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概要と使いどころ
Expert in designing and building autonomous AI agents. Masters tool use, memory systems, planning strategies, and multi-agent orchestration. Use when: build agent, AI agent, autonomous agent, tool use, function calling.
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审计、修复并验证 TradingAgentsCN 中 agent 与提示词工作流。用于持仓分析、workflow prompt、MongoDB prompt_templates、adapter prompt assembly、合规修复、运行期模板与代码同步。触发词：修改 agent 提示词、提示词治理、DB 模板同步、workflow 提示词修复、持仓分析提示词排查、prompt compliance、runtime prompt audit。
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。
Standing context for Slack sibling agents — one room per team, creator-posted introductions, bot-to-bot hop discipline. Ships as instructions.md composed into the group's CLAUDE.md at spawn; there is no workflow to invoke.
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。
Migrate a classic single-bot Slack install to one provisioned Slack app per existing agent group while preserving agent identities, workspaces, memory, and wiring behavior — or record the operator's choice to stay on classic, which remains supported. Use when /update-nanoclaw surfaces the Slack agents requirement, or standalone any time later.
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 Vercel deployment capability to NanoClaw agents. Installs the Vercel CLI in agent containers and sets up OneCLI credential injection for api.vercel.com. Use when the user wants agents to deploy web applications to Vercel.
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。
Build durable backend AI agents with the eve framework. Use when creating, editing, or debugging an eve project — agent instructions, skills, tools, connections, channels, sandboxes, subagents, schedules, or 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。
Fallback authoring playbook for building general-purpose personal assistant agents that do not fit a more specific archetype. Use this only after checking the other archetype skills (coding, spreadsheet, research, customer-support, content-writer, ops-automation). Examples include summarizing emails, drafting short answers, capturing notes, or generic personal-helper 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。
Founder-mode executive team. 13 cs-* C-suite agents (CFO, CMO, CRO, CPO, COO, CHRO, CISO, GC, CDO, CAIO, CCO, VPE, Chief of Staff) and 21 /cs:* slash commands for forcing-question office hours, multi-role boardroom deliberation, strategic sprint pipeline, and meta routing. Use when the founder needs a virtual executive team, when invoking /cs:* commands, or when orchestrating multi-role decisions.
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。
Company Architect: builds a business from scratch as an OKF (Open Knowledge Format) bundle — a tree of version-controllable .md files with frontmatter type, links forming a graph, and reserved index.md/log.md, readable by humans and agents. Guides the founder through a 12-phase interview (foundation, strategy, market, financial, sales, marketing, product, operations, tech, people, legal, governance), one phase at a time, few questions per block, and generates the concepts as conformant markdown. Trigger when the user wants to create, structure, or document an entire company in folders and .md files; when they mention build my company from scratch, company as code, company knowledge base for AI to read, company wiki for agents, OKF, or knowledge bundle. In English.
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 or update a skill in this repository the right way — canonical home in .agents/skills, internal-vs-public marking, symlink mirroring into .claude/skills, and public docs registration for product skills. Use whenever adding a new skill, converting a workflow into a skill, or when .claude/skills and .agents/skills look out of sync.
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。
Onboarding entrypoint for agents-cli in Agent Platform. It should be used when the user wants to "create a new agent", "develop an agent", "build an agent using ADK", "run the agent locally", "debug agent code", "test an agent", "evaluate an agent", "deploy an agent", "publish an agent", "monitor an agent", or needs the ADK (Agent Development Kit) development lifecycle.
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, builds, and deploys AI agents or multi-agent systems on Google Cloud. Provides an interactive workflow to gather requirements, recommend a tailored architecture, and generate deployment instructions. Use when designing or implementing agentic systems on Google Cloud. Don't use for general Google Cloud solution architecture (use google-cloud-solution-architecture instead) or for narrow tasks targeting a single product without agent 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。
Discovers requirements and designs a borderless open data lakehouse using Lakehouse for Apache Iceberg and BigQuery data agents. Use when architecting multi-cloud storage infrastructure (Cloud Storage, AWS S3, Azure Blob), establishing ingestion and AI serving subsystems, configuring Cross-Cloud Interconnect, or deploying Gemini Enterprise Agent Platform and BigQuery data agents. Don't use for single-cloud data warehouses, or when the focus is on Knowledge Catalog metadata governance and Spark-driven IDE analytics workflows (use google-cloud-solution-agentic-analytics-spark-knowledge-catalog 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。
Configures best-practice alerting policies for AI agents using OpenTelemetry (OTel) metrics, generating output as Terraform (.tf) configuration files. Use when analyzing, writing, or deploying alerting policies to monitor agent latency, error rates, token usage, and quality metrics. Don't use for standard infrastructure monitoring unrelated to AI agents, or when the agent is not instrumented with OpenTelemetry (for Reliability, Cost, Safety, Security alerts). NOTE: Reliability, Cost, Safety, and Security alerts use generic OTel metrics and work across runtimes (such as Cloud Run, Vertex AI). Quality alerts rely on Vertex AI Online Monitors and are strictly bound to Vertex AI deployments.
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。
Route code, repository, and data/SQL work to isolated Kortix sessions instead of burning your main context on it. Use when asked to implement a feature, fix a bug, make failing tests pass, work a ticket/issue, refactor, navigate or change a codebase, ship a PR, review a pull request, or run SQL/warehouse analysis against a dataset — and when the user wants two agents or parallel reviewers/investigators on the same work. Covers the explore-vs-session routing call, deciding where the code lives (this project's repo vs cloning an external GitHub repo) or where the data lives, finding the repo with gh, spawning sessions in parallel, dual PR reviews, and reporting results back. Triggers: 'implement', 'fix the bug', 'make the tests pass', 'work this ticket', 'refactor this', 'open a PR', 'review this PR', 'run two agents on it', 'query the warehouse', 'analyze this dataset'.
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 and fix agent readiness: the Lighthouse Agentic Browsing fraction, accessibility tree for agents, robots.txt and Content-Signal for AI agents, WAF treatment of agent traffic, llms.txt, Markdown delivery, ai-catalog.json, /.well-known discovery files, and WebMCP tools. Exclude AI citability and brand signals (seo-geo) and commerce protocol depth (seo-ecommerce).
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 designing hosted or background agent infrastructure: sandboxed execution, remote coding environments, warm pools, session persistence, multiplayer collaboration, self-spawning agents, or Modal-style sandboxes.
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 for the tool-interface layer of an agent system specifically: writing tool descriptions agents can route on, designing tool schemas and response formats, naming conventions, actionable error recovery messages, MCP server design, tool-set consolidation, and deciding when to add or remove an individual tool. Use this when the unit of work is a single tool or a set of tools. Route project-shape, pipeline architecture, and task-model-fit decisions to project-development; route deciding whether to introduce sub-agents to multi-agent-patterns.
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 "share memory between agents", "KV cache compaction for multi-agent", "orchestrator worker context", "latent briefing", "reduce worker tokens", "cross-agent memory without summarization", or discusses Attention Matching compaction, recursive language models with workers, or token explosion in hierarchical 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。
When you need a browser, read this Skill by default. Use it to open and operate websites, fill forms, click buttons, take screenshots, extract page data, sign in, and perform other browser automation tasks, as well as web app testing, dogfooding, QA, bug investigation, and app-quality review. ego-browser (ego-lite) is a Chromium browser designed for both human users and AI Agents. Agents can use the user's logged-in websites and personal context to complete tasks and collaborate smoothly with the user through the browser interface. Therefore, prefer ego-browser over built-in browsers or other web tools.
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。
Digest of what the user's Superset agents did while they were away, sweeping workspaces, tasks, and agent terminals to report what finished, what needs review, and what's blocked. Use when the user asks "what did my agents do", "what happened while I was away", wants a standup or summary of agent work, or returns after a break.
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。
Data framework for building LLM applications with RAG. Specializes in document ingestion (300+ connectors), indexing, and querying. Features vector indices, query engines, agents, and multi-modal support. Use for document Q&A, chatbots, knowledge retrieval, or building RAG pipelines. Best for data-centric LLM 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。
Build complex AI systems with declarative programming, optimize prompts automatically, create modular RAG systems and agents with DSPy - Stanford NLP's framework for systematic LM programming
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-agent orchestration framework for autonomous AI collaboration. Use when building teams of specialized agents working together on complex tasks, when you need role-based agent collaboration with memory, or for production workflows requiring sequential/hierarchical execution. Built without LangChain dependencies for lean, fast execution.
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。