“age”的搜索结果

Drive the AgentWiki knowledge platform via its CLI (`@aiagentwiki/cli`, command `agentwiki`) and MCP server (`https://api.agentwiki.cc/mcp`). Use this skill whenever the user mentions AgentWiki, agentwiki.cc, app.agentwiki.cc, the `agentwiki` CLI, the `@aiagentwiki/cli` npm package, the AgentWiki MCP server, or asks to create/list/search/share/publish documents, manage folders or tags, upload files or images, deploy static sites, traverse a knowledge graph, or query their AgentWiki workspace from the terminal or as an AI agent. Triggers include phrases like "agentwiki login", "aw_… API key", "create a doc on agentwiki", "search my wiki", "publish to agentwiki", "agentwiki MCP", or any task that would otherwise require opening app.agentwiki.cc manually.

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帮助为Claude Code插件创建独立执行多步骤任务的专用智能体，明确启动条件、职责、可用工具和输出格式，并指导验证配置及实际运行情况。

• 创建专门负责代码审查的智能体
• 完善启动条件和使用示例
• 将工具权限限制在必要范围

Build AI agents that make phone calls and send or receive SMS through the AgentPhone API. Manage phone numbers, configure voice agents and event notifications, and inspect call transcripts and account usage.

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• Create an AI voice agent with a phone number
• Send and receive SMS through AgentPhone
• Review call transcripts and account usage

Plan and measure defensive deception operations with MITRE Engage. Connect decoy systems and credentials to specific attacker techniques, define measurable objectives, and document operating boundaries, response procedures, and criteria for assessing results.

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• Giving existing honeypots a coordinated strategy
• Selecting decoys for a specific threat actor
• Writing an adversary engagement operation plan

Investigate 403 and 500 errors in Google Cloud Gemini Enterprise Agent Platform. Examine logs, registered destinations, agent identities, and authorization policies to produce a diagnostic report with evidence, recommended fixes, and verification steps.

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• Investigating 403 errors when an agent calls an external API
• Diagnosing Agent Runtime 500 errors involving Model Armor
• Checking registration and permissions for failing MCP connections

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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• Building a Java agent connected to a language model
• Coordinating agents in sequential or parallel workflows
• Connecting external tools through MCP

Plan or audit large sets of pages built from templates and data, such as location or product comparison pages. Evaluate each page’s distinct value, duplicate content risks, internal links, and whether it should appear in search results.

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• Planning location pages for a service
• Designing product comparison page templates
• Investigating why generated pages are not indexed

Deploy and operate AI agents with AWS Bedrock AgentCore. Choose services for hosting agents, connecting external APIs, managing credentials and conversation state, monitoring activity, and assessing agent quality through automated evaluations.

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• Plan an agent deployment on AgentCore
• Expose a REST API as an agent tool through MCP
• Configure credentials and access controls

Analyze a codebase to create project-specific AGENTS.md instructions and supporting rule files. Detect the package manager and relevant conventions, preview proposed changes, or update existing guidance when the project’s tools change.

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• Create agent instructions tailored to an existing project
• Generate a minimal AGENTS.md file
• Refresh instructions after changing development tools

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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• Create a user identity linked to an existing agent identity
• Prepare an agent account for a mailbox and Teams
• Assign a manager for organizational chart visibility

Design autonomous agents for Claude Code plugins, including when they should run, what tools they may use, and how they should respond. Provides configuration templates, prompt guidance, and validation steps for creating agent files.

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• Create a code review agent
• Refine examples that trigger an agent
• Limit the tools an agent can use

Create a dedicated test project linked to the current worktree and check Agent Builder through APIs and browser interactions. It covers agent and skill management, permissions, defaults, and related features, with selectable test sections.

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• Validate an Agent Builder feature branch before review
• Check role-based permissions and authentication
• Test agent and skill management through APIs and the UI

Diagnoses storage failures in GKE, Google's managed Kubernetes service, including stuck Pods, disk attachment errors, storage pressure, and missing volume expansion. It provides diagnostic commands and reviewable configuration patches for deployment through GitOps.

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• Diagnose Pods stuck in ContainerCreating
• Investigate slow disks and node storage pressure
• Find why expanded storage is missing inside a container

Collect structured Google Images search results through the BrowserAct API. Set keywords, country, language, scrolling, and result limits to obtain titles, thumbnail URLs, source names, source logos, and destination links for visual research.

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• Find visual references for a topic
• Research competitor product imagery
• Compare image results across countries and languages

Prepare an existing OpenClaw setup for import as an AgentTeams managed Worker. Analyze tool dependencies, adapt role instructions and scheduled jobs, and build a ZIP package containing the configuration and custom image requirements.

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• Moving standalone OpenClaw into AgentTeams
• Reviewing required tool dependencies
• Adapting role instructions for a Worker

Single entry point for every Agentlas command: /agentlas network, /agentlas build, /agentlas storm, /agentlas graph, /agentlas one, /agentlas upload, /agentlas cloud, /agentlas hub, /agentlas local, /agentlas browser, /agentlas call, /agentlas search, /agentlas connect, /agentlas login, /agentlas orch, /agentlas update.

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Generate AI images with GPT-Image-2, FLUX, Gemini, Grok, Seedream, Reve and 50+ models via inference.sh CLI. Models: GPT-Image-2, FLUX Dev LoRA, FLUX.2 Klein LoRA, Gemini 3 Pro Image, Grok Imagine, Seedream 4.5, Reve, ImagineArt. Capabilities: text-to-image, image-to-image, inpainting, LoRA, image editing, upscaling, text rendering. Use for: AI art, product mockups, concept art, social media graphics, marketing visuals, illustrations. Triggers: flux, image generation, ai image, text to image, stable diffusion, generate image, ai art, midjourney alternative, dall-e alternative, text2img, t2i, image generator, ai picture, create image with ai, generative ai, ai illustration, grok image, gemini image, gpt image, openai image, chatgpt image

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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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Data structures and serialization formats for agent-to-agent communication. Covers message envelopes, structured output schemas, capability declarations, task handoff payloads, error/retry signaling, and context windows as data structures. Deep comparison of A2A protocol, MCP, OpenAI function calling, and LangChain message types. Teaches when to use rigid schemas vs free-form with validation, typed vs untyped, streaming vs batch. Activate on: "agent message format", "agent communication schema", "agent-to-agent protocol", "A2A protocol", "MCP message format", "structured output for agents", "agent interop", "interchange format", "agent serialization", "task handoff format", "capability declaration". NOT for: what agents say to each other (use agent-conversation-protocols), orchestration topology (use multi-agent-coordination), building agent infrastructure (use agentic-infrastructure-2026).

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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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Build production-grade AI agents from scratch using patterns inspired by production-grade agentic systems like Claude Code. Covers two audiences — (1) workflow builders who need planner+executor pipelines, human-in-the-loop checkpoints, and multi-agent orchestration, and (2) application embedders who need HTTP/SSE APIs, multi-user session management, and React frontend integration. Use when asked to build an AI agent, create a coding assistant, embed an agent in an app, build an API agent, clone Claude Code, build a CLI agent, design an agent architecture, scaffold an agentic system, or automate a multi-step workflow. Triggers on keywords like agent builder, agent blueprint, coding assistant, CLI agent, agentic tool, claude code clone, agent scaffold, agent architecture, workflow automation, multi-agent, planner executor, SSE streaming, agent API.

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使用AgentDB为AI智能体实现会话历史、用户偏好和有效交互模式的保存与检索，分别管理短期和长期记忆，让后续会话能够继续引用。

• 为聊天系统添加会话历史
• 跨会话记住用户偏好
• 复用成功的交互模式

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。

• 减少AgentDB内存占用
• 兼顾精度与搜索速度
• 规划数百万向量的配置

Create and update prompt-based agents in Azure AI Foundry from a local JSON manifest, a file listing agent definitions. The skill prepares synchronization scripts, pushes instructions to the service, and verifies registered agents.

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• Registering multiple agents from JSON definitions
• Synchronizing changes to agent instructions
• Preparing a manifest and synchronization script