Hand Tracking Toolkit

What it does

When your agent uses it

• Data & Analytics work in your project

Example prompts

• “/hand-tracking-toolkit”

Requirements

• Python 3

Workflow steps

3 steps, taken from the step headings in SKILL.md.

1. 1Standard Metrics
2. 2Visualization Tools
3. 3Data Loaders

What it can do on your machine

Read from SKILL.md and the folder at commit df37802. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  Shell commands in SKILL.md call:

  • python
  • git
  • pip

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  Hosts in commands or code, which the agent is likely to contact:

  • github.com

  Also links to:

  • facebookresearch.github.io
  • eval.ai

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names no API keys, tokens, secrets or passwords.

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Hand Tracking Toolkit loads about 1.3k tokens when it runs. Until then it costs about 83 tokens; SKILL.md has 304 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~83

When it runs · the whole SKILL.md, loaded when a task matches

~1.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from wu-yc/LabClaw at commit df37802, republished under its Apache-2.0 licence (© wu-yc). 304 words, ~1,333 tokens.

name

hand-tracking-toolkit

description

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.

license

Apache 2.0

metadata.skill-author

K-Dense Inc.

metadata.original-repo

https://github.com/facebookresearch/hand_tracking_toolkit

metadata.organization

Meta Facebook Research

metadata.skill-category

Computer Vision / Evaluation

metadata.tags

hand-tracking, evaluation, metrics, visualization, benchmarking, hot3d

# 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 \
    --predictions path/to/predictions.pkl \
    --ground_truth path/to/hot3d/sequence \
    --output_dir ./results

# Generate visualizations
python visualize.py \
    --predictions path/to/predictions.pkl \
    --ground_truth path/to/hot3d/sequence \
    --output visualizations.png

from toolkit import Evaluator
import pickle

# Load predictions
with open('predictions.pkl', 'rb') as f:
    predictions = pickle.load(f)

# Load ground truth
evaluator = Evaluator()
evaluator.load_ground_truth('path/to/hot3d_sequence')

# Compute metrics
metrics = evaluator.evaluate(predictions)

print(f"PA-MPJPE: {metrics['pa_mpjpe']:.2f} mm")
print(f"AUC: {metrics['auc']:.3f}")
print(f"PCK@0.1: {metrics['pck_01']*100:.1f}%")

from toolkit import Visualizer

viz = Visualizer()

# Load data
viz.load_predictions('predictions.pkl')
viz.load_ground_truth('ground_truth_path')

# Create visualization
fig = viz.plot_3d_skeleton(
    frame_id=100,
    show_pred=True,
    show_gt=True,
    show_errors=True
)

fig.savefig('comparison_3d.png')

from toolkit import ReportGenerator

report = ReportGenerator()
report.load_evaluation_results('results.json')

# Generate PDF report
report.generate_pdf(
    output_path='evaluation_report.pdf',
    include_plots=True,
    include_per_joint_errors=True
)

predictions = {
    'sequence_id': 'seq001',
    'frames': [
        {
            'frame_id': 0,
            'left_hand': np.array((21, 3)),  # 21 joints x 3 coords
            'right_hand': np.array((21, 3)),
            'confidence': np.array(21),
        },
        # ... more frames
    ]
}

© wu-yc, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/vision/hand-tracking-toolkit of wu-yc/LabClaw.

Open the folder on GitHubat commit df37802

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in wu-yc/LabClaw, which our catalogue first saw on October 7, 2026.

• BioTender-max/awesome-bio-agent-skillsskills/labclaw/hand-tracking-toolkit