'agents' 검색 결과

Create and synchronize prompt-based AI agents directly within Azure AI Foundry via REST API, from a local JSON manifest. Unlike scaffolding skills that only generate local code, this skill registers agents in the Foundry service itself — making them immediately available for invocation. Use when the user asks to create agents in Foundry, sync, deploy, register, or push agents to Foundry, update agent instructions, or scaffold the manifest and sync script for a new repository. Triggers: 'create agent in foundry', 'sync foundry agents', 'deploy agents to foundry', 'register agents in foundry', 'push agents', 'create foundry agent manifest', 'scaffold agent sync'.

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Scan a codebase to create project documentation and project-specific agent instructions. The first run examines the whole project; later runs focus on changed files, with human confirmation required before updating agent instructions after architectural changes.

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• Create documentation when starting agent-assisted development
• Refresh project documentation after architecture changes
• Check changed code for documentation drift

Add controls to AI agents that use APIs, databases, and files. Use Python patterns for tool permissions, approval requirements, call limits, suspicious input checks, audit logs, and trust scores that guide delegation between agents.

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• Restrict which tools an agent can call
• Check suspicious inputs before execution
• Require human approval for sensitive operations

Coordinate with other QwenPaw agents when a task needs their expertise, files, or context. Discover available agents, exchange messages, continue existing conversations, and submit longer tasks in the background while other work proceeds.

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• Ask a specialist agent for a second opinion
• Consult another agent about its files or context
• Delegate analysis or reports as background tasks

Give selected NanoClaw agents access to Dial for sending SMS, making AI voice calls, and receiving verification codes. It installs the command-line tool and uses OneCLI to supply credentials and control which agents can use it.

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• Send SMS from a chat request
• Ask an agent to place an AI voice call
• Receive phone verification codes inside an agent container

Delegate checks for AGENTS.md rule violations to a dedicated agent. It scans the codebase, fixes high-impact issues in a separate working copy, runs full validation, and creates a pull request, tracking progress across runs through memory.

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• Checking code for AGENTS.md rule violations
• Prioritizing high-impact rule violations for fixes
• Checking compliance within a chosen focus area

Create and audit the files and routines that guide coding agents across sessions. It organizes instructions, progress tracking, verification, scope boundaries, and handoffs, with scripts for setup, structural checks, and shareable assessment reports.

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• Creating AGENTS.md or CLAUDE.md and supporting files
• Preserving progress between agent sessions
• Defining checks required before work is marked complete

Build AI agents by connecting workflow blocks in AutoGPT's visual editor. This skill covers multi-step automation, scheduled runs, webhooks that start work after external events, execution monitoring, and custom agent development.

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• Build an AI workflow in a visual editor
• Start an agent when an external event occurs
• Run a multi-step workflow on a schedule

This skill should be used when the user wants to "set up tracing", "monitor my agent", "configure logging", "add observability", "debug production traffic", or needs guidance on monitoring deployed agents, including ADK (Agent Development Kit) agents. Covers Cloud Trace, prompt-response logging, BigQuery Agent Analytics, third-party integrations (AgentOps, Phoenix, MLflow, etc.), and troubleshooting. Part of the agents-cli skills suite. Do NOT use for deployment setup (use google-agents-cli-deploy) or API code patterns (use google-agents-cli-adk-code).

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This skill should be used when the user wants to "publish an agent", "publish my ADK agent", "register an agent with Gemini", "publish to Gemini" (formerly Gemini Enterprise), or needs guidance on the agents-cli publish gemini command. Also use when the user wants to "manage agents in Agent Registry", "list/update/delete registered agents", or "register an MCP server". Covers ADK vs A2A registration modes, programmatic and interactive usage, flag reference, auto-detection from deployment metadata, Agent Registry fleet management, and troubleshooting. Part of the agents-cli skills suite. Do NOT use for deployment (use google-agents-cli-deploy).

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This skill should be used when the user wants to "deploy an agent", "deploy my ADK agent", "set up CI/CD", "configure secrets", "troubleshoot a deployment", or needs guidance on Agent Runtime, Cloud Run, or GKE deployment targets, or binding an agent to an Agent Gateway. Covers deployment workflows, service accounts, rollback, and production infrastructure. Applies to any framework agents-cli deploys (ADK, LangChain, ...). Part of the agents-cli skills suite. Do NOT use for agent API code patterns (ADK: use google-agents-cli-adk-code), evaluation (use google-agents-cli-eval), or project scaffolding (use google-agents-cli-scaffold).

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Azure AI Agents Persistent SDK for .NET. Low-level SDK for creating and managing AI agents with threads, messages, runs, and tools. Use for agent CRUD, conversation threads, streaming responses, function calling, file search, and code interpreter. Triggers: "PersistentAgentsClient", "persistent agents", "agent threads", "agent runs", "streaming agents", "function calling agents .NET".

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Build AI agents with Hugging Face's SmolAgents framework. Use when creating code-executing agents, tool-calling agents, multi-agent systems, agentic RAG, text-to-SQL pipelines, web browsing agents, or any multi-step AI workflows. Covers CodeAgent, ToolCallingAgent, custom tools, MCP integration, memory management, secure code execution (E2B, Docker, Blaxel), and model configuration (HF Inference, LiteLLM, Transformers, Ollama).

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Audit Claude Code configuration with AgentShield to find exposed secrets, excessive permissions, unsafe hooks, and prompt injection risks, where malicious instructions influence an agent. Generate reports, apply supported automatic fixes, or create a security-focused starter configuration.

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• Checking configuration when starting a Claude Code project
• Auditing permission and MCP configuration changes
• Reviewing existing configuration in a newly joined repository

Design and build AI agents for business, research, operations, or creative work. Define essential actions and knowledge, start with a simple execution loop, and add progress tracking or separate child agents as needs emerge.

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• Designing an assistant for business tasks
• Building a research agent
• Choosing essential actions and knowledge

Delegate complex work through a tree of Claude Code sub-agents. Each level uses its own working context and returns concise summaries, helping the coordinating agent manage research, verification, and synthesis without retaining every intermediate detail.

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• Divide research, expansion, verification, and synthesis into layers
• Handle work with more intermediate detail than one agent can retain
• Assign specialized checks to different leaf agents

Find prebuilt WASM agents, software components packaged in WebAssembly format, through gallery browsing and keyword search. Install agents from their configurations, or export and publish your own agents to share with the community.

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• Browse available community agents
• Find agents by keyword
• Install a prebuilt agent

Create and manage persistent Azure AI agents from Java. Covers conversation threads that retain messages, agent execution, status checks, response retrieval, and cleanup, using preview SDK examples with guidance on authentication and cloud resource handling.

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• Building a Java app with reusable AI agents
• Managing conversation threads and messages
• Checking execution states and handling timeouts

Coordinate multiple local command-line agents through tmux sessions in an authorized workspace. This guide covers starting and stopping agents, assigning tasks, monitoring logs, and checking results, while requiring a separately installed and reviewed manager runtime.

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• Running multiple local agents in parallel
• Assigning tasks to configured agents
• Following agent logs and output

Install or refresh oh-my-codex for a user or a specific project. Choose installation settings, preview file changes, manage how existing instructions are updated, and check the resulting files and configuration with diagnostic commands.

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• Install oh-my-codex for a project
• Refresh an existing installation
• Preview setup changes before applying them

Manage multiple local command-line agents in separate tmux sessions. Start and stop agents, assign tasks, and follow their logs, with support for scheduling recurring work through cron. Requires tmux and python3.

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• Run multiple local command-line agents in parallel
• Assign tasks to individual agents
• Follow agent logs and monitor output

Investigate and fix agent behavior in TradingAgentsCN by checking active database prompt templates, prompt assembly code, and output processing. Apply targeted changes and verify them with focused tests, template checks, and workflow logs.

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• Investigating prompt changes that do not affect runtime behavior
• Synchronizing database templates with prompt assembly code
• Tracing incorrect dates or price references in position analysis

Use cmux on macOS to organize visible teams of AI agents, send prompts, inspect terminal output, and monitor completion. Set up role-based teams or have several agents investigate the same problem; live operation remains unproven in the supplied material.

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• Launch a team to investigate an intermittent test failure
• Have multiple agents investigate the same bug
• Send follow-up prompts to a running agent

Coordinate multiple coding agents through Superset in separate workspaces. Split work into bounded tasks, track dependencies and progress, send follow-ups, and collect structured results covering changed files, verification checks, blockers, and the order for combining work.

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• Splitting independent coding tasks across agents
• Passing completed work to dependent tasks
• Monitoring workers on local or remote hosts