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概要と使いどころ
Review an existing verdaccio/verdaccio pull request end to end — description, diff, review threads, CI, labels, changeset, release-line coverage — verify the findings, and report them; optionally fix them on the PR branch when asked. Use when given a PR number or URL to review, re-review, or "review and fix".
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。
口播稿，适用于 AI 技术科普、Agent/Skill/RAG、LLM 等底层原理讲解，以及面试题拆解、工具实测、热点普及。支持两种用法：给定主题从零产出口播稿、对已有口播稿进行优化。触发关键词包括：口播、口播稿、video script。
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 类文章和项目教程的撰写与重构优化。支持新写和重构优化（结合最新源码重写已有文章）两种输入模式，四种风格：安装教程、产品评测、面试八股、深度拆解。覆盖 AI Coding 工具实测、AI 开发框架应用、大模型测评、Agent/Skills/RAG 技术讲解。
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。
Workflow 4: Submission rebuttal pipeline. Parses external reviews, enforces coverage and grounding, drafts a safe text-only rebuttal under venue limits, and manages follow-up rounds. Use when user says "rebuttal", "reply to reviewers", "ICML rebuttal", "OpenReview response", or wants to answer external reviews safely.
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。
Pick the right library for a given frontend task from a curated, opinionated list — numbers, OTP inputs, charts, command menus, virtualization, drag and drop, toasts, state, styling, and more. Only runs when explicitly invoked; it does not trigger on its own.
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's approach to interface design and fluid, physical motion, translated for the web. Use when building or reviewing gesture-driven UI, spring animations, drag/swipe/sheet interactions, momentum and interruptible transitions, translucent materials and depth, typography (optical sizing, tracking, leading), reduced-motion, or the design foundations (feedback, spatial consistency, restraint) behind Apple-style interfaces.
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。
Drives a vehicle to a destination autonomously using CARLA's navigation agents (BasicAgent/BehaviorAgent/ConstantVelocityAgent), or plans a route between two points with GlobalRoutePlanner. The agent handles routing, obstacle avoidance and traffic-light obedience. Use when the user asks to "drive the ego to (x,y,z)", "navigate to the junction", "go to that location", "plan a route from A to B", or "make the car drive itself somewhere specific". Manual control is control-vehicle; autopilot roaming is control-traffic.
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。
Directly drives a specific vehicle on a running CARLA server — raw VehicleControl (throttle/steer/brake/reverse/hand-brake), Ackermann speed control, vehicle lights, opening/closing doors, and physics-control tuning; ros-info reports the native ROS 2 command topics (vehicle_control_cmd, ackermann_control_cmd) for the hero. Targets the ego by default. Use when the user asks to "drive/steer the car", "accelerate/brake/reverse", "set a target speed", "turn on the headlights/blinkers/brake lights", "open a door", "drive the car from ROS", or "change the vehicle's mass/drag/physics". This is manual control (autopilot off) — for TM-driven traffic use control-traffic.
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。
Facebook's library for efficient similarity search and clustering of dense vectors. Supports billions of vectors, GPU acceleration, and various index types (Flat, IVF, HNSW). Use for fast k-NN search, large-scale vector retrieval, or when you need pure similarity search without metadata. Best for high-performance applications.
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 cognitive science frameworks for creative thinking to CS and AI research ideation. Use when seeking genuinely novel research directions by leveraging combinatorial creativity, analogical reasoning, constraint manipulation, and other empirically grounded creative strategies.
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 guide for writing systems papers targeting OSDI, SOSP, ASPLOS, NSDI, and EuroSys. Provides paragraph-level structural blueprints, writing patterns, venue-specific checklists, reviewer guidelines, LaTeX templates, and conference deadlines. Use this skill for all systems conference paper 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。
Multi-cloud orchestration for ML workloads with automatic cost optimization. Use when you need to run training or batch jobs across multiple clouds, leverage spot instances with auto-recovery, or optimize GPU costs across providers.
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。
Meta's 86M prompt injection and jailbreak detector. Filters malicious prompts and third-party data for LLM apps. 99%+ TPR, <1% FPR. Fast (<2ms GPU). Multilingual (8 languages). Deploy with HuggingFace or batch processing for RAG security.
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。
Creates Vue 3 components, builds vanilla JS composables, configures Vite projects, and sets up routing and state management using JavaScript only — no TypeScript. Generates JSDoc-typed code with @typedef, @param, and @returns annotations for full type coverage without a TS compiler. Use when building Vue 3 applications with JavaScript only (no TypeScript), when projects require JSDoc-based type hints, when migrating from Vue 2 Options API to Composition API in JS, or when teams prefer vanilla JavaScript, .mjs modules, or need quick prototypes without TypeScript setup.
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 chaos experiments, creates failure injection frameworks, and facilitates game day exercises for distributed systems — producing runbooks, experiment manifests, rollback procedures, and post-mortem templates. Use when designing chaos experiments, implementing failure injection frameworks, or conducting game day exercises. Invoke for chaos experiments, resilience testing, blast radius control, game days, antifragile systems, fault injection, Chaos Monkey, Litmus Chaos.
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 deploying or managing Kubernetes workloads. Invoke to create deployment manifests, configure pod security policies, set up service accounts, define network isolation rules, debug pod crashes, analyze resource limits, inspect container logs, or right-size workloads. Use for Helm charts, RBAC policies, NetworkPolicies, storage configuration, performance optimization, GitOps pipelines, and multi-cluster management.
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。
Analyzes code diffs and files to identify bugs, security vulnerabilities (SQL injection, XSS, insecure deserialization), code smells, N+1 queries, naming issues, and architectural concerns, then produces a structured review report with prioritized, actionable feedback. Use when reviewing pull requests, conducting code quality audits, identifying refactoring opportunities, or checking for security issues. Invoke for PR reviews, code quality checks, refactoring suggestions, review code, code quality. Complements specialized skills (security-reviewer, test-master) by providing broad-scope review across correctness, performance, maintainability, and test coverage in a single pass.
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。
当用户在纠结职业/副业/自由职业方向、问「我该做什么才能赚钱/不被替代」、或想找到自己的独特优势时调用。 核心理念: 特殊知识(不可培训、对你像玩对别人像工作) × 产品化(杠杆规模化) = 无可替代的致富定位。 不适用于: 纯求职投递、写简历、已有明确方向的执行细节。 Triggers: 找方向/独特优势/副业/不可替代/productize/special knowledge/moat
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。
Audit an executed AI phase's evaluation coverage and produce an EVAL-REVIEW.md remediation plan.
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。
Audit paid-ad copy, images, video, hooks, concepts, format coverage, platform-native fit, message match, creative fatigue, accessibility, and policy across supported platforms. Use for creative audit, ad creative, creative fatigue, creative diversity, ad copy review, video review, image review, or production priorities.
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。
Pre-drafting context gathering for a demand letter — parties, facts, basis, leverage, BATNA, and privilege filters — written to a structured intake.md the demand-draft skill reads. Use when the user wants to prep a demand letter, run intake before drafting, or capture context for a payment demand, breach/cure notice, cease-and-desist, employment separation, or preservation demand.
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。
Generate or drill flashcards for black-letter memorization — Leitner-style buckets, per-subject markdown storage, drill mode with self-assessment. Use when the user says "drill flashcards", "make flashcards from", "quiz me on cards", or wants to memorize rules.
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。
Reference: shared framework for managing internal investigations from intake through final memo — privileged investigation log, document processing with needle-finding, source coverage tracking, Q&A against the log, memo drafting, and audience summaries. Loaded by /investigation-open, /investigation-add, /investigation-query, /investigation-memo, and /investigation-summary; not invoked directly.
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。