auto-target-tracker

Detect target-related images in conversations and extract progress details with a local vision-language model.

Updated Apr 28, 2026
One-click install
npx skills add https://github.com/ncsound919/deterministic-brain --skill auto-target-tracker-ncsound919
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: auto-target-tracker
Source: https://github.com/ncsound919/deterministic-brain/tree/main/skills/auto-target-tracker
Command: npx skills add https://github.com/ncsound919/deterministic-brain --skill auto-target-tracker-ncsound919

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

The tool helps individuals automatically detect and log progress data from target-related images in conversations, eliminating manual note-taking and increasing accountability.

Core Features & Use Cases

  • Identify target-related images in chats and extract key progress data using a local vision model.
  • Record progress entries into daily notes, with options to summarize and provide feedback.
  • Support scenarios across learning, fitness, work, creative projects, and habit tracking, with privacy-preserving storage.

Quick Start

Ask it to auto-detect target-related images in your conversation and log key progress data to your daily notes.

Frequently Asked Questions about auto-target-tracker

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I automatically log habit tracking progress from images into my daily notes?▼

You can automatically log habit tracking progress by detecting target-related images in your conversation and extracting key progress data into daily notes. The tool validates detections locally and provides feedback without manual note-taking.

Can I use image recognition to track fitness targets without manually typing my logs?▼

Image recognition tracks fitness targets by detecting target-related images in chats and extracting key progress details. It automatically records progress entries into daily notes, eliminating manual typing and increasing accountability.

How does a local vision-language model work for personal productivity progress logging?▼

A local vision-language model works for progress logging by detecting target-related images in conversations and extracting key progress details. It processes learning, fitness, work, creation, and habit-tracking scenarios, storing results locally to preserve privacy.

Does this target tracking tool upload my personal data beyond the VLM API?▼

No, this target tracking tool does not upload personal data beyond the VLM API. It stores results locally, validates detections, and ensures privacy-preserving storage for your learning, fitness, work, and habit-tracking scenarios.

What's the best way to start auto-detecting target-related images for progress logging?▼

The best way to start progress logging is to simply ask the tool to auto-detect target-related images in your conversation. It will then extract key progress data, record entries into daily notes, and provide feedback automatically.