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- YouTube Content Pipeline: Automated video idea scouting with X/Twitter integration - Personal CRM: Contact discovery and meeting prep automation - AI Earnings Tracker: Tech earnings monitoring and summaries - Knowledge Base (RAG): Searchable repository of saved content - Health & Symptom Tracker: Food and symptom logging with pattern analysis - Multi-Channel Assistant: Unified interface across Telegram, Slack, Google Workspace All use cases verified in daily production use by @matthewberman
37 lines
1.6 KiB
Markdown
37 lines
1.6 KiB
Markdown
# Personal Knowledge Base (RAG)
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You read articles, tweets, and watch videos all day but can never find that one thing you saw last week. Bookmarks pile up and become useless.
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This workflow builds a searchable knowledge base from everything you save:
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• Drop any URL into Telegram or Slack and it auto-ingests the content (articles, tweets, YouTube transcripts, PDFs)
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• Semantic search over everything you've saved: "What did I save about agent memory?" returns ranked results with sources
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• Feeds into other workflows — e.g., the video idea pipeline queries the KB for relevant saved content when building research cards
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## Skills you Need
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- [knowledge-base](https://clawhub.ai) skill (or build custom RAG with embeddings)
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- `web_fetch` (built-in)
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- Telegram topic or Slack channel for ingestion
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## How to Set it Up
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1. Install the knowledge-base skill from ClawdHub.
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2. Create a Telegram topic called "knowledge-base" (or use a Slack channel).
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3. Prompt OpenClaw:
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```text
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When I drop a URL in the "knowledge-base" topic:
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1. Fetch the content (article, tweet, YouTube transcript, PDF)
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2. Ingest it into the knowledge base with metadata (title, URL, date, type)
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3. Reply with confirmation: what was ingested and chunk count
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When I ask a question in this topic:
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1. Search the knowledge base semantically
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2. Return top results with sources and relevant excerpts
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3. If no good matches, tell me
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Also: when other workflows need research (e.g., video ideas, meeting prep), automatically query the knowledge base for relevant saved content.
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```
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4. Test it by dropping a few URLs and asking questions like "What do I have about LLM memory?"
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