infinite-gratitude
Multi-agent research skill for parallel research execution (10 agents, battle-tested with real case studies).
Best use case
infinite-gratitude is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Multi-agent research skill for parallel research execution (10 agents, battle-tested with real case studies).
Teams using infinite-gratitude should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/infinite-gratitude/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How infinite-gratitude Compares
| Feature / Agent | infinite-gratitude | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Multi-agent research skill for parallel research execution (10 agents, battle-tested with real case studies).
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Infinite Gratitude > **Source**: [sstklen/infinite-gratitude](https://github.com/sstklen/infinite-gratitude) ## Description A multi-agent research skill designed for parallel research execution. It orchestrates 10 agents to conduct deep research, battle-tested with real case studies. ## When to Use Use this skill when you need to perform extensive, parallelized research on a topic, leveraging multiple agents to gather and synthesize information more efficiently than a single linear process. ## How to Use This is an external skill. Please refer to the [official repository](https://github.com/sstklen/infinite-gratitude) for installation and usage instructions. ```bash git clone https://github.com/sstklen/infinite-gratitude ```
Related Skills
zustand-store-ts
Create Zustand stores with TypeScript, subscribeWithSelector middleware, and proper state/action separation. Use when building React state management, creating global stores, or implementing reacti...
zoom-automation
Automate Zoom meeting creation, management, recordings, webinars, and participant tracking via Rube MCP (Composio). Always search tools first for current schemas.
zoho-crm-automation
Automate Zoho CRM tasks via Rube MCP (Composio): create/update records, search contacts, manage leads, and convert leads. Always search tools first for current schemas.
zod-validation-expert
Expert in Zod — TypeScript-first schema validation. Covers parsing, custom errors, refinements, type inference, and integration with React Hook Form, Next.js, and tRPC.
zeroize-audit
Detects missing zeroization of sensitive data in source code and identifies zeroization removed by compiler optimizations, with assembly-level analysis, and control-flow verification. Use for auditing C/C++/Rust code handling secrets, keys, passwords, or other sensitive data.
zendesk-automation
Automate Zendesk tasks via Rube MCP (Composio): tickets, users, organizations, replies. Always search tools first for current schemas.
zapier-make-patterns
No-code automation democratizes workflow building. Zapier and Make (formerly Integromat) let non-developers automate business processes without writing code. But no-code doesn't mean no-complexity ...
youtube-summarizer
Extract transcripts from YouTube videos and generate comprehensive, detailed summaries using intelligent analysis frameworks
youtube-automation
Automate YouTube tasks via Rube MCP (Composio): upload videos, manage playlists, search content, get analytics, and handle comments. Always search tools first for current schemas.
yes-md
6-layer AI governance: safety gates, evidence-based debugging, anti-slack detection, and machine-enforced hooks. Makes AI safe, thorough, and honest.
yann-lecun
Agente que simula Yann LeCun — inventor das Convolutional Neural Networks, Chief AI Scientist da Meta, Prêmio Turing 2018. Use quando quiser: perspectivas sobre deep learning e visão...
yann-lecun-tecnico
Sub-skill técnica de Yann LeCun. Cobre CNNs, LeNet, backpropagation, JEPA (I-JEPA, V-JEPA, MC-JEPA), AMI (Advanced Machinery of Intelligence), Self-Supervised Learning (SimCLR, MAE, BYOL),...