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概要と使いどころ
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.
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 and curate knowledge-base using ByteRover CLI. Use `brv query` for knowledge retrieval, `brv curate` for adding context, and `brv push/pull` for syncing.
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 the Model Context Protocol (MCP) Atlassian server in Docker, enabling integration with Jira, Confluence, and other Atlassian products. Use when you need to query Jira issues, search Confluence, or interact with Atlassian services programmatically. Requires Docker and valid Jira API credentials.
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。
A modern text-based browser. Renders web pages in the terminal using headless Firefox.
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 user-facing App Store release notes by collecting and summarizing all user-impacting changes since the last git tag (or a specified ref). Use when asked to generate a comprehensive release changelog, App Store "What's New" text, or release notes based on git history or tags.
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。
Memory is the cornerstone of intelligent agents. Without it, every interaction starts from zero. This skill covers the architecture of agent memory: short-term (context window), long-term (vector stores), and the cognitive architectures that organize them. Key insight: Memory isn't just storage - it's retrieval. A million stored facts mean nothing if you can't find the right one. Chunking, embedding, and retrieval strategies determine whether your agent remembers or forgets. The field is fragm
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。
Triage, categorize, and draft responses to emails. Sorts by urgency, flags action items, and generates context-aware reply drafts.
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。
Extract calendar events, deadlines, action items, and follow-ups from emails. Works with any calendar provider (Google, Outlook, Apple, Notion, etc.). No external dependencies — pure agent intelligence. Use when the user forwards an email, asks to check inbox for events, or wants to extract structured scheduling data from any text.
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。
Complete Claude Code productivity system — project setup, prompting patterns, sub-agent orchestration, context management, debugging, refactoring, TDD, and shipping 10X faster. Zero scripts needed.
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。
Mema's personal brain - SQLite metadata index for documents and Redis short-term context buffer. Use for organizing workspace knowledge paths and managing ephemeral session state.
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。
High-performance temporary storage system using Redis. Supports namespaced keys (mema:*), TTL management, and session context caching. Use for: (1) Saving agent state, (2) Caching API results, (3) Sharing data between sub-agents.
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。
Send personalized LinkedIn direct messages to a list of existing 1st-degree connections via browser automation. Use when the user wants to message LinkedIn connections with AI-personalized outreach — e.g. nurturing leads, following up after events, reconnecting with contacts, or announcing something. Takes a data file (CSV/TSV) or plain list with connection names and companies, asks for outreach context/goal, generates a tailored message per person, and sends each one via browser automation. Handles message compose flow, character limits, and incremental status tracking.
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。
Manage LinkedIn outreach leads from Google Sheets — search by name, read live conversation threads, update status, and send contextual follow-up messages. Use after linkedin-dm to move leads through the pipeline (Sent → Replied → Call Scheduled → Demo Done → Closed).
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。
Place MML blocks in Doppel worlds. Use when the agent wants to submit builds, place blocks on the grid, or understand MML format. Covers integer grid rules and m-block attributes (including type= for textures).
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。
Interact with the BNB Chain Model Context Protocol (MCP) server. Use to query DeFi data, get token prices, search documentation, fetch git diffs, and retrieve smart contract source code on BNB Chain.
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 creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
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 any time a .pptx file is involved in any way — as input, output, or both. This includes: creating slide decks, pitch decks, or presentations; reading, parsing, or extracting text from any .pptx file (even if the extracted content will be used elsewhere, like in an email or summary); editing, modifying, or updating existing presentations; combining or splitting slide files; working with templates, layouts, speaker notes, or comments. Trigger whenever the user mentions "deck," "slides," "presentation," or references a .pptx filename, regardless of what they plan to do with the content afterward. If a .pptx file needs to be opened, created, or touched, use this 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 this skill whenever the user wants to do anything with PDF files. This includes reading or extracting text/tables from PDFs, combining or merging multiple PDFs into one, splitting PDFs apart, rotating pages, adding watermarks, creating new PDFs, filling PDF forms, encrypting/decrypting PDFs, extracting images, and OCR on scanned PDFs to make them searchable. If the user mentions a .pdf file or asks to produce one, use this 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。
React composition patterns that scale. Use when refactoring components with boolean prop proliferation, building flexible component libraries, or designing reusable APIs. Triggers on tasks involving compound components, render props, context providers, or component architecture. Includes React 19 API changes.
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 Claude Code features and capabilities to uncover advanced usage patterns, best practices, and real-world application scenarios. Use this skill whenever the user mentions a Claude Code feature (like Streaming Tool Execution, Hooks, MCP, Skills, Subagents, Context Management, Permission Modes, etc.) and wants to know how to leverage it effectively — including advanced tips, workflow optimizations, creative use cases, and pitfalls to avoid. Also trigger when the user pastes a feature description or asks 'how to use X better in Claude Code', even without explicitly saying 'analyze'.
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 users through a structured workflow for co-authoring documentation. Use when user wants to write documentation, proposals, technical specs, decision docs, or similar structured content. This workflow helps users efficiently transfer context, refine content through iteration, and verify the doc works for readers. Trigger when user mentions writing docs, creating proposals, drafting specs, or similar documentation 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。
Web search, content extraction, crawling, and deep research via the Tavily CLI. Use this skill whenever the user wants to search the web, find articles, research a topic, look something up online, extract content from a URL, grab text from a webpage, crawl documentation, download a site's pages, discover URLs on a domain, or conduct in-depth research with citations. Also use when they say "fetch this page", "pull the content from", "get the page at https://", "find me articles about", or reference extracting data from external websites. This provides LLM-optimized web search, content extraction, site crawling, URL discovery, and AI-powered deep research — capabilities beyond what agents can do natively. Do NOT trigger for local file operations, git commands, deployments, or code editing 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。
Converts the learning Markdown into a standalone, split-screen interactive HTML page. Left pane for EN/CN text, right pane for deep linguistic 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。
Securely store, search, and use secrets (API keys, tokens, passwords, SSH keys) with hal-vault, an SSH-key encrypted local secret store. Use when the user shares a credential that should be saved, asks what secrets are stored or where a key is, or when a command/workflow needs a secret injected. Core discipline - never print raw secret values into chat, logs, or files; reference secrets only by their masked form, and use --reveal only to feed another process, never into a command argument (where it lands in the machine's process table) or into text you write down.
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。