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🐾 無限貓報恩 | Infinite Gratitude | 無限の恩返し

Dispatch 10 parallel research agents — like having a team of researchers working for you simultaneously

⚡ Quick Start

# Install (one command!)
curl -sSL https://raw.githubusercontent.com/sstklen/infinite-gratitude/main/infinite-gratitude.skill.md \
  -o ~/.claude/skills/infinite-gratitude.skill.md

# Use in Claude Code
/infinite-gratitude "your research topic"

💡 What It Does

Problem: Deep research takes hours. Reading papers, comparing tools, analyzing competitors — one person can only do so much.

Solution: Dispatch multiple AI agents in parallel. Each agent researches a different angle, then brings findings back.

You: "Research pet AI recognition"
     ↓
🐱🐱🐱🐱🐱 5 agents go out (parallel)
     ↓
📊📊📊📊📊 Each brings back a report
     ↓
You: "Great! Now go deeper on ArcFace..."
     ↓
🔄 Loop until satisfied

Like cats bringing gifts home — mice, bugs, leaves. This skill keeps bringing research findings until you say stop.

📊 Real Results: Pet AI Research

We used this skill to research building an AI system for recognizing 28 cats & dogs.

Metric	Result
Research Topics	12
Agents Deployed	10 (parallel)
Reports Generated	9
Time	30 minutes (vs 20+ hours manual)
Key Discovery	Petnow's 99% accuracy secret

Reports Produced

#	Report	Key Finding
1	Competitor Analysis	Petnow leads with 99% accuracy
2	Dataset Survey	Oxford-IIIT Pet is commercially safe
3	Technical Roadmap	ArcFace > Triplet Loss for stability
4	GitHub Projects	MegaDescriptor is the best pretrained model
5	HuggingFace Models	DINOv2 for general, MegaDescriptor for animals
6	Petnow Deep Dive	Siamese + Self-Attention + 200K data
7	Loss Function Guide	ArcFace vs Triplet comparison
8	Business Model	Pet insurance is the money maker
9	Data Formula	10K→85%, 50K→92%, 200K→99%

Outcome: Achieved 77.6% accuracy, with clear roadmap to 90%+.

🔧 Configuration

# Basic usage
/infinite-gratitude "topic"

# Deep research (more thorough)
/infinite-gratitude "RAG best practices" --depth deep

# Control agent count
/infinite-gratitude "vector databases" --agents 10

# Multiple waves
/infinite-gratitude "embedding models" --waves 5

Parameter	Default	Description
--depth	normal	quick, normal, deep
--agents	5	Parallel agents (1-10)
--waves	3	Research iterations

🎯 Best Use Cases

Use Case	Why It Works
Technical Research	Compare 10 tools/libraries simultaneously
Competitor Analysis	Each agent analyzes a different competitor
Literature Review	Parallel paper reading and summarization
Market Research	Multi-angle industry analysis
Due Diligence	Comprehensive background checks

📁 Files

├── infinite-gratitude.skill.md   # ← Install this!
├── infinite-gratitude-story.md   # Full origin story
└── docs/                         # Additional documentation

🐾 Origin Story

In Japan's Boso Peninsula, Washin Village is home to 28 cats and dogs. While building their AI recognition platform, there was too much research for one person.