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Automate Agenty tasks through Rube MCP, a connection to external tools. The skill discovers available operations and their required inputs, checks the Agenty connection, and executes tasks using the tools it finds.
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Create an agent user in Microsoft Entra ID so an AI agent can access services requiring a user identity. Covers validating the parent agent identity, creating the account, assigning a manager and licenses, and troubleshooting setup errors.
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Manage multiple AI agents through the AI Maestro command-line tool. Create agents, inspect their status, pause and resume work, rename them, export or import configurations, and manage plugins from one interface.
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Create specialized Agent Zero profiles by defining their roles, working methods, and delegation criteria. The skill prepares configuration files and instructions, with optional custom tools, extensions, and model preset selection when needed.
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Automate Agenty tasks through Rube MCP, a connection to external tools. The skill discovers available operations and their required inputs, checks the Agenty connection, and executes tasks using the tools it finds.
中文简介正在准备中。当前显示英语简介。
Build Java applications with cooperating AI agents using AgentScope Java. Guides asynchronous processing, where waiting tasks do not block execution, plus language model connections, external tools, workflow coordination, error handling, and tests.
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Build VeADK Agent code from functional requirements, including agent structure and prompt refinement. Convert existing Langchain or Langgraph code, or a Dify workflow, into VeADK Agent code and save the generated Python files.
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Generate VeADK-Go Agent code from a description of the required functionality, or convert existing Enio Agent code using the supplied framework documentation, examples, and feature mapping rules.
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Build Python agents with Agentica SDK using function decorators or agent instances. Examples cover conversation memory, external tools, MCP connections for tool access, multi-agent coordination, typed results, and logging to help implement and debug agent behavior.
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Test how AI agents get started with a product, SDK, or skill by having them attempt real tasks independently. Receive an HTML report with execution records, scores, and concrete suggestions for improving setup and documentation.
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Builds Agent2Agent (A2A) servers and clients, the open protocol (originally from Google, now under the Linux Foundation) that lets AI agents from different frameworks call each other. Use when the user wants to create an A2A-compliant agent, build an Agent Card, implement task management, connect agents across frameworks, set up agent discovery, handle streaming responses, implement push notifications, or orchestrate multi-agent workflows. Trigger words: a2a, agent to agent, agent2agent, a2a protocol, a2a server, a2a client, agent card, agent interoperability, agent collaboration, multi-agent, agent discovery, a2a sdk, a2a task.
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Use when: AI teammate / AIチームメイト / AI チームメイト / Agent Template / Foundry Autopilot / Agent 365 / 同僚エージェントの開発・公開。既定は Foundry hosted agent を Agent template として発行する経路。publish_foundry_autopilot.py が必須で、汎用 scripts/deploy.py や SDK agents.create_version だけで完了にしない。明示的な Agent Template / Autopilot 要求の前提が不足したら停止し、通常 Agent・自己ホストへの自動フォールバックは禁止。明示選択の自己ホストは別経路として対応する。M365 Requests の Type=Agent template と Teams Agents for your team での hire を手動確認するまで未完了。初回 scaffold で評価Hub（Code Apps）・Python EvaluationWorker・回帰テスト、publish 時に Foundry Monitor/Evaluations を構成する。CI/CD・レビューゲートは alm に委譲する。
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Use when deploying an Agentforce agent to a channel surface: Embedded Service web chat, Slack, Experience Cloud, Agent REST API, or Salesforce Mobile. Triggers: 'deploy agent to website', 'add Agentforce to Slack', 'enable agent for mobile app', 'configure embedded service for agent', 'agent API for custom integration', 'multi-channel agent deployment'. NOT for agent logic design — use agentforce/agent-topic-design or agentforce/agent-actions for topics, instructions, and action contracts.
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Architecture and systems design for building always-on AI agents with episodic memory. Covers the memory hierarchy (core/recall/archival), persistence layers, agent server infrastructure, vector stores, and framework selection. Provides concrete deployment patterns for agents that maintain identity and learn across sessions. Activate on: "always-on agent", "persistent agent architecture", "episodic memory system", "agent memory design", "long-running agent", "stateful agent", "agent that remembers", "MemGPT architecture", "Letta deployment", "/always-on-agent-architecture". NOT for: choosing what data to feed the agent (use always-on-agent-inputs), brainstorming applications (use always-on-agent-applications), safety and privacy concerns (use always-on-agent-safety), general agentic patterns (use agentic-patterns).
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Cryptographic security for agentic systems — zero-trust agent networking, signed message envelopes (JWS/JWE), capability-based security (ocaps), Merkle tree audit trails, WASM sandboxing, and formal verification. Covers CLI dev tool security, mTLS between agents, permission boundaries (least privilege for AI agents), and supply chain security for skills/plugins. Activate on: "agent security", "zero trust agents", "secure agent communication", "capability-based security", "ocap", "signed messages between agents", "agent audit trail", "sandbox agent execution", "agent permissions", "mTLS agents", "cryptographic verification", "agent supply chain", "OWASP agentic", "prove agent did X", "tamper-proof agent logs". NOT for: application-level SAST scanning (use security-auditor), network firewall rules (use infrastructure), SOC2/HIPAA compliance (organizational), or prompt injection defense (use prompt-engineer).
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Improve agent reliability over time — diagnose why agents fail and fix the setup. Triggers on: agent keeps failing, same mistake again, agent not improving, make agent smarter, agent quality plateau, agents ignore skills, agent skips tests, fix agent behavior, agent unreliable, improve agent setup, self-improving harness, agents worse over time, tune agent instructions, agent going in circles, agent ignores AGENTS.md, repeated agent errors. Requires harness v0 and eval harness. AUTO-ROUTED from harness-engineering on symptoms. Not first setup — harness-generation first.
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Cryptographic security for agentic systems — zero-trust agent networking, signed message envelopes (JWS/JWE), capability-based security (ocaps), Merkle tree audit trails, WASM sandboxing, and formal verification. Covers CLI dev tool security, mTLS between agents, permission boundaries (least privilege for AI agents), and supply chain security for skills/plugins. Activate on: "agent security", "zero trust agents", "secure agent communication", "capability-based security", "ocap", "signed messages between agents", "agent audit trail", "sandbox agent execution", "agent permissions", "mTLS agents", "cryptographic verification", "agent supply chain", "OWASP agentic", "prove agent did X", "tamper-proof agent logs". NOT for: application-level SAST scanning (use security-auditor), network firewall rules (use infrastructure), SOC2/HIPAA compliance (organizational), or prompt injection defense (use prompt-engineer).
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帮助调整AgentDB的数据压缩、搜索索引、缓存和批量操作，并测量性能，以改善内存占用与搜索速度，权衡速度、精度和内存需求。
帮助调整AgentDB的数据压缩、搜索索引、缓存和批量操作，并测量性能，以改善内存占用与搜索速度，权衡速度、精度和内存需求。
Design autonomous agents for Claude Code plugins, including when they should run, what they should do, and which tools they may use. Provides file templates and guidance for validating configuration and testing behavior.
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Plan a Claude Managed Agent around one clear job, then route work through setup, launch preparation, evaluation, and scheduling. Produce execution plans and curl commands that users run with their own Anthropic API key.
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Guide AI agent development from requirements and project setup through implementation, behavior evaluation, deployment, and monitoring. Provides coding rules, debugging steps, and links to specialized skills so each phase follows a defined process.
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Entry point for AI-agent work on AWS: pick a runtime, plan a migration for existing workloads, and build an executable POC — one phased flow. Triggers on: which runtime for my agent, AgentCore vs ECS vs EKS vs Lambda, AgentCore vs Lambda MicroVMs, deploy an AI agent on AWS, agent architecture on AWS, I have an agent idea what do I build, move/migrate my agents to AWS, agent migration plan, add AgentCore services (memory, gateway, identity, policy, observability) to an agent already on AWS, Temporal on AWS (migrate/run Temporal workers on AWS, a service orchestrated by Temporal, Temporal Cloud vs self-hosted). Temporal Workflow code is never rewritten into Step Functions. Requires at least one agentic component — a purely non-agent system (plain services, batch jobs, HTTP endpoints, non-agent Temporal Activities) is out of scope, redirected to gcp-to-aws / heroku-to-aws / llm-to-bedrock. Not for: compute/data migration with no AI agent; pure LLM SDK rewrite (use llm-to-bedrock); per-model pricing.
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Use to extend an existing agent project with memory, app integration, VPC, multi-agent, migration, model, browser, code interpreter, payments, or resource removal. Triggers: "add memory", "remember across sessions", "call agent from app", "invoke agent from code", "agent auth", "streaming", "VPC", "VPC connectivity", "can't reach from VPC", "multi-agent", "A2A", "A2A auth", "orchestrator not delegating", "specialist not called", "migrate Bedrock Agent", "migration issue", "change model", "browser tool", "code interpreter", "delete agent", "tear down", "agentcore remove", "cross-account memory", "add payments capability to my agent", "wire payments plugin", "integrate x402 payments with the agent I'm building", "add MPP payments", "Machine Payments Protocol". External APIs via Gateway: use agents-connect. New project: use agents-get-started. CLI/dev-server errors: use agents-debug. Runtime x402/MPP payments: use agents-pay. Migration-specific Strands vs LangGraph routes here.
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