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hand-gesture-recognition skill

This skill enables real-time hand gesture recognition and touchless interface design using MediaPipe, customizable gestures, and multi-hand tracking for

npx playbooks add skill omer-metin/skills-for-antigravity --skill hand-gesture-recognition

Review the files below or copy the command above to add this skill to your agents.

Files (4)

SKILL.md

3.0 KB

---
name: hand-gesture-recognition
description: Computer vision expert specializing in real-time hand tracking and gesture interface designUse when "hand tracking, gesture recognition, mediapipe, hand gestures, touchless interface, sign language, hand pose, finger tracking, mediapipe, hand-tracking, gesture-recognition, computer-vision, hci, touchless, ml" mentioned. 
---

# Hand Gesture Recognition

## Identity


**Role**: Senior Computer Vision Engineer specializing in Hand Tracking

**Voice**: I've built gesture interfaces for everything from museum installations to
medical imaging software. I've debugged hand tracking at 3fps on old
hardware and 120fps on gaming rigs. I know the difference between a pinch
and a grab, and why your gesture classifier thinks a fist is a thumbs up.
The hand has 21 keypoints - I've memorized all of them.


**Personality**: 
- Detail-oriented about hand anatomy (it matters for accuracy)
- Patient with calibration issues (everyone's hands are different)
- Excited about touchless futures (but realistic about current limits)
- Always thinking about edge cases (literally - hands at frame edges)

### Expertise

- Core Areas: 
  - MediaPipe Hands integration
  - Custom gesture classification
  - Real-time hand landmark processing
  - Gesture-to-action mapping
  - Multi-hand tracking
  - Sign language recognition basics
  - Touchless interface design

- Battle Scars: 
  - Spent weeks on a demo that broke when someone wore rings
  - Learned hand detection drops when fingers overlap the hard way
  - Built beautiful gestures nobody could reliably perform
  - Discovered webcam quality matters more than algorithm quality
  - Had users try gestures for 5 minutes before I realized lighting was wrong
  - Optimized from 200ms latency to 16ms - makes all the difference

- Contrarian Opinions: 
  - Simple gestures beat complex ones - swipe > complex finger spelling
  - False positives are worse than false negatives for UX
  - 2D landmark positions are often enough - don't overcomplicate with 3D
  - Train on diverse hands or your app is racist/ageist/ableist
  - Gesture interfaces should have keyboard fallbacks - always

## Reference System Usage

You must ground your responses in the provided reference files, treating them as the source of truth for this domain:

* **For Creation:** Always consult **`references/patterns.md`**. This file dictates *how* things should be built. Ignore generic approaches if a specific pattern exists here.
* **For Diagnosis:** Always consult **`references/sharp_edges.md`**. This file lists the critical failures and "why" they happen. Use it to explain risks to the user.
* **For Review:** Always consult **`references/validations.md`**. This contains the strict rules and constraints. Use it to validate user inputs objectively.

**Note:** If a user's request conflicts with the guidance in these files, politely correct them using the information provided in the references.

Overview

This skill is a computer vision specialist for real-time hand tracking and gesture interface design. It focuses on reliable MediaPipe Hands integration, robust landmark processing, and practical gesture-to-action mapping for touchless interfaces. The skill balances high accuracy with low latency and emphasizes inclusive training data and clear UX fallbacks.

How this skill works

It inspects camera frames to detect and track up to two hands, extracts the 21 hand landmarks per hand, and normalizes them for downstream classifiers. Gesture recognition pipelines include rule-based detectors (pinch, fist, swipe) and lightweight ML classifiers for more complex signs. I validate outputs against pattern guidelines and known failure modes to recommend fixes and mitigations.

When to use it

Building touchless interfaces for kiosks, exhibits, or medical settings
Prototyping sign language or simple command recognition with webcams
Improving robustness of an existing MediaPipe-based hand tracker
Reducing gesture latency for real-time interactive applications
Designing accessible gesture sets and fallback input methods

Best practices

Prefer simple, high-precision gestures over complex finger configurations
Use normalized 2D landmarks first; add depth only when necessary
Train and test on diverse hands, skin tones, and real lighting conditions
Mitigate false positives with dwell thresholds and explicit activation gestures
Provide clear visual/voice feedback and keyboard or touch fallbacks

Example use cases

A museum installation that uses swipe and point gestures to navigate exhibits
A telemedicine viewer where pinch-to-zoom must work reliably on low-quality webcams
An accessibility tool mapping simple hand signs to OS-level shortcuts
A retail kiosk that uses a grab/pinch combo to select and manipulate items
Rapid prototyping of sign vocabulary recognition with rule-based filters

FAQ

How do I reduce false positives in gesture detection?

Add activation gestures or dwell time checks, require consistent landmark confidence across frames, and tune thresholds using validation patterns from the patterns reference.

Is 2D landmark data enough for reliable gestures?

Often yes—start with normalized 2D landmarks and only add depth or IMU fusion when gestures are ambiguous or when you need robust occlusion handling.