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概要と使いどころ
Git-Notes-Based knowledge graph memory system. Claude should use this SILENTLY and AUTOMATICALLY - never ask users about memory operations. Branch-aware persistent memory using git notes. Handles context, decisions, tasks, and learnings across sessions.
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。
Intelligently organizes files and folders by understanding context, finding duplicates, and suggesting better organizational structures. Use when user wants to clean up directories, organize downloads, remove duplicates, or restructure projects.
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。
Deep Research Agent specializes in complex, multi-step research tasks that require planning, decomposition, and long-context reasoning across tools and files by we-crafted.com/agents/deep-research
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。
Neural web search and code context via Exa AI API. Requires EXA_API_KEY. Use for finding documentation, code examples, research papers, or company info.
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。
Manages project knowledge using ByteRover context tree. Provides two operations: query (retrieve knowledge) and curate (store knowledge). Invoke when user requests information lookup, pattern discovery, or knowledge persistence. Developed by ByteRover Inc. (https://byterover.dev/)
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。
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。
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。
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。
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。
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。
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。
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。
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。
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。
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。
Resume work on the current task. Loads the workflow Phase Index, figures out which phase/step to pick up at, then pulls the step-level detail via get_context.py --mode phase. Use when coming back to an in-progress task and you need to know what to do next.
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 quality verification: spec compliance, lint, type-check, tests, cross-layer data flow, code reuse, and consistency checks. Use when code is written and needs quality verification, before committing changes, or to catch context drift during long sessions.
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。
Initializes an AI development session by reading workflow guides, developer identity, git status, active tasks, and project guidelines from .trellis/. Classifies incoming tasks and routes to brainstorm, direct edit, or task workflow. Use when beginning a new coding session, resuming work, starting a new task, or re-establishing project context.
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 the user wants to set up, check, repair, or report the SuperCtx state for this repository. Consolidated command for global context convergence.
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。