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Agent Skill · 研究检索

make-and-model-recognition(品牌和型号识别)

make-and-model-recognition 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

make-and-model-recognition 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为接触敏感数据;许可证为 MIT-0,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 21,879。

总安装 4,727周安装 195GitHub Stars 公开资料未说明下载 1,544核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
1.0.1

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:make-and-model-recognition(品牌和型号识别)
来源仓库:https://github.com/eyedea-ai/make-and-model-recognition
安装命令:
openclaw skills install make-and-model-recognition
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install make-and-model-recognition

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

从图像中检测车辆并识别其品牌与型号,同时提取车牌信息。适用于交通监控、停车场管理或保险定损等视觉分析场景。基于 TrafficEye 模型输出结构化数据,便于后续业务逻辑处理。依赖图像清晰度与光照条件,遮挡严重时识别准确率可能下降。

make-and-model-recognition 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install make-and-model-recognition;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要接触敏感数据,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

make-and-model-recognition 怎么安装?

在终端执行 openclaw skills install make-and-model-recognition。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

make-and-model-recognition 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

make-and-model-recognition 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

make-and-model-recognition 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

make-and-model-recognition 用的人多吗?

公开数据是累计安装 4,727 次,近一周安装 195 次,下载 1,544 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

make-and-model-recognition 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
make-and-model-recognition

品牌和型号识别 · 本页

4,727—接触敏感数据MIT-0通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

name
make-and-model-recogntion
description
Detect the largest vehicle from an image using TrafficEye car-box detection, run make and model recognition for that vehicle, and return all license plates attached to the same road-user payload. Use when the user wants the dominant vehicle, vehicle classification, car box detection, make and model recognition, or the plates associated with the main vehicle in a local image file. You can obtain API key and tokens from https://trafficeye.ai.
metadata
openclaw
requires
env
anyBins
primaryEnv
TRAFFICEYE_API_KEY
homepage
https://trafficeye.ai
os

TrafficEye Largest Road User Reader

Use this skill when the user wants the largest detected vehicle from an image, along with its make and model classification and every detected license plate belonging to that same road user.

What This Skill Does

  1. Accepts a local image path.
  2. Uploads the image to the TrafficEye recognition API.
  3. Sends a recognition request that asks for detection, OCR, and MMR with box preference by default.
  4. Parses the API response, including responses wrapped as { "status": ..., "data": ... }.
  5. Picks the largest detected road user by box.position area.
  6. Returns a wrapper object containing roadUser, box, plates, area, and source, preserving the full selected road-user payload.

Expected Input

  • A local image file path.
  • If the user supplied an attachment instead of a path, first resolve it to a local file path and then run the helper.

Default Runtime Assumptions

  • The API endpoint defaults to https://trafficeye.ai/recognition.
  • The default request payload is {"tasks":["DETECTION","OCR","MMR"],"requestedDetectionTypes":["BOX","PLATE"],"mmrPreference":"BOX"}.
  • The default API-key transport matches the TrafficEye public API example: header mode with header name apikey.
  • Auth and request fields remain configurable in case your deployment differs.

Environment Variables

  • TRAFFICEYE_API_KEY: required unless passed explicitly to the helper.
  • TRAFFICEYE_API_URL: optional, defaults to https://trafficeye.ai/recognition.
  • TRAFFICEYE_API_KEY_MODE: one of header, bearer, form, query. Default: header.
  • TRAFFICEYE_API_KEY_NAME: key name for header, form, or query mode. Default: apikey.
  • TRAFFICEYE_FILE_FIELD: multipart field for the image. Default: file.
  • TRAFFICEYE_REQUEST_FIELD: multipart field for the JSON request. Default: request.
  • TRAFFICEYE_REQUEST_JSON: JSON string to include as the request field. By default this is {"tasks":["DETECTION","OCR","MMR"],"requestedDetectionTypes":["BOX","PLATE"],"mmrPreference":"BOX"}.
  • TRAFFICEYE_TIMEOUT_S: optional timeout in seconds. Default: 30.

Only TRAFFICEYE_API_KEY is required for the default live API flow. The other variables are optional overrides.

How To Run

Setup your API key:

export TRAFFICEYE_API_KEY='YOUR_REAL_KEY'

Use the road-user helper:

python3 recognize_road_user.py /absolute/path/to/image.jpg

For structured output:

python3 recognize_road_user.py /absolute/path/to/image.jpg --format json

If the deployment expects Bearer auth:

TRAFFICEYE_API_KEY_MODE=bearer python3 recognize_road_user.py /absolute/path/to/image.jpg

If the deployment needs an explicit request payload:

TRAFFICEYE_REQUEST_JSON='{"tasks":["DETECTION","OCR","MMR"],"requestedDetectionTypes":["BOX","PLATE"],"mmrPreference":"BOX"}' python3 recognize_road_user.py /absolute/path/to/image.jpg --format json

Equivalent to the documented public API example:

curl -X POST \
  -H "Content-Type: multipart/form-data" \
  -H "apikey: YOUR_API_KEY_HERE" \
  -F "file=@image.jpg" \
  -F 'request={"tasks":["DETECTION","OCR","MMR"],"requestedDetectionTypes":["BOX","PLATE"],"mmrPreference":"BOX"}' \
  https://trafficeye.ai/recognition

Agent Workflow

  1. Verify that the image path exists.
  2. Run python3 recognize_road_user.py <image-path> --format json.
  3. Present the full selected road-user payload to the user, especially box, mmr, and the complete plates array.
  4. If the selected road user has no plates, explain that the largest vehicle was found but no plates were attached to that road user.
  5. If authentication fails, ask the user which auth mode their deployment expects and retry with the matching environment variables.

Offline Validation

You can validate the selection logic without calling the API:

python3 recognize_road_user.py --response-json-file examples/sample_response.json --format json

Output Shape

The helper prints JSON with this top-level structure:

{
  "roadUser": {"box": {}, "plates": [], "mmr": {}},
  "box": {},
  "plates": [],
  "area": 0,
  "source": {
    "combinationIndex": 0,
    "roadUserIndex": 0,
    "path": "combinations[0].roadUsers[0]"
  }
}
  • roadUser is the original selected road-user payload from TrafficEye.
  • box repeats roadUser.box for convenience.
  • plates repeats roadUser.plates for convenience and may be empty.
  • area is the computed rectangle area used for winner selection.
  • source identifies where the selected road user came from in the API response.

Notes

  • The helper intentionally chooses the largest boxed vehicle by geometric area, not by detection confidence.
  • The response parser first checks data.combinations[].roadUsers[], then combinations[].roadUsers[], then roadUsers[], and finally nested road-user payloads discovered recursively.
  • The default request and auth header mirror the public example at https://www.trafficeye.ai/api.
  • The selected result now includes the original road-user payload from the API so mmr, box, all plates, and their scores are preserved.

平台分布

OpenClaw

98.42%
按下载量换算1,520

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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