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What it does
Infinite-gratitude is a multi-agent automation tool that dispatches parallel agents to research a topic, compile findings, and iteratively refine discoveries.
Skill profile
Keep exploring
More options in Automation.
Claude Code · Codex · OpenClaw
Updated 3/15/2026
Agent compatibility
Compatibility has not been reviewed for this listing yet. Check the publisher documentation before installing.
Installation
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infinite-gratitude is organized in the Automation category. Compare its source, install method, and compatibility before adding it to your workflow.
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npx skills add https://github.com/sstklen/infinite-gratitude --skill rootSKILL.md
---
name: infinite-gratitude
description: Multi-agent research that keeps bringing gifts back — like cats! Dispatch multiple agents to research a topic in parallel, compile findings, and iterate on new discoveries.
argument-hint: "<topic>" [--depth quick|normal|deep] [--agents 1-10]
---
# Infinite Gratitude 🐾
> 無限貓報恩 | 無限の恩返し
> Multi-agent research that keeps bringing gifts back — like cats! 🐱
## Quick Reference
| Option | Values | Default |
|--------|--------|---------|
| `topic` | Required | - |
| `--depth` | quick / normal / deep | normal |
| `--agents` | 1-10 | 5 |
## Usage
```bash
/infinite-gratitude "pet AI recognition"
/infinite-gratitude "RAG best practices" --depth deep
/infinite-gratitude "React state management" --agents 3
```
## Behavior
### Step 1: Split Directions
Split `{topic}` into 5 parallel research directions:
1. GitHub projects
2. HuggingFace models
3. Papers / articles
4. Competitors
5. Best practices
### Step 2: Dispatch Agents
```
Task(
prompt="Research {direction} for {topic}...",
subagent_type="research-scout",
model="haiku",
run_in_background=True
)
```
### Step 3: Collect Gifts
Compile all findings into structured report.
### Step 4: Loop
If follow-up questions exist → Ask user → Continue? → Back to Step 2
### Step 5: Final Report
## Example Output
```
🐾 Infinite Gratitude!
📋 Topic: "pet AI recognition"
🐱 Dispatching 5 agents...
━━━━━━━━━━━━━━━━━━━━━━
🎁 Wave 1
━━━━━━━━━━━━━━━━━━━━━━
🐱 GitHub: MegaDescriptor, wildlife-datasets...
🐱 HuggingFace: DINOv2, CLIP...
🐱 Papers: Petnow uses Siamese Network...
🐱 Competitors: Petnow 99%...
🐱 Tutorials: ArcFace > Triplet Loss...
💡 Key: Data volume is everything!
🔍 New questions:
- How to implement ArcFace?
- How to use MegaDescriptor?
Continue? (y/n)
🐾 by washinmura.jp
```
## Notes
- Uses `haiku` model to save cost
- Max 5 agents per wave
- Deep mode loops until satisfied
## Additional Resources
- For agent configuration, see [references/agent-config.md](references/agent-config.md)
## Related Skills
- **ai-dojo** — Foundation for AI coding agents
- **research-scout** — Single-agent research
---
*Part of 🥋 AI Dojo Series by [Washin Village](https://washinmura.jp) 🐾*
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