Noverload provides your AI agent with a semantic knowledge base, allowing it to search, synthesize, and extract actions from your saved YouTube videos, articles, and social media posts.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install noverload
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install noverload using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Noverload?
Noverload acts as a centralized knowledge memory for your AI agents, transforming fragmented information from across the web into a queryable brain. By integrating this skill, your agent gains the ability to perform semantic searches that understand meaning rather than just keywords, ensuring highly relevant information retrieval from your personal library of YouTube transcripts, Reddit threads, and PDFs.
This integration leverages Openclaw Skills to bridge the gap between static saved content and active agent workflows. Whether you are conducting deep research or looking for specific frameworks hidden in your archives, Noverload processes every piece of content to extract key insights, automatically identifying action items and takeaways to keep you productive and informed.
Noverload Use Cases
- Semantic Knowledge Retrieval: Query your agent for specific concepts or advice across hundreds of saved articles and videos without needing exact keyword matches.
- Automated Research Synthesis: Ask your agent to compare perspectives or find contradictions between multiple sources in your library, such as different startup growth strategies.
- Dynamic Action Tracking: Automatically extract tasks and to-dos from consumed content and organize them by goals like Health, Wealth, or Relationships.
- Framework Extraction: Instantly pull out step-by-step methodologies and processes from complex long-form content like podcasts or technical whitepapers.
How Noverload Works
- The agent connects to your Noverload account using a secure personal access token.
- When you issue a command, the agent uses the Model Context Protocol (MCP) to query your library semantically.
- Noverload processes the request by searching through indexed transcripts, OCR-extracted text, and AI-generated summaries.
- The skill returns structured data—including insights, frameworks, or action items—directly into the agent's context window.
- If write access is enabled, the agent can also save new URLs, tag content, and update the status of extracted tasks.
Noverload Setup
To begin using Noverload with Openclaw Skills, follow these installation steps:
- Sign up for an account at noverload.com.
- Navigate to Settings > Apps and generate a New Token.
- Open your configuration file at
~/.openclaw/openclaw.jsonand add your token:
{
"skills": {
"entries": {
"noverload": {
"env": {
"NOVERLOAD_TOKEN": "nv_your_token_here"
}
}
}
}
}
- Restart your agent to activate the skill and begin querying your knowledge base.
Noverload Data Schema & Taxonomy
Noverload organizes information into a structured taxonomy optimized for AI retrieval:
| Data Type | Content Extracted |
|---|---|
| YouTube | Transcripts, timestamps, key moments, and action items |
| Articles | Full text, main arguments, notable quotes, and frameworks |
| Social (X/Reddit) | Threads, top comments, discussion themes, and linked content |
| PDFs | Full OCR-processed text, document structure, and highlights |
| Metadata | Tags, source URLs, swipe file status, and goal categorization (Health, Wealth, Relationships) |
Noverload Advanced Features
- Read/Write Capability: Configure the skill with readOnly: false to allow agents to actively save new bookmarks and complete tasks directly from the chat.
- Cross-Source Synthesis: The ability to identify patterns and connections across disparate media formats, such as connecting a podcast insight to a Reddit discussion.
- Goal-Oriented Action Tracking: Automatically maps extracted tasks to specific life or professional domains for better organization.
- Semantic Discovery: Uses vector embeddings to find relevant content even when the user query uses different terminology than the source text.
SKILL.md
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