framework with built-in infrastructure. Use when someone asks to "build a backend", "Encore", "type-safe API framework", "backend with built-in infra", "auto-provision cloud resources", or "backend framework with databases built-in". Covers API definition, databases, pub/sub, cron, and auto-provisioned infrastructure.
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。
Parse architectural drawings, floor plans, and building code compliance documents using Vision AI. Extracts building type, occupancy, floor areas, room layouts, dimensions, and code parameters. Use when: reading PDF floor plans, analyzing architectural drawings, extracting building data from images or scanned documents.
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。
Computer vision expert specializing in real-time hand tracking and gesture interface designUse when "hand tracking, gesture recognition, mediapipe, hand gestures, touchless interface, sign language, hand pose, finger tracking, mediapipe, hand-tracking, gesture-recognition, computer-vision, hci, touchless, ml" mentioned.
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 patterns for AI-powered document understanding including PDF parsing, OCR, invoice/receipt extraction, table extraction, multimodal RAG with vision models, and structured data output. Use when "document parsing, PDF extraction, OCR, invoice processing, receipt extraction, document understanding, LlamaParse, Unstructured, vision document, table extraction, structured output from PDF, " mentioned.
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 that interact with computers like humans do - viewing screens, moving cursors, clicking buttons, and typing text. Covers Anthropic's Computer Use, OpenAI's Operator/CUA, and open-source alternatives. Critical focus on sandboxing, security, and handling the unique challenges of vision-based control. Use when "computer use, desktop automation agent, screen control AI, vision-based agent, GUI automation, Claude computer, OpenAI Operator, browser agent, visual agent, RPA with AI, " mentioned.
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。
Articulate a compelling north-star product vision that aligns teams and inspires strategic design decisions.
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。
Manages Neo4j Aura Agents via the v2beta1 REST API — create, list, get, update, delete, and invoke Aura agents backed by an AuraDB instance. Use when configuring Aura Agent tools (CypherTemplate, SimilaritySearch, Text2Cypher), setting system prompts, deploying agents to REST or MCP endpoints, invoking agents with natural language queries, or generating evaluation datasets (max 50 questions) for the Aura Agent evaluation feature. Covers OAuth2 auth, organization/project scoping, tool parameter schemas, and InvokeAgentResponse format. Does NOT cover AuraDB instance provisioning — use neo4j-aura-provisioning-skill. Does NOT cover vector index creation — use neo4j-vector-index-skill.
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 installing, configuring, or troubleshooting the official Neo4j MCP server (neo4j/mcp) — connecting Claude Code, Claude Desktop, Cursor, Windsurf, VS Code, Kiro, or other MCP-compatible editors to a Neo4j database via stdio or HTTP transport. Covers the four MCP tools (get-schema, read-cypher, write-cypher, list-gds-procedures), read-only mode, and multi-database configuration. Does NOT cover writing Cypher queries via those tools — use neo4j-cypher-skill. Does NOT cover agent memory — use neo4j-agent-memory-skill. Does NOT cover Aura instance provisioning — use neo4j-aura-provisioning-skill.
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。
Orchestrates zero-to-running-app in 8 stages — prerequisites → context → provision → model → load → explore → query → build. Each stage reads its own reference file. Supports HITL and fully autonomous operation. Use when starting a new Neo4j project from scratch, provisioning Aura, generating synthetic data, building a notebook or app, or running the full onboarding pipeline. Time budget ≤15 min autonomous, ≤90 min HITL. Does NOT cover Cypher query authoring — use neo4j-cypher-skill. Does NOT cover driver upgrades or Cypher migration — use neo4j-migration-skill. Does NOT cover CLI/admin tasks on an existing DB — use neo4j-cli-tools-skill.
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。