image-mining
I mine pixels for atoms. Reality is just compressed resources.
npx skills add majiayu000/claude-skill-registry --skill image-mining-simhacker-moollm-3 --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
Converts images into mined resource representations by applying a structured mining workflow to extract elements (like resources, colors, notes) and outputs them in YAML Jazz format.
How it works
The skill defines a pipeline where an image is analyzed to identify mineable resources. It supports multiple analyzers (pre-processors, custom analyzers, LLM vision, post-processors) and can incorporate external models via the extensible analyzer pipeline. It culminates in a final YAML Jazz output that encodes resources, context, and insights, with inline comments and metadata.
When to use it
Use when you need to extract semantically meaningful resources from images (photos, graphs, UI screenshots, diagrams) and want the results formatted as YAML Jazz with contextual notes and confidence data.
What it can touch
Tools allowed: read_file, write_file. The workflow relies on the CLI pipeline (mine.py) and analyzers to process images and generate the YAML Jazz output.
Caveats
License: MIT. The description emphasizes extensibility and interpretation by LLM vision; outcomes depend on integrated analyzers and models.
# Image Mining > *"I mine pixels for atoms. Reality is just compressed resources."* > > *"Every image is a lode. Every pixel, potential ore."* **Image Mining** extends the Kitchen Counter's DECOMPOSE action to **images**. Your camera isn't just a recorder — it's a **PICKAXE FOR VISUAL REALITY**. --- ## 📑 Index **Quick Start** - [The Core Insight](#the-core-insight) - [Preferred Mode: Native LLM Vision](#preferred-mode-native-llm-vision) **Operation Modes** - [When to Use Remote API](#when-to-use-remote-api) - [What Can Be Mined](#what-can-be-mined) **Extensibility** - [Extensible Analyzer Pipeline](#extensible-analyzer-pipeline) - [Leela Customer Models](#leela-customer-models) - [Adding Your Own Analyzer](#adding-your-own-analyzer) **Protocols** - [YAML Jazz Output Style](#yaml-jazz-output-style) - [How Mining Works](#how-mining-works) - [Character Recognition](#character-recognition) - [Multi-Look Mining](#multi-look-mining) **Reference** - [Depth Levels](#depth-levels) - [Resource Categories](#resource-categories) - [Example Outputs](#example-outputs) --- ## The Core Insight ``` 📷 Camera Shot → 🖼️ Image → ⛏️ MINE → 💎 Resources ``` Just like the Kitchen Counter breaks down: -
- 📑 Index
- The Core Insight
- Preferred Mode: Native LLM Vision
- Why Native Beats Remote API
- When to Use Remote API
- Native Mode Workflow
- What Can Be Mined
- Source Examples
- Extensible Analyzer Pipeline
- Example: Multi-Analyzer Pipeline
- Leela Customer Models
- Adding Your Own Analyzer
- Why Pipeline Beats Monolith
- YAML Jazz Output Style
python mine.py image.png --context room.yml --characters chars/ --prior mined.yml DO this (in Cursor/Claude): mine.py fashion-shoot.jpg \ From Leela model registry mine.py widget-photo.jpg --analyzer leela://customer-id/defect-detector-v3 Local model file mine.py widget-photo.jpg --analyzer ./models/my-classifier.pt Generate through a character's eyes visualize.py PHOTO.yml PHOTO.md --through "Luna" -o luna-vision.png Generate through multiple characters (batch)
What does the image-mining skill do?
I mine pixels for atoms. Reality is just compressed resources.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill image-mining-simhacker-moollm-3 --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 majiayu000/claude-skill-registry, a repository with 534 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.