'autonomous-agent' 검색 결과

Autonomous Agents workflow skill. Use this skill when the user needs Autonomous agents are AI systems that can independently decompose and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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🕹️ Autonomous Agent Patterns workflow skill. Use this skill when the user needs Design patterns for building autonomous coding agents, inspired by Cline and OpenAI Codex and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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Autonomous Agents workflow skill. Use this skill when the user needs Autonomous agents are AI systems that can independently decompose and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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🕹️ Autonomous Agent Patterns workflow skill. Use this skill when the user needs Design patterns for building autonomous coding agents, inspired by Cline and OpenAI Codex and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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Expert en orchestration d'agents autonomes (CrewAI, AutoGen, LangGraph, swarm patterns, planning)

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Autonomous Agents Specialist IA — Expert en agents autonomes (AutoGPT, CrewAI, LangGraph, planification multi-étapes, tool use)

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やりたいことを1行で言うと、B 型 loop engineering の 7 要素 (context / feedback / verification gate / termination / error handling / state / cost guard) を埋めた loop spec をコピペ可能な形で生成する。生成した spec は現行 Claude Code の実行プリミティブ (Workflow ツール / /loop skill / ralph-loop plugin / claude -p) に落とせる骨格になる (旧 `claude --goal` は廃止)。10 件 failure mode (F1-F10) の対策フックも自動挿入。Hybrid モード: 1 行入力 → デフォルト埋めで一発出力 → 「ここを調整したい」場所だけ短く聞き直す。Use when ユーザーが「loop を作りたい / loop-design / 自走ループ書いて / Workflow 用の spec 作って / 7 要素埋めて / verification gate ありで」と言ったとき、または `/loop-design` を実行したとき。Loop engineering の理論背景は Cards/loop-engineering-autonomous-agents、失敗モード対策は Cards/loop-failure-modes-mechanism-2026 を参照。

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Claude Code의 기억, 정기 실행, 작업 대기열을 결합해 지속적인 작업을 설계합니다. 외부 실행 환경과 화면 조작 연동 설정도 안내합니다.

• 풀 리퀘스트를 정기적으로 확인할 때
• 매일 조사 결과를 모아 요약할 때
• 기억한 맥락으로 회의를 준비할 때

Design autonomous coding agents with patterns inspired by Cline and OpenAI Codex. Explore tool calling interfaces, permission and approval systems, browser automation, and workflows where people review or guide the agent's actions.

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• Planning an autonomous coding agent
• Designing tool and function calling interfaces
• Defining permissions and approval steps

Design AI agents that break down goals, plan actions, use tools, and correct their own mistakes. The guidance focuses on reliable execution, clear operating boundaries, and managing conversation context as tasks grow longer.

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• Designing an agent's planning and execution loop
• Breaking a complex goal into manageable tasks
• Reviewing self-correction and reliability patterns

Design autonomous coding agents using implementation patterns for tool calls, permission checks, browser interaction, and saved task state. Code examples and checklists help structure agent behavior and workflows that include human approval.

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• Designing a coding agent's execution loop
• Defining tool interfaces for files and commands
• Adding permissions and human approval steps

Design autonomous AI agents that break goals into tasks, use tools, and review their own work. Covers reasoning and action loops, separate planning and execution, and reliability measures such as cost limits, validation, and logging.

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• Choosing how an agent plans and executes tasks
• Breaking goals into constrained steps
• Adding self-review and correction to an agent

Create and manage prediction markets on Solana, where participants trade YES or NO outcome tokens. Support includes direct peer bets, social media metrics, custom result authorities, market settlement, and redemption of winning positions.

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• Create a prediction market around social media metrics
• Build direct peer-to-peer betting markets
• Let an AI agent determine market outcomes

Design patterns for building autonomous coding agents, inspired by [Cline](https://github.com/cline/cline) and [OpenAI Codex](https://github.com/openai/codex).

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Autonomous agents are AI systems that can independently decompose goals, plan actions, execute tools, and self-correct without constant human guidance. The challenge isn't making them capable - it's making them reliable. Every extra decision multiplies failure probability.

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You are an expert in Cline, the autonomous AI coding agent for VS Code that can read files, write code, run terminal commands, and use the browser — with human-in-the-loop approval at each step. You help developers use Cline for complex multi-file refactoring, feature implementation, debugging, and codebase exploration where the AI plans and executes while the developer reviews and approves.

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Autonomous agents are AI systems that can independently decompose goals, plan actions, execute tools, and self-correct without constant human guidance. The challenge isn't making them capable - it's making them reliable. Every extra decision multiplies failure probability. This skill covers agent loops (ReAct, Plan-Execute), goal decomposition, reflection patterns, and production reliability. Key insight: compounding error rates kill autonomous agents. A 95% success rate per step drops to 60% by step 10. Build for reliability first, autonomy second. 2025 lesson: The winners are constrained, domain-specific agents with clear boundaries, not "autonomous everything." Treat AI outputs as proposals, not truth. Use when "autonomous agent, autogpt, babyagi, self-prompting, goal decomposition, react pattern, agent loop, self-correcting agent, reflection agent, langgraph, agentic ai, agent planning, autonomous, agents, langgraph, react, planning, reflection, guardrails, reliability, checkpointing" mentioned.

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Dispute resolution, forensic investigation, and risk assessment for autonomous AI agent transactions. Reconstruct provenance chains, adjudicate fault, generate actuarial risk profiles for agent insurance. The accountability layer of the Agent Trust Stack.

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Manage an engagement buffer that ingests, prioritizes, rate-limits, deduplicates, and tracks state for incoming engagement items across platforms. Generates periodic digests and enforces cooldown periods. Composes with du-dum: du-dum sets the observation/action cadence, this skill manages the queue between beats.

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Separate expensive observation from cheap decision-making in autonomous agent loops using a two-clock architecture. A fast clock accumulates data into a digest file; a slow clock reads the digest and acts only when something is pending. Idle cycles cost nothing because the action clock returns immediately after reading an empty digest. Use when building autonomous agents that must observe continuously but can only afford to act occasionally, when API or LLM costs dominate and most cycles have nothing to do, when designing cron-based agent architectures with observation and action phases, or when an existing heartbeat loop is too expensive because it calls the LLM on every tick.

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A pattern catalog for autonomous agents — ReAct, plan-and-execute, reflection, multi-agent debate, toolformer-style tool use, and when each fits. Use when choosing the control pattern for an agent task.

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Build agents that work toward goals with minimal supervision — goal decomposition, self-correction loops, safe autonomy levels, and long-horizon task management. Use when an agent must run for hours or complete open-ended tasks.

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Transform Claude Code into a fully autonomous agent system with persistent memory, scheduled operations, computer use, and task queuing. Replaces standalone agent frameworks (Hermes, AutoGPT) by leveraging Claude Code's native crons, dispatch, MCP tools, and memory. Use when the user wants continuous autonomous operation, scheduled tasks, or a self-directing agent loop.

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Apply normative BDI reasoning to agents that must detect norms, choose which commitments to internalize, and resolve conflicts by comparing consequences. Use when obligations, prohibitions, or policies collide in autonomous systems. NOT for simple fixed-priority rules, pure constraint satisfaction, or domains where no real normative conflict exists.

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