「autonomous」の検索結果

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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Manage persistent coding sessions across Claude Code, Codex, Antigravity (agy), Cursor, and OpenCode engines. Use when orchestrating multi-engine coding agents, starting/sending/stopping sessions, running multi-agent council collaborations, cross-session messaging, ultraplan deep planning, ultrareview parallel code review, autoloop autonomous workspace iteration, ultraapp building deployable web apps from a structured Q&A interview, switching models/tools at runtime, exposing the orchestrator's 69 tools as an MCP server to Hermes Agent / Claude Desktop / Cursor / Cline / Continue / Zed / Windsurf / Goose, or running as an Agent Client Protocol (ACP) agent that Zed / JetBrains / Neovim / Emacs / VS Code / dsh can drive directly. Triggers on "start a session", "send to session", "run council", "ultraplan", "ultrareview", "autoloop", "ultraapp", "Forge tab", "build a web app", "one-click app", "AppSpec", "autonomous iteration", "iterate until goal", "deep paper review", "auto research", "switch model", "multi-agent", "coding session", "session inbox", "cursor agent", "opencode", "mcp server", "clawo-mcp", "hermes mcp", "model context protocol", "ultracode", "dynamic workflow", "fanout", "fan-out", "best-of-N", "steer turn", "interrupt turn", "fork thread", "rollback turns", "acp", "agent client protocol", "clawo acp", "zed agent", "jetbrains agent", "external agent", "dsh subagent", "deepseek harness".

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Multi-agent orchestration framework for autonomous AI collaboration. Use when building teams of specialized agents working together on complex tasks, when you need role-based agent collaboration with memory, or for production workflows requiring sequential/hierarchical execution. Built without LangChain dependencies for lean, fast execution.

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Let AKA autonomously add, run, inspect, and revise intra-kernel timeline probes for standalone CUDA/inline PTX or CuTe DSL when ordinary benchmark, NSYS, or NCU evidence cannot answer a specific kernel-internal timing question.

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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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AgentFlow workflow skill. Use this skill when the user needs Orchestrate autonomous AI development pipelines through your Kanban board (Asana, GitHub Projects, Linear). Manages multi-worker Claude Code dispatch, deterministic quality gates, adversarial review, per-task cost tracking, and crash-proof pipeline execution and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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AI Agents Architect workflow skill. Use this skill when the user needs Expert in designing and building autonomous AI agents. Masters tool and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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AgentFolio workflow skill. Use this skill when the user needs Skill for discovering and researching autonomous AI agents, tools, and ecosystems using the AgentFolio directory and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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AI Agent Development Workflow workflow skill. Use this skill when the user needs AI agent development workflow for building autonomous agents, multi-agent systems, and agent orchestration with CrewAI, LangGraph, and custom agents and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

日本語の概要は準備中です。原文の説明を表示しています。

AI Agents Architect workflow skill. Use this skill when the user needs Expert in designing and building autonomous AI agents. Masters tool and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

日本語の概要は準備中です。原文の説明を表示しています。

AI Agents Architect workflow skill. Use this skill when the user needs Expert in designing and building autonomous AI agents. Masters tool and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

日本語の概要は準備中です。原文の説明を表示しています。

AgentFolio workflow skill. Use this skill when the user needs Skill for discovering and researching autonomous AI agents, tools, and ecosystems using the AgentFolio directory and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

日本語の概要は準備中です。原文の説明を表示しています。

AI Agent Development Workflow workflow skill. Use this skill when the user needs AI agent development workflow for building autonomous agents, multi-agent systems, and agent orchestration with CrewAI, LangGraph, and custom agents and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

日本語の概要は準備中です。原文の説明を表示しています。

Orchestrates zero-to-running-app in 8 stages — prerequisites → context → provision → model → load → explore → query → build. Each stage reads its own reference file. Supports HITL and fully autonomous operation. Use when starting a new Neo4j project from scratch, provisioning Aura, generating synthetic data, building a notebook or app, or running the full onboarding pipeline. Time budget ≤15 min autonomous, ≤90 min HITL. Does NOT cover Cypher query authoring — use neo4j-cypher-skill. Does NOT cover driver upgrades or Cypher migration — use neo4j-migration-skill. Does NOT cover CLI/admin tasks on an existing DB — use neo4j-cli-tools-skill.

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Rigorous engineering orchestrator ported from Lauren Tan's pstack (poteto-mode): reads your task, picks one of 23 playbooks (bug fix, feature, refactoring, perf, investigation, prototype, babysit, shipping, autonomous run, orchestrate, and more), routes to bundled procedures (how, why, architect, arena, swarm, interrogate, unslop, technical-writing, show-me-your-work, tdd, and others), and applies 24 engineering principles. Self-contained: no plugin install, no sibling skills required, works with any agent that reads skills.sh-format SKILL.md files. Use whenever a task needs rigor: nontrivial code changes, architecture decisions, debugging, reviews, PRs, long autonomous runs, or "work like poteto", "poteto-mode", "pstack".

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Run all remaining phases autonomously — discuss→plan→execute per phase

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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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Expert guide for multi-pass autonomous AI code self-review — syntax validation, logic correctness, architectural conformance, security audit, and performance analysis without external tooling — enabling the agent to catch its own errors before presenting code / Panduan ahli review kode otonom multi-pass oleh AI — validasi sintaks, kebenaran logika, konformitas arsitektur, audit keamanan, dan analisis performa tanpa tooling eksternal — memungkinkan agen menangkap errornya sendiri sebelum menampilkan kode.

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Expert guide for autonomous API schema drift detection, OpenAPI / gRPC breaking change monitoring, self-healing SDK adapter generation, and zero-downtime client-server synchronization / Panduan ahli deteksi drift skema API otonom, pemantauan breaking change OpenAPI / gRPC, pembuatan adapter SDK self-healing, dan sinkronisasi client-server tanpa downtime.

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Empowers the agent to autonomously run tests, read terminal stack traces, and self-heal code until tests pass. Transforms the agent from a passive coder to an active CI pipeline debugger.

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ALU execution pattern for ephemeral autonomous work. Detects hooked work, enters GUPP autonomous mode, executes the assigned bead, commits and pushes results, then self-terminates. Never coordinates other agents.

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Patterns and architectures for autonomous Claude Code loops — from simple sequential pipelines to RFC-driven multi-agent DAG systems.

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Expert in designing and building autonomous AI agents. Masters tool use, memory systems, planning strategies, and multi-agent orchestration. Use when: build agent, AI agent, autonomous agent, tool use, function calling.

日本語の概要は準備中です。原文の説明を表示しています。