local-researcher

What This Skill Does

The Local Researcher Skill is a powerful, privacy-focused agent for OpenClaw that performs autonomous, iterative web research entirely on your local machine. By leveraging local LLMs via Ollama or LMStudio, it eliminates the need to send sensitive research queries to cloud-based services. The skill conducts multi-step research cycles: generating search queries, fetching content, analyzing findings, and identifying knowledge gaps to refine its next steps. It concludes by synthesizing a high-quality, professional Markdown report complete with inline citations and a full list of sources.

Installation

To install this skill, run the following command in your terminal: clawhub install openclaw/skills/skills/antonia-sz/local-researcher Ensure you have Ollama or LMStudio installed locally with an accessible API server. You will also need to configure your environment variables in a .env file to point to your preferred LLM model and search API provider.

Use Cases

• Academic Research: Rapidly synthesize existing literature on specific scientific or technical topics without exposing your interests to third parties.
• Market Analysis: Gather business insights or competitor data by iterating through web search results.
• Private Documentation Analysis: Conduct research on sensitive internal projects where cloud data privacy is a primary concern.
• Tech Stacks & Tutorials: Automatically generate comprehensive guides on new programming libraries or frameworks by aggregating the latest docs and community discussions.

Example Prompts

1. "Perform a deep dive into the current state of local LLM orchestration and create a comparative report for me."
2. "Research the latest advancements in quantum computing for drug discovery and summarize the findings with source links."
3. "Investigate how to optimize Python code for low-latency systems and generate a comprehensive study document."

Tips & Limitations

• Model Selection: For better reasoning, use high-parameter models like qwen:14b or deepseek-r1:8b. Smaller models may struggle with long-form synthesis.
• Search APIs: While DuckDuckGo is free and privacy-focused, using Tavily or Perplexity with an API key often yields higher-quality, more relevant results.
• Resource Management: Local research can be hardware-intensive. Ensure you have sufficient RAM when running large models alongside web scraping tasks.
• Loop Limits: Start with the default MAX_WEB_RESEARCH_LOOPS of 3 to prevent excessive local processing time.