Kunwu Builder

控制Kunwu Builder工业仿真软件的HTTP API，实现模型管理、机器人控制、物流设备、相机及传感器操作、场景与行为配置等功能。

Security Scan

The skill mostly matches its stated purpose (controlling a Kunwu Builder HTTP API) but contains inconsistent defaults and documentation (multiple different base URLs, references to other product names) and a non-local default BASE_URL in the code — meaning it could contact unexpected hosts unless you explicitly lock the API address.

Purpose & Capability

The skill's name, docs, and many JS helpers all implement HTTP calls and helpers for model/behavior management — consistent with 'control Kunwu Builder'. However documentation and code disagree about the API host (SKILL.md: http://192.168.30.9:16888; README/QUICKSTART: 127.0.0.1:16888; kunwu-tool.js default: http://100.85.119.45:16888) and some docs mention 'SpeedBot' (different product name). These mismatches are sloppy and create risk of contacting the wrong machine, but the core capability (HTTP control of a simulation) aligns with the stated purpose.

Instruction Scope

SKILL.md and other docs instruct the agent (and user) to run node scripts (e.g., scripts/model-loader.js, test-connection-kunwu.js, auto-gripper-behavior.js) that will enumerate scene models and call API endpoints. Those instructions are within the stated purpose, but they also include explicit IP addresses and examples that may point to non-local hosts. The runtime instructions and included code will make arbitrary POSTs to whatever BASE_URL is set to; because some docs and code default to external/non-local addresses, the skill could send scene/model data to an unexpected host if not reconfigured.

Install Mechanism

There is no install spec — the package is instruction + JS files. That is the lower-risk pattern because nothing is automatically downloaded or executed during install. The included scripts will only run if the agent/user invokes them. No remote downloads or extract-from-URL installs were found.

Credentials

requires.env lists none and no credentials are requested, which is appropriate. However the code reads process.env.KUNWU_API_URL (optional) and the hardcoded default in kunwu-tool.js is an external IP (http://100.85.119.45:16888). That default is disproportionate for a tool that should target a local instance and could cause network calls to a remote host without additional credentials or explicit user configuration.

Persistence & Privilege

always is false and the skill does not request system-wide privileges. The package contains scripts and docs that instruct how to tar/transfer the skill, but there is no automatic persistence or modification of other skills' configuration. Note: autonomous invocation is allowed by default (normal for skills); combined with the non-local default endpoint this increases the impact if an agent runs scripts without the user locking the API URL.

What to consider before installing

This skill appears to implement a legitimate Kunwu Builder API client and many useful helper scripts, but there are inconsistent and potentially dangerous defaults you should fix before use: - Inspect kunwu-tool.js for BASE_URL (line that sets DEFAULT = process.env.KUNWU_API_URL || 'http://100.85.119.45:16888'). That default points to a non-local IP. Before running anything, set KUNWU_API_URL explicitly to the expected host (for example http://127.0.0.1:16888 or your known internal IP) or edit the file to use a safe default. - SKILL.md, README, and QUICKSTART show different default addresses (192.168.30.9, 127.0.0.1, and 100.85.119.45). Decide which host is correct for your environment and update all config/docs; do not rely on package defaults. - Because the skill contains runnable node scripts that will POST scene/model data to the configured API, run the scripts in a controlled environment first (offline or on an isolated network) and monitor network traffic to confirm they only contact the intended host. - The skill requests no credentials, but APIs are unauthenticated in the docs — confirm your Kunwu instance is on a trusted network and not exposed to the Internet. - Search the codebase for any other hardcoded URLs or references to external hosts (e.g., '100.85.119.45', '192.168.30.9', or 'SpeedBot') and replace or remove them. - If you allow the agent to invoke the skill autonomously, be aware it could run scripts that change the simulation state; prefer manual invocation until you vet the scripts. If you want, I can highlight the exact lines in kunwu-tool.js and SKILL.md where the differing defaults appear and suggest an explicit safe configuration to apply.

Kunwu Builder 控制技能

控制 Kunwu Builder (坤吾) 工业仿真软件的 HTTP API。

基础信息

项目	值
API 地址	http://192.168.30.9:16888
认证	无
请求方式	POST
Content-Type	application/json

工具

kunwu_call

kunwu_call endpoint="/model/create" data='{"id":"M900iB_280L","position":[0,0,0],"checkFromCloud":true}'

参数	必填	说明
endpoint	✅	API 路径
method	❌	HTTP 方法，默认 POST
data	❌	请求体 JSON

---

常用 API

1. 模型加载机制（核心流程）⚡

唯一推荐方式：使用 /model/create + checkFromCloud=true

kunwu_call endpoint="/model/create" data='{
  "id": "M900iB_280L",
  "rename": "机器人_右",
  "position": [0, 2500, 515],
  "eulerAngle": [0, 0, 0],
  "checkFromCloud": true
}'

机制说明：

• checkFromCloud: true → 本地有直接加载（快速），本地没有自动从云端下载
• checkFromCloud: false → 仅从本地加载，本地没有则失败

决策树：

需要加载模型？
  ├─ 是 → 用 /model/create + checkFromCloud:true（99% 场景）
  └─ 否 → 不需要任何操作

步骤 1: 直接使用 /model/create 创建模型

kunwu_call endpoint="/model/create" data='{
  "id": "辊床_01",
  "position": [0, 0, 0],
  "checkFromCloud": true
}'

批量加载（推荐）：使用 scripts/model-loader.js

# 准备 models.json
node scripts/model-loader.js models.json

# 环境变量指定 API 地址
KUNWU_API_URL=http://192.168.30.9:16888 node scripts/model-loader.js models.json

步骤 2: 设置模型参数化（如支持）

kunwu_call endpoint="/model/set_pose" data='{
  "id": "皮带线_01",
  "position": [-11013.5, -603.5, 0],
  "eulerAngle": [0, 0, -90],
  "parameterizationCfg": [
    {"type": 0, "value": 10000},
    {"type": 1, "value": 1460},
    {"type": 2, "value": 1000}
  ]
}'

ModelName 说明：

• 使用本地模型库中的 modelName 字段值
• 常见模型：辊床_01, M900iB_280L, 方形底座_02, 吸盘_10, 托盘_07

参数化模型说明：

• 部分模型支持参数化（如皮带线、方形等）
• 使用 /model/set_pose 设置参数化配置
• parameterizationCfg 格式：[{"type": 0, "value": 值}, ...]
• type 编号含义：0=长，1=宽，2=高（具体参考模型文档）

2. 场景查询

获取层级树

kunwu_call endpoint="/models/tree" data='{"rootId":"scene","useModeId":true}'

3. 装配（核心）

kunwu_call endpoint="/model/assemble" data='{
  "childId": "子模型 modelId",
  "parentId": "父模型 modelId",
  "assemblePosIndex": 0,
  "replaceExisting": false
}'

响应码：

• 200 - 装配成功
• 409 - 装配位被占用（设置 replaceExisting: true 替换）

4. 销毁模型

kunwu_call endpoint="/model/destroy" data='{"id":"modelId","useModeId":true}'

---

夹具行为配置（核心功能）

四大原则（必须遵守！）

原则	说明	示例
1. 轴向判断	使用 transform 相对位置确定运动轴向	ΔX 最大 → X 轴开合
2. 相向夹紧	主动臂朝中心运动，从动臂反向	主动 X+ → 从动 X-
3. 行程计算	行程 = boundSize ÷ 3	52mm → 17mm
4. 联动配置	从动臂必须配置 dependentTargetId	⚠️ 不配无法联动

轴向对照表

轴向	值	相反轴向	值	异或
X+	0	X-	1	0^1=1
Y+	2	Y-	3	2^1=3
Z+	4	Z-	5	4^1=5

快速反向： dependentAxis = activeAxis ^ 1

7 步配置流程

# 步骤 1: 获取层级树
kunwu_call endpoint="/models/tree" data='{"rootId":"scene"}'

# 步骤 2-3: 分析结构，确定轴向
# 读取 transform 计算 ΔX/ΔY/ΔZ，选最大值的轴
# 根据相对位置确定方向（相向夹紧原则）

# 步骤 4: 计算行程
# travel = boundSize[axisIndex] / 3

# 步骤 5: 配置主动臂 (behavioralType: 1)
kunwu_call endpoint="/behavior/add" data='{
  "id": "DH_PGS_5_5/1",
  "useModeId": false,
  "behavioralType": 1,
  "referenceAxis": 0,
  "minValue": 0,
  "maxValue": 4,
  "runSpeed": 80,
  "targetValue": 2
}'

# 步骤 6: 配置从动臂 (behavioralType: 3 + dependentTargetId！)
kunwu_call endpoint="/behavior/add" data='{
  "id": "DH_PGS_5_5/2",
  "useModeId": false,
  "behavioralType": 3,
  "referenceAxis": 1,
  "minValue": 0,
  "maxValue": 4,
  "runSpeed": 80,
  "targetValue": 2,
  "dependentTargetId": "DH_PGS_5_5/1",
  "dependentTargetUseModeId": false
}'

# 步骤 7: 验证配置
kunwu_call endpoint="/behavior/list" data='{"id":"夹具 modelId","useModeId":true}'

实测案例

夹具型号	ΔX	ΔY	ΔZ	轴向	行程
DH_PGS_5_5	16mm	-	-	X 轴	4mm
DH_PGI_140_80	40mm	34.7mm	-	X 轴	17mm
DH_PGE_100_26	-	46mm	-	Y 轴	15mm
DH_RGD_5_14	40mm	-	-	X 轴	13mm

---

完整示例：搭建双机器人工作站（带托盘布局）

方式 A: 使用批量加载工具（推荐）

node scripts/model-loader.js scripts/models-dual-robot-trays.json

方式 B: 手动逐步创建

# 步骤 1: 创建模型（使用 checkFromCloud=true，本地有则快，没有自动下载）
# 1.1 输送线
kunwu_call endpoint="/model/create" data='{
  "id": "辊床_01",
  "rename": "输送线",
  "position": [0, 0, 0],
  "checkFromCloud": true
}'

# 1.2 底座×2
kunwu_call endpoint="/model/create" data='{
  "id": "方形底座_02",
  "rename": "底座_右",
  "position": [0, 2500, 0],
  "checkFromCloud": true
}'
kunwu_call endpoint="/model/create" data='{
  "id": "方形底座_02",
  "rename": "底座_左",
  "position": [0, -2500, 0],
  "checkFromCloud": true
}'

# 1.3 机器人×2
kunwu_call endpoint="/model/create" data='{
  "id": "M900iB_280L",
  "rename": "机器人_右",
  "position": [0, 2500, 515],
  "checkFromCloud": true
}'
kunwu_call endpoint="/model/create" data='{
  "id": "M900iB_280L",
  "rename": "机器人_左",
  "position": [0, -2500, 515],
  "checkFromCloud": true
}'

# 1.4 吸盘×2
kunwu_call endpoint="/model/create" data='{
  "id": "吸盘_10",
  "rename": "吸盘_右",
  "position": [0, 2500, 2000],
  "checkFromCloud": true
}'
kunwu_call endpoint="/model/create" data='{
  "id": "吸盘_10",
  "rename": "吸盘_左",
  "position": [0, -2500, 2000],
  "checkFromCloud": true
}'

# 1.5 托盘×4（分布在机器人前后两侧）
kunwu_call endpoint="/model/create" data='{
  "id": "托盘_07",
  "rename": "托盘_右前",
  "position": [3000, 4500, 0],
  "checkFromCloud": true
}'
kunwu_call endpoint="/model/create" data='{
  "id": "托盘_07",
  "rename": "托盘_右后",
  "position": [3000, 500, 0],
  "checkFromCloud": true
}'
kunwu_call endpoint="/model/create" data='{
  "id": "托盘_07",
  "rename": "托盘_左前",
  "position": [3000, -500, 0],
  "checkFromCloud": true
}'
kunwu_call endpoint="/model/create" data='{
  "id": "托盘_07",
  "rename": "托盘_左后",
  "position": [3000, -4500, 0],
  "checkFromCloud": true
}'

# 步骤 2: 设置参数化（必须同时传 position + eulerAngle + parameterizationCfg）
kunwu_call endpoint="/model/set_pose" data='{
  "id": "输送线 modelId",
  "useModeId": true,
  "position": [0, 0, 0],
  "eulerAngle": [0, 0, 0],
  "parameterizationCfg": [
    {"type": 0, "value": 7940},
    {"type": 1, "value": 4340},
    {"type": 2, "value": 708}
  ]
}'

kunwu_call endpoint="/model/set_pose" data='{
  "id": "底座_右 modelId",
  "useModeId": true,
  "position": [0, 2500, 0],
  "eulerAngle": [0, 0, 0],
  "parameterizationCfg": [
    {"type": 0, "value": 1000},
    {"type": 1, "value": 1000},
    {"type": 2, "value": 515}
  ]
}'

# 步骤 3: 获取模型 ID 并执行装配
# 吸盘 → 机器人 → 底座
kunwu_call endpoint="/model/assemble" data='{
  "childId": "吸盘 modelId",
  "parentId": "机器人 modelId",
  "assemblePosIndex": 0,
  "useModeId": true
}'
kunwu_call endpoint="/model/assemble" data='{
  "childId": "机器人 modelId",
  "parentId": "底座 modelId",
  "assemblePosIndex": 0,
  "useModeId": true
}'

托盘布局说明：

         Y+
          ↑
    左后  |  右后
    ──────┼──────
    左前  |  右前
          |
──────────┼──────────→ X+
    输送线 (0,0)
    
机器人_右：Y=2500
机器人_左：Y=-2500
托盘_右前：(3000, 4500)   托盘_右后：(3000, 500)
托盘_左前：(3000, -500)   托盘_左后：(3000, -4500)

---

装配规则

正确层级：

场景
├── 地轨/底座 → 机器人 → 夹具
├── 桁架 → 自由臂 → 抓手
└── 相机支架 → 相机

错误示例： ❌ 底座 → 桁架（两者都是支撑结构，不应互相装配）

多装配位选择规则：

1. 优先 assemblePosName（按名称匹配）
2. 优先 assemblePosIndex >= 0（兼容位）
3. 自动选择（先找兼容且空闲位）

---

完整 API 参考

模型库管理

接口	说明	示例
/model/library/local	获取本地模型库	{"get":"true"}
/model/library/remote	获取远程模型库	{"pageNum":1,"pageSize":10}
/model/library/categories	获取分类列表	{}
/model/library/favorites	获取收藏列表	{}
/model/library/favorite	收藏模型	{"id":"模型名"}
/model/library/delete	删除本地模型	{"id":"模型名"}

模型创建与加载

接口	说明	示例
/model/create	创建/加载模型（唯一推荐）	{"id":"模型名","position":[x,y,z],"checkFromCloud":true}
/model/set_pose	设置位置/参数化	{"id":"模型名","position":[x,y,z],"parameterizationCfg":[...]}
/model/set_render	设置渲染颜色	{"id":"模型名","color":"#FF0000"}
/model/export	导出模型	{"id":"模型名"}
/model/destroy	销毁模型	{"id":"modelId","useModeId":true}

重要： 始终使用 /model/create 并设置 checkFromCloud: true：

• 本地有模型 → 直接加载（快速）
• 本地没有 → 自动从云端下载（无需额外步骤）

⚠️ 参数化设置注意事项： /model/set_pose 设置参数化时必须同时传位置和角度，否则返回 406 错误！

# ✅ 正确 - 三者一起传
kunwu_call endpoint="/model/set_pose" data='{
  "id": "皮带线_01",
  "position": [0, 0, 0],
  "eulerAngle": [0, 0, 0],
  "parameterizationCfg": [
    {"type": 0, "value": 7940},
    {"type": 1, "value": 4340},
    {"type": 2, "value": 708}
  ]
}'

# ❌ 错误 - 只传参数化会失败（406 NotAcceptable）
kunwu_call endpoint="/model/set_pose" data='{
  "id": "皮带线_01",
  "parameterizationCfg": [...]
}'

场景查询

接口	说明	示例
/models/tree	获取场景层级树	{"rootId":"scene","useModeId":true}
/GetModelInfo	获取单个模型信息	{"id":"modelId","useModeId":true}
/GetAllModelInfo	获取所有模型信息	{}
/scene/get_scene_json	获取场景 JSON	{}

层级与装配

接口	说明	示例
/model/assemble	装配到指定位置	{"childId":"xxx","parentId":"xxx","assemblePosIndex":0}
/model/set_parent	设置父子关系	{"childId":"xxx","parentId":"xxx"}

行为控制

接口	说明	示例
/behavior/add	添加行为	{"id":"模型/子节点","behavioralType":1,"referenceAxis":0,...}
/behavior/list	获取行为配置列表	{"id":"modelId","useModeId":true}
/behavior/get	获取行为参数	{"id":"模型/子节点","useModeId":false}
/behavior/delete	删除行为	{"id":"模型/子节点","useModeId":false}

其他功能

接口	说明	示例
/batch/execute	批量执行命令	{"commands":[{"url":"...","body":{}}]}
/view/show_progress	显示进度条	{"progress":50,"message":"处理中..."}
/ResetScene	重置场景	{}
/ChangeMode	切换模式	{"mode":0} (0:构建 1:信号 2:机器人 3:孪生)

---

错误码

Code	说明	处理
200	成功	-
202	任务运行中	等待后查询
400	请求错误	检查参数
404	资源不存在	检查 modelId
409	装配位被占用	replaceExisting: true

---

快速参考

参考轴 (referenceAxis)： 0=X+, 1=X-, 2=Y+, 3=Y-, 4=Z+, 5=Z-

行为类型 (behavioralType)： 1=主动，3=从动

层级路径： "DH_PGS_5_5/1" → 夹具的第一个子关节

---

更新日志

• 2026-03-20: 统一模型加载机制（仅用 /model/create + checkFromCloud:true），彻底移除 /model/download
• 2026-03-19 (3): 补充完整 API 参考表（25+ 接口）
• 2026-03-19 (2): 新增参数化设置方法（/model/set_pose）+ 完整场景搭建示例
• 2026-03-16: 固化四大原则 + 相向夹紧原则 + 7 步流程
• 2026-03-14: 支持从属运动配置
• 2026-03-13: 新增模型库管理