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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.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/amap-cvlab/ABot-Claw --skill abotclaw-bundle命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
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.
SpatialMemoryHub-based robot memory querying, writing, and retrieval for AbotClaw. Use when the agent needs to store or retrieve object memory, place memory, keyframe memory, or semantic frame memory; when checking whether a memory service is running; when using robot SDK camera frames to perform memory ingestion or memory-guided retrieval; or when grounding robot tasks in prior visual/spatial experience.
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.
基于 SOC 职业分类
正在显示 SKILL.md
| name | abotclaw-bundle |
| description | 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. |
Bundle robot skill code into a single portable artifact when the runtime or review flow benefits from one-file submission.
deps.txt style dependency chainsskill-name/
├── SKILL.md
└── scripts/
├── main.py
└── deps.txt
scripts/deps.txt recursively if present.Only bundle dependencies that are actually required by the target task. Do not drag in half the workspace just because it exists.
For implementation details, see references/usage.md.