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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.
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Computer Use Agent — autonomous browser control via Steel, Browser Use, and Browserbase cloud providers. Supports headful CDP sessions, CAPTCHA solving, proxy rotation, live view, session recording/replay, Playwright/Puppeteer integration, and LLM-driven visual browsing. Use when asked about computer use, browser automation, web scraping, Steel sessions, cloud browsers, headful browsing, CAPTCHA solving, or autonomous web navigation.
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AUTONOMOUS - the world's largest summit for AI innovators — Sept 9-10 | World's Largest Summit for A
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Abbruchbedingung für autonome Loops: So weit wie möglich selbständig weiterarbeiten UND einen Loop erst beenden, wenn belegt ist, dass keine autonom ausführbare Aufgabe mehr vorliegt. Der bloße Eindruck "nichts mehr zu tun" reicht NICHT — er löst eine vierstufige Prüfkette aus (think/decide, _CONTROL/_DECISIONS, Gardener/USMC, decision-avatar/BYUM), die zuerst neue Aufgaben GEWINNEN muss, bevor der Loop enden darf. Jeder Kettenschritt meldet found/empty/unavailable statt eines binären Ergebnisses — "exhausted" gilt nur, wenn ALLE Quellen tatsächlich befragbar waren; ist mindestens eine Quelle unavailable, meldet der Skill "blind" statt "exhausted" und behauptet nichts Ungeprüftes. Nutze diesen Skill bei /work-autonomous, /waafap, "arbeite autonom weiter bis nichts mehr zu tun ist", als Goal-Bedingung innerhalb eines /loop, oder wenn geprüft werden soll, ob wirklich keine autonome Arbeit mehr übrig ist, bevor ein Loop/eine Session beendet wird.
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Einmaliger Tasksolver+Writer+Maintainer-Durchlauf fuer den Projektordner, in dem die Session gerade sowieso gearbeitet hat (inkl. Unterordner) — kein systemweiter Sweep. Loest offene triviale/autonome Register-Punkte, aktualisiert Dokumentation auf den gemessenen Ist-Stand (im Projekt UND in der Root der Pipeline, in der das Projekt liegt) und raeumt Strays/Temp- Dateien nach dem Papierkorb-Prinzip weg. Nutze diesen Skill bei /tidy-up, "raeum das Projekt auf", "mach hier sauber bevor wir weitermachen", am Ende einer Arbeitssitzung an einem Projekt, oder als faellige autonome Aufgabe innerhalb von /work-autonomous + /goal.
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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'...
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Design patterns for building autonomous coding agents. Covers tool integration, permission systems, browser automation, and human-in-the-loop workflows. Use when building AI agents, designing tool ...
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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 ...
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Autonomous build-ship-verify loop that ends with code committed, deployed, and confirmed live. Triggers on: "$ownership", "execute X with ownership", "build this overnight", or any request for an autonomous product loop with no human checkpoints until production is verified. NOT for: one-off tasks, quick fixes, or supervised dev work (use devprocess for those).
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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 en orchestration d'agents autonomes (CrewAI, AutoGen, LangGraph, swarm patterns, planning)
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Expert en véhicules autonomes avancés (perception, planning, simulation, safety, DZ regulations)
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Expert en véhicules autonomes (perception, planning, ROS, simulation, safety)
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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.
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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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You are an agent architect who has learned the hard lessons of autonomous AI. You've seen the gap between impressive demos and production disasters. You know that a 95% success rate per step means only 60% by step 10.
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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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Analyze nonlinear dynamical systems — phase portraits, fixed points, stability analysis, bifurcation diagrams, Poincare sections, Lyapunov exponents, and chaos detection. Use for any autonomous or non-autonomous ODE system where qualitative behavior matters.
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Build autonomous AI workflows.
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Writes an autonomous agent for the CARLA Leaderboard — an AutonomousAgent subclass with setup/sensors/run_step/destroy, a sensor suite inside the track's budget, and the right constructor signature for the leaderboard version (1.0 and 2.x differ). Generates a working skeleton, validates the sensor configuration against the real per-track limits offline, and covers the ROS1/ROS2 agent base classes. Use when the user asks to "write a leaderboard agent", "make my agent work with the leaderboard", "add sensors to my agent", "port my agent to 2.1", or hits SensorConfigurationInvalid.
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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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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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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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You are an agent architect who has learned the hard lessons of autonomous AI. You've seen the gap between impressive demos and production disasters. You know that a 95% success rate per step means only 60% by step 10.
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