An automated AI intelligence gathering skill that aggregates and structures weekly reports on AI large model trends, specifically sourcing video insights from Bilibili.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install auto-llm-1644
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 auto-llm-1644 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 Auto LLM 1644?
Auto LLM 1644 is a dedicated intelligence-gathering and learning-expansion skill designed for Openclaw Skills ecosystems. Its primary purpose is to automate the tracking and compilation of technical developments, presentations, and tutorials related to large language models (LLMs). By indexing content from key video sources, it provides developer agents with quick access to structured summaries of recent industry milestones.
This specific skill processes curated entries from May 2026, focusing on academic presentations, local model orchestrations (like ComfyUI local LLM integration), reinforcement learning in deep learning frameworks, and real-time voice interaction systems combining ASR, LLM, and TTS technologies.
Auto LLM 1644 Use Cases
- Automatically fetching and updating a curated index of the latest LLM tutorials and academic papers from Bilibili.
- Providing instant context, video URLs, and knowledge cross-references when a user says "llm" or asks for the May 2026 weekly report.
- Serving as an automated knowledge expansion module for developer agents keeping track of pipeline advancements like ASR+LLM+TTS pipelines.
How Auto LLM 1644 Works
- The user triggers the agent using specific keywords like "llm" or the explicit weekly report title.
- The skill invokes its core engine driven by the underlying source file
auto_llm_1644.pymanaged under thelearn-expandcore. - The skill scans and parses metadata across multiple sequential learning rounds to extract author updates, video notes, and direct URLs.
- It formats the structured output as an easily navigable markdown report directly inside the conversation environment.
Auto LLM 1644 Setup
To deploy this skill within your local Openclaw Skills repository, ensure the source script is placed in your configuration path:
# Navigate to your local configuration folder
cd ~/.config/opencode/skills/
# Create the skill directory if it does not exist
mkdir -p auto_llm_1644
# Place your auto_llm_1644.py file into the directory
# The system will automatically register the learn-expand core hook
Auto LLM 1644 Data Schema & Taxonomy
The skill manages its assets locally and maintains data records based on chronological learning iterations. The structure maps out as follows:
| Attribute | Description |
|---|---|
| Core Classification | learn-expand |
| Source Controller | auto_llm_1644.py |
| Local Storage Path | file:///C:/Users/pc/.config/opencode/skills/auto_llm_1644 |
| Data Schema Output | Formatted iteratively into learning round snapshots tracking Creator Name, Video Topic, and Bilibili BV URL strings. |
Auto LLM 1644 Advanced Features
- Multi-Round Learning Tracking: Keeps historical records of multiple learning cycles (e.g., Round 1 and Round 2 logs) to ensure updates are captured if a resource changes.
- Implicit Keyword Hooking: Triggers seamlessly on broad industry shorthand terms such as "llm" without requiring the full specific report title.
- Direct Link Extraction: Automatically maps platform video IDs (BV keys) to valid web links for immediate consumption by downstream multi-agent research tools.
SKILL.md
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