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Depalletizing Workflow Planning

Organization
insightos-community
depalletizing-workflow-planning

根据当前场景和资源规划拆码垛 Workflow、搬箱 Task 及其依赖

Overview

Publisherinsightos-community
RepositorySemantic-Framework
Skill namedepalletizing-workflow-planning
Stars
104
Forks
22
Bundled files
Instructions only
LicenseApache-2.0
Links
Skill sourceRepositoryHomepage
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by insightos-community on GitHub. Read the source before you install it.

Installation

Install the Depalletizing Workflow Planning AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/insightos-community/Semantic-Framework/tree/main/configs/skills/workflow/depalletizing-workflow-planning
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/insightos-community/Semantic-Framework.git /tmp/Semantic-Framework
mkdir -p .claude/skills
cp -r /tmp/Semantic-Framework/configs/skills/workflow/depalletizing-workflow-planning .claude/skills/depalletizing-workflow-planning
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Depalletizing Workflow Planning in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Depalletizing Workflow Planning on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Depalletizing Workflow Planning is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

拆码垛 Workflow 规划

本 Skill 只帮助 Leader 理解业务和生成主要 Task，不执行 Robot Skill，也不生成 Stage、Action、Ability 或关节轨迹。

规划原则

  • 对“当前最上面一层搬到对应位置”这类短请求，先用 map.query 查询当前 Project 的 托盘、箱体、Region 和 Robot（例如 entity_types: [pallet, tote, region, robot]，带有效过滤； 结果达到 limit 时按类型、区域或实体收窄查询，不能视为全集）。先按当前位置/支撑关系归属托盘，再按几何高度选择各来源栈顶箱体， 不能选取已搬至目标托盘但仍保留旧层号名称的箱体。以当前槽位身份、行列关系匹配目标。

  • 已明确来源和目标且地图足够时直接提交 Proposal，不再次询问其名称。若目标已占用， 根据当前支撑/容量信息与用户目标判断是否可叠放；缺少容量依据或用户未授权的层位选择 确实影响方案时才询问。不能为了短 Prompt 的“一次通过”跳过业务歧义或安全校验。

  • 地图查询缺少过滤时按返回错误补充过滤；不要退回历史 Artifact/目录扫描猜测当前场景， 更不要递归读取本轮产生的 reduction 产物作为新证据。

  • Semantic Map 是场景记忆和规划参考。对象、槽位和 Robot 数量必须来自当前 Project 上下文或 map.query，不能写死为 1、4 或 12。

  • 目标明确且当前 Map 上只有一个匹配实体时直接生成 Plan Proposal；存在多个 真实候选时先查询，仍无法选择才用一次结构化 interaction.ask。

  • 一个 Robot Task 表达一台 Robot 搬运一个业务对象到一个目标堆叠列。抓取、 导航、放置是该 Task 的 SubTask，不是 Leader Task。

  • 来源依赖按可访问顺序建立：同一垂直栈中以实时几何中心高度最高者作为当前顶层， 不能从 l1/l2/l3 等名称猜测上下关系；被上层箱体遮挡的箱体必须等待其上方箱体搬走。

  • 目标托盘的堆叠列从当前 Map 的实体与 Region 中识别。选择兼容且未达到 max_layers 的列； 同列下一层依赖上一层放置完成。实际放置高度由实时感知计算。

  • 多 Robot 可以并行执行资源不冲突的 Task；单 Robot 或 Robot 忙碌时由现有调度 等待，不要为了并行而复制 Robot 或提前绑定忙碌设备。

  • Proposal 只保存对象/槽位身份、必要位姿提示、允许 Skill 范围和完成标准。 通过 map.query 选中实体后，Task input 必须把查询结果中的稳定业务 name 原样 保存为来源 object_ref 和目标 target_ref；entity_id 只作可选规划来源。不能只 保存 r1-c2 等局部行列标签，也不能让 Robot Agent 猜测或拼接引用。Map pose 不是 Robot Skill 执行成功的证明。

Task 字段

  • Robot Task 的 required_role 固定写机器值 robot，不能写显示名称 Robot Agent。
  • resource_requirements 只表达用户明确要求的可调度资源：robot_ids、robot_models、 backends 和 workspace_write。backends 只能填写Robot实际目录中的实现标识，例如 mujoco 或 fake；“仿真/simulation”是环境类别，不是backend约束，应当省略。箱体、 槽位、工具和导航目标属于业务输入，必须写入 Task input。
  • required_capabilities 只能列出 approved_scope.allowed_skills 中已经批准的 Robot Skill， 工具组合、抓取方式等业务要求写入 Task input，不要发明新的能力名称。
  • 常规拆码垛若明确使用现有三个 Robot Skill，应同时把 semantic-navigation、grasp-object、place-object 写入 approved_scope.allowed_skills 和 Robot Task 的 required_capabilities。版本仍由批准后 的实际 Robot目录固定，不在 Leader Task中猜测。
  • 如果计划有意保留 Robot Skill后绑定，就同时省略 approved_scope.allowed_skills 和 required_capabilities。绝不能把“来源导航”“抓取并整理携物姿态”等业务步骤写成 required_capabilities；这些步骤属于 Task目标或批准后由 Robot Agent生成的 SubTask。

完成标准

每个搬箱 Task 至少验证：目标对象位于目标列的正确层、放置稳定、Robot双工具 为空且恢复行走姿态。Workflow 完成后再由 Leader 汇总所有 Task 的真实结果和证据。

Frequently asked questions

What does the Depalletizing Workflow Planning AI skill do?

根据当前场景和资源规划拆码垛 Workflow、搬箱 Task 及其依赖

Why use Depalletizing Workflow Planning on TypingMind?

Because you install it once and use it with any model. Depalletizing Workflow Planning is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Depalletizing Workflow Planning in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/insightos-community/Semantic-Framework/tree/main/configs/skills/workflow/depalletizing-workflow-planning. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Depalletizing Workflow Planning?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Depalletizing Workflow Planning?

As many as you like. As long as a model supports skills, you can use Depalletizing Workflow Planning with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Depalletizing Workflow Planning AI skill free?

Yes. It is published on GitHub by insightos-community under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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