Testing patterns for PHPUnit and Playwright E2E tests. Use when writing tests, debugging test failures, setting up test coverage, or implementing test patterns for ActivityPub features.
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 Model Context Protocol (MCP) servers - discover, analyze, and execute tools/prompts/resources from configured MCP servers. Use when working with MCP integrations, need to discover available MCP capabilities, filter MCP tools for specific tasks, execute MCP tools programmatically, access MCP prompts/resources, or implement MCP client functionality. Supports intelligent tool selection, multi-server management, and context-efficient capability discovery.
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Skill for managing and interacting with Model Context Protocol (MCP) servers.
MCP is an open protocol enabling AI agents to connect to external tools and data sources. This skill provides scripts and utilities to discover, analyze, and execute MCP capabilities from configured servers without polluting the main context window.
Key Benefits:
Use this skill when:
MCP servers configured in .claude/.mcp.json.
Gemini CLI Integration (recommended): Create symlink to .gemini/settings.json:
mkdir -p .gemini && ln -sf .claude/.mcp.json .gemini/settings.json
See references/configuration.md and references/gemini-cli-integration.md.
npx tsx scripts/cli.ts list-tools # Saves to assets/tools.json
npx tsx scripts/cli.ts list-prompts
npx tsx scripts/cli.ts list-resources
Aggregates capabilities from multiple servers with server identification.
LLM analyzes assets/tools.json directly - better than keyword matching algorithms.
Primary: Gemini CLI (if available)
gemini -y -m gemini-2.5-flash -p "Take a screenshot of https://example.com"
Secondary: Direct Scripts
npx tsx scripts/cli.ts call-tool memory create_entities '{"entities":[...]}'
Fallback: mcp-manager Subagent
See references/gemini-cli-integration.md for complete examples.
Use Gemini CLI for automatic tool discovery and execution. See references/gemini-cli-integration.md for complete guide.
Quick Example:
gemini -y -m gemini-2.5-flash -p "Take a screenshot of https://example.com"
Benefits: Automatic tool discovery, natural language execution, faster than subagent orchestration.
Use mcp-manager agent when Gemini CLI unavailable. Subagent discovers tools, selects relevant ones, executes tasks, reports back.
Benefit: Main context stays clean, only relevant tool definitions loaded when needed.
LLM reads assets/tools.json, intelligently selects relevant tools using context understanding, synonyms, and intent recognition.
Coordinate tools across multiple servers. Each tool knows its source server for proper routing.
Core MCP client manager class. Handles:
.claude/.mcp.jsonCommand-line interface for MCP operations. Commands:
list-tools - Display all tools and save to assets/tools.jsonlist-prompts - Display all promptslist-resources - Display all resourcescall-tool <server> <tool> <json> - Execute a toolNote: list-tools persists complete tool catalog to assets/tools.json with full schemas for fast reference, offline browsing, and version control.
Method 1: Gemini CLI (recommended)
npm install -g gemini-cli
mkdir -p .gemini && ln -sf .claude/.mcp.json .gemini/settings.json
gemini -y -m gemini-2.5-flash -p "Take a screenshot of https://example.com"
Method 2: Scripts
cd .claude/skills/mcp-management/scripts && npm install
npx tsx cli.ts list-tools # Saves to assets/tools.json
npx tsx cli.ts call-tool memory create_entities '{"entities":[...]}'
Method 3: mcp-manager Subagent
See references/gemini-cli-integration.md for complete guide.
See references/mcp-protocol.md for:
Gemini CLI (Primary): Fast, automatic, intelligent tool selection
command -v geminigemini -y -m gemini-2.5-flash -p "<task>"Direct CLI Scripts (Secondary): Manual tool specification
npx tsx scripts/cli.ts call-tool <server> <tool> <args>mcp-manager Subagent (Fallback): Context-efficient delegation
The mcp-manager agent uses this skill to:
gemini command if availableThis keeps main agent context clean and enables efficient MCP integration.
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概要と使いどころ
Testing patterns for PHPUnit and Playwright E2E tests. Use when writing tests, debugging test failures, setting up test coverage, or implementing test patterns for ActivityPub features.
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。
Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence optimization. Also use for submitting experiments via API, tracking experiment status, downloading results, optimizing protein sequences for better expression using computational tools (NetSolP, SoluProt, SolubleMPNN, ESM), or managing protein design workflows with wet-lab validation.
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。
Add unsigned integer (uint) type support to PyTorch operators by updating AT_DISPATCH macros. Use when adding support for uint16, uint32, uint64 types to operators, kernels, or when user mentions enabling unsigned types, barebones unsigned types, or uint support.
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 an agent", "add an agent", "write a subagent", "agent frontmatter", "when to use description", "agent examples", "agent tools", "agent colors", "autonomous agent", or needs guidance on agent structure, system prompts, triggering conditions, or agent development best practices for Claude Code plugins.
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。
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search 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。
Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
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。