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概要と使いどころ
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).
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Use when you need Codex to coordinate multiple agents through Agent Relay for peer-to-peer messaging, lead/worker handoffs, or shared status tracking across sub-agents and terminals.
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。
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。
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。
Authoring and managing Agentforce agent definitions using the declarative Agent Script DSL (.agent files) and associated metadata types, including the Agent Script language for the new Agentforce Builder (hybrid-reasoning -> logic vs | prompt instructions, @actions/@subagent/@variables references, subagents). Use when creating agents in source control, writing or debugging Agent Script, debugging agent metadata, or understanding the metadata lifecycle of GenAiPlugin/GenAiPlanner/BotVersion types. Triggers: 'how do I deploy an Agentforce agent using source control', 'convert an agent topic to a subagent', 'what metadata types make up an Agentforce agent', 'agent test run command failing in CI pipeline', 'GenAiPlugin vs GenAiPlanner metadata relationship', 'Agent Script logic vs prompt instructions'. NOT for Apex-based agent actions — use agentforce/custom-agent-actions-apex. NOT for clicking the agent together in the Builder UI — use agentforce/agentforce-agent-creation.
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。
Persistent memory patterns for moflo agents — session memory, long-term knowledge, pattern learning, and cross-session context via moflo's node:sqlite + HNSW vector store. Use when building stateful agents or assistants that need to remember across runs.
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。
Adaptive learning for moflo agents via ReasoningBank: trajectory storage, verdict judgment, memory distillation, consolidation, and MMR retrieval. Use when building agents that should improve from experience across runs.
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。
Initialize the UniKit framework in a project. Scans the current project to detect the engine and real tech stack, then generates the base framework documents that give AI agents context about the project — the resolved project description, the architecture doc, AGENTS.md, and the user-editable .unikit/config.yaml. This is the one-time, whole-project setup run before any other unikit skill. Use whenever the user wants to initialize, set up, or onboard the framework, e.g. "initialize unikit", "init the framework", "set up unikit", "set up the project", "configure AI context", "scan my project and set it up", "bootstrap the project". It also actualizes the config of a project already set up — say "update the config", "add missing settings", "actualize configuration", "fix my config".
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 Mastra framework guide. Teaches how to find current documentation, verify API signatures, and build agents and workflows. Covers documentation lookup strategies (embedded docs, remote docs), core concepts (agents vs workflows, tools, memory, RAG), TypeScript requirements, and common patterns. Use this skill for all Mastra development to ensure you're using current APIs from the installed version or latest documentation.
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 the MoltBot A2A Hub — register agents, search the registry, relay messages, and stream responses. Use when working with the A2A agent-to-agent protocol hub deployed at a2a-hub.fly.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。
Two superpowers for AI agents: a collective brain and a Base ecosystem mentor. BRAIN: Before debugging/compiling/architecting, search for existing solutions. After solving, propose so no agent repeats your mistake. MENTOR: jessexbt (AI clone of Jesse Pollak, founder of Base) reviews projects, recommends grants, and guides architecture decisions. Consult him directly when building on Base/crypto/onchain/web3. Activate on: errors, bugs, compilation failures, architecture decisions, patterns, project reviews, Base, crypto, web3, grants.
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 allows agents to securely access and manage secrets using the 1Password CLI (op) and a Service Account. It provides commands for reading, writing, and managing items within a dedicated v...
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。
Connect AI agents to the 2020117 decentralized network. Register, post to timeline, trade compute via NIP-90 DVM jobs (text generation, translation, summarization, image/video/speech), pay with Lightning, build reputation through Nostr zaps and Web of Trust. Use when building or operating AI agents that need to communicate, exchange capabilities, or transact on an open protocol.
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 scoring scale for evaluating how well a CLI is designed for AI agents, based on the "Rewrite Your CLI for AI Agents" principles.
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。
Expert in designing and building autonomous AI agents. Masters tool use, memory systems, planning strategies, and multi-agent orchestration.
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 development workflow for building autonomous agents, multi-agent systems, and agent orchestration with CrewAI, LangGraph, and custom 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。
Identify independent failures and spawn concurrent agents. Turns sequential N×time into 1×time.
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 structures and algorithms for AI agent episodic memory. Covers vector stores (HNSW, IVF, PQ), temporal indexing, knowledge graphs with triple stores, hierarchical summarization, forgetting curves, working/long-term/ procedural memory, and memory consolidation. Deep analysis of MemGPT/Letta, Zep/Graphiti, Mem0, and the Stanford generative agents memory architecture. Teaches the CS fundamentals behind how agents remember, retrieve, and forget. Activate on: "agent memory", "episodic memory", "vector search algorithm", "HNSW", "memory retrieval", "forgetting curve", "knowledge graph memory", "MemGPT", "Letta", "Zep", "Mem0", "memory consolidation", "temporal retrieval", "agent long-term memory", "memory layer". NOT for: conversation protocol design (use agent-conversation-protocols), agent infrastructure selection (use agentic-infrastructure-2026), building RAG pipelines (use ai-engineer).
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。
Framework for coordinating meaning across autonomous agents that use different conceptual models, vocabularies, and knowledge representations. Treats ontologies as first-class negotiable resources rather than hardcoded implementation details.
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。
BDI (Beliefs-Desires-Intentions) agency framework for designing autonomous agents with mental state architectures
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。
Design patterns for BDI agents using the MORA methodology for practical multi-agent system development
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。
Meta-agent for creating new custom agents, skills, and MCP integrations. Expert in agent design, MCP development, skill architecture, and rapid prototyping. Activate on 'create agent', 'new skill', 'MCP server', 'custom tool', 'agent design'. NOT for using existing agents (invoke them directly), general coding (use language-specific skills), or infrastructure setup (use deployment-engineer).
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。