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概要と使いどころ
Rewrite AI-generated copy in Spanish and English so it sounds direct, concrete and human while preserving meaning, facts and numbers exactly. Use when drafts feel robotic, verbose or translated and need short active sentences without em-dashes.
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 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。
Review a pull request or a set of code changes for bugs, logic errors, and project-convention violations using a confidence-filtered, multi-agent process. Use this skill when the user asks to review a PR, audit pending changes, or inspect a diff for problems before merging.
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。
Perform a focused security review of pending git changes to identify high-confidence security vulnerabilities with real exploitation potential. Use this skill when the user asks for a security review, security audit, vulnerability scan, or wants to check pending changes on a branch for security issues before merging. This is NOT a general code 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。
Systematically verifies suspected security bugs to eliminate false positives. Produces TRUE POSITIVE or FALSE POSITIVE verdicts with documented evidence for each bug.
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。
Performs security-focused differential review of code changes (PRs, commits, diffs). Adapts analysis depth to codebase size, uses git history for context, calculates blast radius, checks test coverage, and generates comprehensive markdown reports. Automatically detects and prevents security regressions.
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 CLI to estimate the required memory to load Safetensors or GGUF model weights for inference from the 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。
Discover the active AWS profile, region, account, and caller identity before SageMaker or AWS work. Use when local AWS context is needed or unspecified; do not use for cloud-agnostic model 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。
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。
Operational rubric that turns "don't make AI slop" into observable properties, severity levels, evidence requirements, and repair actions for interface design. Use as the reference rubric when building or reviewing marketing sites, product interfaces, dashboards, portfolios, or e-commerce pages, especially alongside frontend-design.
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。
Track and visualize ML training experiments with Trackio. Use when logging metrics during training (Python API), firing alerts for training diagnostics, or retrieving/analyzing logged metrics (CLI). Supports real-time dashboard visualization, alerts with webhooks, HF Space syncing, and JSON output for 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。
Use the `hf` CLI for Hub authentication, downloads, uploads, repositories, cache, jobs, buckets, webhooks, and endpoint administration. Use for CLI operations, not model recommendations, paper analysis, training design, or building a Space.
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 new skills (SKILL.md files), modify and improve existing skills, and design skill descriptions for accurate triggering. Use when the user wants to create a new skill from scratch, edit an existing skill, optimize a skill's description, or convert a workflow they just demonstrated into a reusable 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。
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。
Plan and coordinate a model deployment to Amazon SageMaker, including serving stack and real-time versus async inference. Use as the entry point for SageMaker hosting requests, before image, IAM, and endpoint implementation skills.
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, deploy, debug, or maintain a Hugging Face Space using Gradio, Docker, or Static SDKs. Use for general Space hosting and configuration; use huggingface-zerogpu for ZeroGPU runtime constraints and lora-space-builder for LoRA demos.
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。
Deeply analyze an existing codebase feature by tracing execution paths, mapping architecture layers, understanding patterns and abstractions, and documenting dependencies. Use this skill when you need to understand how a feature works before modifying or extending it, when dispatched as a sub-task during feature-dev exploration, or when the user asks "how does X work in this codebase".
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。
Maximum-craft one-page landing — the wow bar. omd:landing 의 구조 위에 마감(서체 조달·그레인·깊이·색보정·스크롤 타임라인)과 최고급 에셋을 얹고, 랜딩 검사 전 항목 통과를 합격선으로 삼는다. Trigger: '와우', '압도적인', '최고급', '쇼케이스용', 'aphrodite', '데모용 랜딩', '이거 자랑할 거야'.
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。
Maximum-craft one-page landing — the wow bar. omd:landing 의 구조 위에 마감(서체 조달·그레인·깊이·색보정·스크롤 타임라인)과 최고급 에셋을 얹고, 랜딩 검사 전 항목 통과를 합격선으로 삼는다. Trigger: '와우', '압도적인', '최고급', '쇼케이스용', 'aphrodite', '데모용 랜딩', '이거 자랑할 거야'.
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 layout systems and responsive interfaces including grid systems, flexbox patterns, sidebar layouts, and responsive breakpoints. Use when structuring app layouts, building responsive designs, or creating complex page structures.
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。
Check current Claude usage limits (session and weekly) with ASCII progress bars. Trigger when the user asks about usage, quota, limits, rate limits, how much Claude they've used, remaining capacity, or phrases like "check usage", "usage status", "how much quota left", "am I close to the limit", "用量", "额度", "配额".
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 branded architecture, architecture delta, IT current-state, flowchart, sequence, state machine, ER/data model, timeline, swimlane, quadrant, radar/spider, polar chart (polar/radial lollipop), loop/flywheel, nested, tree, org chart, layer stack, exploded axonometric, axonometric plan, Venn, pyramid/funnel, treemap and marimekko, heatmap, bar and dumbbell, waterfall, line (slopegraph, ridgeline, streamgraph, bump), Gantt and scatter charts (bubble, beeswarm), high-level, process, medallion, data flow, DP integration, DP security matrix, Sankey, fishbone, Wardley map, kanban, user journey, deployment, dependency graph, UML class, story map, or database schema diagrams as HTML/SVG/PNG, with .drawio, Mermaid, and .excalidraw import, plus lifecycle phase maps, block decomposition trees, and onboarding guidance.
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 the ax CLI instead of curl + throwaway parsing scripts whenever you fetch a URL, explore an unknown web page, or extract structured data from HTML. Trigger whenever you are about to write an inline script (python3 heredoc, node -e, regex over HTML) or a bare curl for one-off web fetching, scraping, or page exploration.
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 for implementing Shell-based navigation in .NET MAUI apps. Covers AppShell setup, visual hierarchy (FlyoutItem, TabBar, Tab, ShellContent), URI-based navigation with GoToAsync, route registration, query parameters, back navigation, flyout and tab configuration, navigation events, and navigation guards. Use when: setting up Shell navigation, adding tabs or flyout menus, navigating between pages with GoToAsync, passing parameters between pages, registering routes, customizing back button behavior, or guarding navigation with confirmation dialogs. Do not use for: deep linking from external URLs (see .NET MAUI deep linking documentation), data binding on pages (use maui-data-binding), dependency injection setup (use maui-dependency-injection), or NavigationPage-only apps that don't use Shell.
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。