Agent skill · Data & Analytics

hand-tracking-toolkit

Facebook Research Hand Tracking Challenge Toolkit - evaluation and visualization tools for 3D hand tracking. Supports loading HOT3D data, computing metrics (PA-MPJPE, AUC, etc.), visualizing 3D pose projections, and generating tracking evaluation reports. Essential for benchmarking hand tracking algorithms.

BioTender-maxgithub.com/BioTender-maxGitHub ↗
claude-codeNOASSERTION
Install
npx skills add BioTender-max/awesome-bio-agent-skills --skill hand-tracking-toolkit --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 1
SKILL.md size: 5 KB
Bundled scripts: none
Path: skills/labclaw/hand-tracking-toolkit/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 135
Language: Python

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Hand Tracking Toolkit - Evaluation & Visualization ## Overview Comprehensive toolkit from Meta Facebook Research for evaluating and visualizing 3D hand tracking systems. Provides standardized metrics, visualization tools, and data loaders for the HOT3D dataset. Essential for researchers developing and benchmarking hand tracking algorithms on multi-view egocentric data. **Use this for**: Evaluating hand tracking performance, generating evaluation reports, visualizing 3D predictions vs ground truth. ## When to Use This Skill Use when you need to: - **Evaluate** hand tracking algorithms with standard metrics - **Visualize** 3D hand pose predictions and ground truth - **Benchmark** on HOT3D dataset - **Generate** evaluation reports and leaderboards - **Compare** different tracking methods - **Debug** hand tracking predictions ## Core Capabilities ### 1. Standard Metrics Compute widely-used hand tracking metrics: - **PA-MPJPE**: Per-vertex Mean Per Joint Position Error (aligned) - **MPJPE**: Mean Per Joint Position Error - **AUC**: Area Under Curve for error thresholds - **PCK**: Percentage of Correct Keypoints - **Mesh error**: Surface-to-surface distance ### 2. Visualization Tools R

What's inside
Steps it walks through
  1. Overview
  2. When to Use This Skill
  3. Core Capabilities
  4. 1. Standard Metrics
  5. 2. Visualization Tools
  6. 3. Data Loaders
  7. Quick Start
  8. Usage Examples
  9. Example 1: Evaluate Predictions
  10. Example 2: Visualize Results
  11. Example 3: Generate Report
  12. Supported Formats
  13. Metrics Reference
  14. Integration
Commands it runs
Clone repository
git clone https://github.com/facebookresearch/hand_tracking_toolkit.git
cd hand_tracking_toolkit
Install
pip install -r requirements.txt
Run evaluation
python evaluate.py \
Generate visualizations
python visualize.py \
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About this skill
What does the hand-tracking-toolkit skill do?

Facebook Research Hand Tracking Challenge Toolkit - evaluation and visualization tools for 3D hand tracking. Supports loading HOT3D data, computing metrics (PA-MPJPE, AUC, etc.), visualizing 3D pose projections, and generating tracking evaluation reports. Essential for benchmarking hand tracking algorithms.

How do I install it?

Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill hand-tracking-toolkit --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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