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概要と使いどころ
Design and write deterministic multi-agent workflow scripts (.js files in .claude/workflows/) for Claude Code's Workflow tool. Use when a user wants to build, create, author, scaffold, or run a custom Claude Code workflow, orchestrate sub-agents (fan-out, pipeline, loop, judge-panel), or automate a repeatable multi-step task across fresh-context 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。
Manage AI agents in tmux: spawn sessions, send messages, wait, capture replies, inspect fleets, and tear down safely. Use for tmux-hosted CLI agents. Don't use for SSH, GNU screen, or GUI apps.
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, update, or audit AGENTS.md (default) or CLAUDE.md agent instructions: set up, prune, add a rule, or merge CLAUDE.md or .cursorrules into one file Claude Code, Codex, and Cursor all read. Don't use for READMEs, skills, or subagents.
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。
Give an AI agent GitHub access via @github-tools/sdk. Mount as an eve extension, or use with the AI SDK, TanStack AI, Vercel Workflow, and Chat SDK. Covers tools, presets, approval control, token scoping, and durable 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。
Use this skill when the user explicitly wants to connect to a running Chrome browser, scan local CDP ports, specify a `cdp_port`, or share a single browser across multiple agents/tools. By default, browser_use already launches the browser using managed CDP; if the user does not want to expose browser history, cookies, or other sensitive data, recommend using `private_mode=true` instead.
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。
For external plan request scenarios, guides the Agent to request a clear, actionable, step-by-step plan from a stronger Agent via list_agents and chat_with_agent, emphasizing that the plan is executed by the requester, not by the consulted Agent.
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。
Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
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。
Suede-owned editorial pipeline for agent-run content: six roles with written contracts, one inspectable record that travels between them, and a distribution stage that gives every asset its own argument instead of a shorter draft. Use when content agents return generic, duplicated, or unsourced work, when one flagship piece has to carry a week of distribution, when someone asks to build an AI content team or one-person media company, or when a founder wants a weekly interview-led content system across personal and company accounts. NOT FOR: choosing which topics, pillars, or clusters to cover (use suede-content-strategy); platform mix, cadence, calendars, or listening (use suede-social); an Instagram-specific program (use suede-instagram-growth); running a stage on a schedule with state and stop conditions (use suede-marketing-loops); lane ownership for agents changing code (use suede-agent-teams); writing the flagship piece itself (use suede-ship-copy).
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。
Authoritative reference for the neo4j-agent-memory Python package — a graph-native memory system for AI agents built on Neo4j — and for the hosted service (NAMS) at memory.neo4jlabs.com. Use this skill whenever the user mentions neo4j-agent-memory, agent memory with Neo4j, context graphs, the POLE+O model, MemoryClient/MemorySettings, the memory MCP server, or any of the framework integrations (LangChain, PydanticAI, CrewAI, AWS Strands, Google ADK, Microsoft Agent Framework, OpenAI Agents, LlamaIndex). Also use when the user mentions the hosted service at memory.neo4jlabs.com, NAMS, the Neo4j Agent Memory Service, the `nams_` API key prefix, or the hosted MCP endpoint. Also use when writing documentation, blog posts, tutorials, PRDs, or code samples for the project, when comparing agent memory approaches, or when positioning graph-native memory against vector-only approaches — even if the user doesn't explicitly name the package.
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。
Write, run, and analyze structured test suites for Agentforce agents — functional AND security. TRIGGER when: user writes or modifies test spec YAML (AiEvaluationDefinition); runs sf agent test create, run, run-eval, or results commands; asks about test coverage strategy, metric selection, or custom evaluations; interprets test results or diagnoses test failures; asks about batch testing, regression suites, or CI/CD test integration; requests security testing, OWASP LLM Top 10, red-teaming, penetration testing, prompt-injection tests, a security grade, or a vulnerability assessment of an agent. DO NOT TRIGGER when: user creates, modifies, previews, or debugs .agent files (use agentforce-generate); deploys or publishes agents; writes Agent Script code; uses sf agent preview for development iteration; analyzes production session traces (use agentforce-observe); performs a static safety review of .agent file content (use agentforce-generate Section 15).
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。
Sub-skill: Implement DNS-AID records so agents discover endpoints through DNS. Use SVCB/HTTPS records under _agents namespace with alpn and connection parameters.
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。
Sub-skill: Implement ARD (Agentic Resource Discovery). Publish /.well-known/ai-catalog.json so agents can discover MCP servers, A2A agents, skills and API tools (agenticresourcediscovery.org / ai-catalog).
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。
Discovers and triages recent arXiv papers for AI/ML, agents, and software/QA. Use when scouting categories, arXiv IDs, or source lists.
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。
Mines recent papers, blogs, and newsletters for methods worth stealing, with graded evidence. Use when finding recent papers or techniques for RAG, evals, or 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。
Chooses PaaS hosting for apps, agents, APIs, and workers. Use when picking Vercel, Fly.io, Railway, Render, Cloudflare, Netlify, Heroku, Cloud Run, or container PaaS.
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 workflow automation with n8n, Zapier/Make, Temporal, Trigger.dev, Inngest. Use when choosing platforms, durable execution, or approval gates; not job queues or 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。
Applies planning/search theory (A*, CSP, MCTS, PDDL, HTN) to agents. Use when ordering tool calls, enforcing preconditions before side effects, or validating a 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。
Analyzes hazards in systems or AI agents: STPA, guardrails, safety cases, incident analysis. Use when an agent can take actions or send payments.
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 team Git workflows for branching, PRs, and releases. Use when choosing branching models, stacked PRs, merge queues, worktree isolation for agents, or collaboration 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。
Designs coding-agent observability and evals. Use when measuring traces, replay, checkpoint lineage, tool grading, regression, or cost. Not for general LLM evals (ai-evals).
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 and runs agent hives and multi-agent waves. Use when setting hive roles, code gates, dependency-graph fan-out, or verifier, vote and loop-until-dry passes.
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 agent architecture, graph and loop composition, protocol choice, evaluation, and observability. Use when scoping or reviewing systems before implementation.
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 coding-agent settings and fleet CLI version control. Use when diagnosing precedence, managed policy, version pins, update kill switch, catalog budgets, or searchable notes.
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。
Adds per-skill learnings loops for dated patterns, mistakes, and domain facts. Use when wiring skill memory, consolidation, or drift audits.
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。