Browse reusable Agent Skills, each with a clear purpose and practical guidance.
External services and backend capabilities for AbotClaw robots — vision APIs, VLM/LLM endpoints, planners, speech services, grasping backends, and client SDKs. Use when a robot skill needs an external service, when checking what backend tools already exist, when debugging service reachability, or when deciding whether to build locally versus call a service.
Bundle an AbotClaw robot skill and its dependencies into a single executable Python file or portable submission artifact. Use when preparing robot code for execution environments that want one file, when flattening dependency chains, or when packaging a skill for deployment or review.
Use a deployed VLAC-style vision-language-action critic service to evaluate task progress, compare current observations against a reference image, and judge task completion from robot camera frames. Use when the agent needs external progress supervision, completion verification, failure detection, or image-based task-state comparison for Piper, Unitree G1, or Unitree Go2.
Hardware roles, embodiment boundaries, and task-fit guidance for Piper, Unitree G1, and Unitree Go2. Use when deciding which robot should handle a task, when reasoning about embodiment constraints, or when documenting robot-specific assumptions in a skill.
Analyze OSWorld-V2 agent trajectory logs and task results to produce actionable insights. Use this skill whenever the user wants to understand agent performance on OSWorld tasks — including analyzing trajectories, reviewing task results, finding error patterns, comparing code vs GUI strategies, identifying which tools/commands the agent used, or deciding which task types to scale up in the benchmark.
Build a CC:Ladder for any CodeClash arena: import human-written solutions as git branches, rank them via round-robin PvP + Elo, and assemble the ladder configs. Use when asked to "create a ladder", "import human solutions", "push human bots as branches", or "make a CC:<arena> ladder" for arenas like BattleSnake, RobotRumble, CoreWar, Gomoku, RoboCode, SCML, etc.
Migrate an agent from upstream OSWorld into this OSWorld-V2 repository, add matching evaluation entrypoints, and verify the integration.
Supervise a set of slay tasks through planning, execution, and verification
全平台嵌入式 AI 开发框架（对标 Trellis）：把 RIPER-5 五阶段协议 + 四文件记忆 + 分层架构门禁 + Scout/Builder/Verifier 多 Agent + 21 个工具调用技能（build/flash/debug/serial/can/modbus/visa/static/memory/rtos），做成『装进工程、项目级 hook 必然运行、按角色自动注入相关 spec、REVIEW 学习回流』的闭环，并一次写、全平台交付（Claude/Cursor/Codex/OpenCode/Copilot/Gemini/Windsurf）。用 `aemb init` 在固件工程里安装运行时与各平台注入接线。当用户要为 STM32/ESP32/GD32/MSPM0/RISC-V/国产 MCU 工程搭建可复用开发规范基座、让约定自动注入、跨会话可恢复、编译/烧录/调试一体化，或问到 auto-embedded/aemb/spec 注入/项目级 hook/全平台时使用。不适用于纯 Web/移动/桌面软件或与硬件无关的通用 C/C++。