Hk101 Living Rag

What This Skill Does

Hk101 Living Rag is a streamlined Retrieval-Augmented Generation (RAG) tool designed specifically for local document interaction within the OpenClaw environment. This skill allows users to index and query local text-based content, such as Markdown files or raw text notes, directly from their local machine. By converting your static local documents into a searchable knowledge base, it enables the OpenClaw AI to provide context-aware answers based on your private information. The skill automates the retrieval process by identifying the most relevant segments (snippets) from your data and synthesizing a natural language response using OpenAI's powerful language models.

Installation

To integrate this skill into your OpenClaw workflow, execute the following command in your terminal:

clawhub install openclaw/skills/skills/metatronsdoob369/hk101-living-rag

Ensure that you have set the OPENAI_API_KEY environment variable in your system, as the skill requires this to process the retrieval and synthesis tasks via OpenAI's API. Without a valid API key, the skill will be unable to generate the natural language answers based on your document content.

Use Cases

Hk101 Living Rag is ideal for individuals or developers who need to interact with local knowledge repositories. Common use cases include: querying personal notes or technical documentation directories, summarizing long-form local research papers, quickly retrieving specific configuration instructions from local project readme files, or cross-referencing technical specifications saved as text files. It acts as a personal librarian for your local filesystem, allowing you to ask questions about your data rather than manually searching through hundreds of files.

Example Prompts

1. "Look in the default docs folder and explain the installation steps found for the new project."
2. "Based on my local notes, what are the primary troubleshooting steps for the production environment?"
3. "Search my documentation for any mentions of API rate limits and summarize the constraints."

Tips & Limitations

To get the best performance, ensure your documentation files are formatted cleanly, preferably in Markdown. Avoid overly complex file structures, as the skill works most effectively when documents are clearly categorized within the targeted folder. Note that the 'k' parameter controls how many snippets are retrieved; increasing this value may improve comprehensive context but could increase token usage costs. Currently, the skill is limited to local text and markdown files and requires an active internet connection to communicate with the OpenAI API for the RAG processing steps.