6.7천건 · 관련도순
Claude Code の SubAgent（agents/*.md）を正しく定義するためのベストプラクティスガイド。 YAML frontmatter、ツール選択、3-Phase 構造、コンテキスト受け渡し、アンチパターンを網羅。 Use when: agents/*.md を書く、SubAgent 定義を改善する、エージェントの動作が想定外、 コンテキストが渡らない、ツール選択に迷う。 Triggers: "subagent", "agent definition", "agents/*.md", "エージェント定義", "サブエージェント", "3-Phase", "context passing", "コンテキスト渡し", "tool selection", "ツール選択", "subagent_type", "bypassPermissions"
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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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Generates a complete AGENTS.md at the repository root following the agents.md format: setup, build, test, style, and PR conventions coding agents need. Use when a repo has no AGENTS.md, when onboarding coding agents to a codebase, or when the existing agent instructions are stale.
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Azure AI Agents Persistent SDK for Java. Low-level SDK for creating and managing AI agents with threads, messages, runs, and tools. Triggers: "PersistentAgentsClient", "persistent agents java", "agent threads java", "agent runs java", "streaming agents java".
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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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Configures AI agents (GitHub Copilot, Claude Code, Cursor, Codex) to understand a codebase and self-improve as the project evolves. Use when setting up a new project for AI-assisted development, onboarding AI agents to an existing repo, creating AGENTS.md, configuring MCP servers for code navigation, packaging project workflows as agent skills, or establishing self-improvement feedback loops. Covers AGENTS.md authoring, skill packaging, MCP configuration, context management, and living-documentation strategies.
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Meta-agent skill for orchestrating complex tasks through autonomous sub-agents. Decomposes macro tasks into subtasks, spawns specialized sub-agents with dynamically generated SKILL.md files, coordinates file-based communication, consolidates results, and dissolves agents upon completion. MANDATORY TRIGGERS: orchestrate, multi-agent, decompose task, spawn agents, sub-agents, parallel agents, agent coordination, task breakdown, meta-agent, agent factory, delegate tasks
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장기 프로젝트의 기존 문서를 규칙, 구조, 현황, 결정 이력으로 정리합니다. 에이전트의 참조 경로와 갱신 규칙을 마련해 문서 불일치와 삭제한 파일의 재생성을 방지하도록 돕습니다.
실제 개발 과제로 여러 코딩 에이전트를 비교합니다. 성공률, 확인 가능한 API 비용, 소요 시간과 반복 실행의 안정성을 측정해 도입 결정과 업데이트 후 평가에 활용합니다.
Organize complex work across specialized agents with claude-flow. Start and stop agents, check their status and metrics, and manage groups by role, using shared memory to coordinate tasks such as development, reviews, and security checks.
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Coordinates a named team of Codex workers with concrete responsibilities and shared progress records. It selects available collaboration tools, manages communication and separate working copies when needed, and handles integration, team archival, and cleanup after the work ends.
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Create container-based hosted agents in Azure AI Foundry using Python and the Azure AI Projects SDK. Follow examples for resource settings, authentication, version management, and checks needed before treating a deployment as healthy.
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Plan and build AI agents through selected stages of design, implementation, tool integration, memory, and evaluation. The workflow points to specialized skills for single agents, coordinated agent teams, and stateful workflows using CrewAI or LangGraph.
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Design AI agents that use tools, plan tasks, retain relevant context, and recover from failures. Choose execution patterns, permission boundaries, memory handling, and runtime limits, and assess whether coordinating multiple agents is justified for the task.
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Create, improve, or audit CLAUDE.md files that introduce AI agents to a project. Review project structure and development commands, keep shared instructions concise, and move detailed guidance into referenced documents, with drafts reviewed before changes are applied.
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Create, improve, or audit CLAUDE.md files that explain a project's purpose, structure, and development workflow to AI agents. Organize essential instructions, remove redundant content, and link to detailed documents where needed.
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Create, improve, or audit CLAUDE.md files that explain a project's purpose, structure, and development workflow to AI agents. Organize essential instructions, remove redundant content, and link to detailed documents where needed.
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Organize feature development across multiple agents by dividing work, assigning one owner per file, and agreeing on interfaces before implementation. Provides strategies for shared files, branches, and integration so agents can build separate parts of the same system.
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Generate coding agent instruction files from a project's actual code using AgentRC. Capture detected languages, frameworks, and conventions in one shared file or split rules by topic and project area, with a preview before writing.
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Consult another agent for specialist advice, reviews, or workspace context through QwenPaw commands. Keep follow-up conversations connected using session identifiers, and submit longer tasks in the background while continuing other work and checking results later.
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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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Connect agentmemory to coding agents such as Claude Code and Codex while preserving existing server settings. It guides configuration, host restarts, and checks that memory tools are available and can save and retrieve information.
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Design AI agents and chatbots in n8n, choosing suitable nodes and connecting models, tools, conversation memory, and structured JSON output. Includes guidance on document retrieval and human approval before tool execution.
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Plan and build AI agents on Google Cloud, from gathering requirements to choosing a single-agent or multi-agent design. Produce architecture diagrams, deployment code and instructions, and validation plans tailored to the workload.
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