Tracking Pipeline

Organization

VectorSpaceLab

tracking-pipeline

Run AB3DMOT tracking safely and use the core AB3DMOT tracker APIs for KITTI and nuScenes 3D MOT workflows.

Overview

Publisher	VectorSpaceLab
Repository	`AREX-Skill`
Skill name	`tracking-pipeline`
Stars	341
Forks	26
Bundled files	6
License	NOASSERTION
Links	Skill source Repository

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.
6 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by VectorSpaceLab on GitHub. Read the source before you install it.

Installation

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

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/ab3dmot/sub-skills/tracking-pipeline
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/ab3dmot/sub-skills/tracking-pipeline

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

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/VectorSpaceLab/AREX-Skill.git /tmp/AREX-Skill
mkdir -p .claude/skills
cp -r /tmp/AREX-Skill/skills/repositories/repo-skills/ab3dmot/sub-skills/tracking-pipeline .claude/skills/tracking-pipeline

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Tracking Pipeline 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 Tracking Pipeline 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 Tracking Pipeline 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.

tracking-pipeline

Use this sub-skill when a task is about running AB3DMOT tracking, constructing a safe main.py command, understanding tracker configuration, or calling the AB3DMOT.track API directly.

Read first

For command-level tracking workflows, dataset prerequisites, category loops, and output folders, read references/tracking-workflow.md.
For direct API usage, synthetic smoke behavior, AB3DMOT.track, Box3D, matching, and Kalman filter details, read references/api-reference.md.
For YAML defaults, CLI override behavior, detector/category compatibility, and config pitfalls, read references/configuration.md.
For common tracking failures and recovery steps, read references/troubleshooting.md.

Bundled scripts

scripts/build_tracking_command.py builds explicit, non-running main.py commands and prints the expected input/result folder names without importing AB3DMOT.
scripts/smoke_track_synthetic.py runs a no-dataset one-frame API smoke. Pass --repo-root /path/to/AB3DMOT and, when Xinshuo is external, --toolbox-root /path/to/Xinshuo_PyToolbox; the helper injects both paths before importing.

Boundaries

This sub-skill owns:

main.py flags: --dataset, --split, and --det_name.
Config fields that affect tracking: save_root, dataset, split, det_name, cat_list, score_threshold, num_hypo, ego_com, vis, and affi_pro.
KITTI and nuScenes detector/category compatibility for tracking.
Result-directory naming, category-specific tracking, combined results, affinity outputs, and logs.
Direct use of AB3DMOT, AB3DMOT.track, Box3D, matching, and the Kalman filter state model.

Use sibling skills for neighboring concerns:

Data download, raw nuScenes conversion, detector-result conversion, and detection schema repair belong to ../data-conversion/SKILL.md.
Metrics, confidence thresholding, result export, server submission, and visualization belong to ../evaluation-visualization/SKILL.md.

Fast decisions

For KITTI PointRCNN validation, use explicit flags: python main.py --dataset KITTI --split val --det_name pointrcnn.
Do not run bare python main.py unless the task intentionally uses the nuScenes config default.
If the user is unsure which input folders/results will be used, run the bundled command builder first.
If the user wants to embed AB3DMOT in another Python loop, start with the API reference and synthetic smoke script instead of the dataset-level CLI.
If full tracking data is missing, do not treat detector text files as enough; route back to data-conversion layout checks.

Minimal checks before running tracking

python main.py --help succeeds in the target runtime.
The dataset config has the intended split, det_name, cat_list, and save_root.
Category-specific detection folders exist for every configured category.
The full tracking data root contains calibration, image frame lists, and ego-motion/OXTS data for the split.
The result root has enough write space; tracking writes per-frame files, affinity matrices, logs, and combined category outputs.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Tracking Pipeline AI skill do?

Run AB3DMOT tracking safely and use the core AB3DMOT tracker APIs for KITTI and nuScenes 3D MOT workflows.

Why use Tracking Pipeline on TypingMind?

Because you install it once and use it with any model. Tracking Pipeline 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 Tracking Pipeline in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/ab3dmot/sub-skills/tracking-pipeline. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Tracking Pipeline?

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 Tracking Pipeline?

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

Is the Tracking Pipeline AI skill free?

Yes. It is published on GitHub by VectorSpaceLab under the NOASSERTION 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

tracking-pipeline

Read first

Bundled scripts

Boundaries

Fast decisions

Minimal checks before running tracking

Bundled files

Frequently asked questions

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