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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.

So we made AI agents work like village cats: go out, bring gifts back, repeat.

The name "Infinite Gratitude" (無限報恩) comes from cats bringing "gifts" home — their way of saying thanks.

Full story: infinite-gratitude-story.md

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📜 License

MIT License

Pair With | 搭配使用 | 組み合わせ

• YES.md (sstklen/yes.md) — Keep your 10 research agents honest: safety gates, evidence rules, anti-slack detection. | 讓研究 Agent 守規矩：安全閘門 + 證據規則 | エージェントを規律正しく：安全ゲート＋証拠ルール
• 5x-cto (sstklen/5x-cto) — Done researching? Build it. Full dev pipeline from requirements to delivery. | 查完了？來蓋。完整開發流水線 | リサーチ完了？構築しよう。完全開発パイプライン

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Made with 🐾 by Washin Village — 和牠一起，療癒全世界