@nikosv/trustmebro
v0.2.1
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Fetch and index social media content for agentic workflows
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TrustMeBro
Turn social media noise into a searchable knowledge base for AI agents.
Fetch content from YouTube, Reddit, and more — process it with LLMs — and let your AI agent search, analyze, and synthesize insights across all of it.
Supported platforms: YouTube, Reddit · Coming soon: Telegram, Twitter
Why TrustMeBro?
Ever felt that low-key anxiety when you're 47 videos behind on your favorite finance YouTubers, three Reddit threads deep at 2 AM, and you still feel like you're missing something important? The FOMO is real. You can't watch everything, read everything, and remember everything — and yet somehow you're supposed to make informed decisions based on all of it.
We've all been there, and TrustMeBro is here to help.
It fetches all the content you care about, runs it through an LLM, and spits out a searchable, tagged, grep-friendly knowledge base. Then you point ClaudeCode (or any other AI agent) at it and ask questions like a normal person instead of doom-scrolling until 3 AM.
| The pain | The fix |
|---|---|
| 47 unwatched videos, infinite Reddit scroll | Automated fetching — pulls everything from your tracked sources |
| "I saw a great take on NVDA last week... somewhere" | Persistent storage — every piece of content saved as searchable Markdown |
| Can't ctrl+F your YouTube watch history | Structured tagging — LLM extracts tickers, sentiment, topics, narratives |
| Alt-tabbing between 6 platforms like a maniac | Agent-ready output — ask your AI to synthesize it all in one place |
Quick Start
1. Install
# npm
npm install -g @nikosv/trustmebro
# bun
bun install -g @nikosv/trustmebro
# or run directly without installing
npx @nikosv/trustmebro index
bunx @nikosv/trustmebro indexPrerequisite: yt-dlp is required for YouTube fetching.
2. Create a Workspace
mkdir -p ~/stocks && cd ~/stocksCreate a config.yaml in your workspace directory. See config.template.yaml for a fully documented example with inline comments.
3. Configure the Model
TrustMeBro uses an LLM to process and tag content. It relies on AI SDK providers for model access, so you can choose from a wide range of providers and models based on your needs and budget.
Set your API key as an environment variable:
export OPENAI_API_KEY="sk-..." # or add to ~/.bashrc, ~/.zshrc, etc.Reference it in
config.yaml:indexing: workers: 5 model: provider: "@ai-sdk/openai" model: "gpt-4o-mini" options: apiKey: "env.OPENAI_API_KEY" # References environment variable
Tip: Use
"env.VARIABLE_NAME"syntax in your config to keep secrets out of config files.
4. Configure Sources and Tags
Edit your config.yaml to specify:
startDate— Fetch content from this date forward (YYYY-MM-DD)topic— High-level description for LLM contextsources.youtube.publishers— YouTube channel handles (with@prefix)sources.reddit.publishers— Subreddit names to tracktags— Structured metadata to extract (tickers, sentiment, sectors, narratives, etc.)
See config.template.yaml for detailed documentation on all options.
5. Run Indexing
cd ~/stocks
# Index all configured sources
trustmebro index
# Index a specific source
trustmebro index --source youtube
# Index a specific publisher
trustmebro index --source youtube --publisher @JosephCarlsonShow
# Use a different workspace
trustmebro index --workspacePath /path/to/workspaceHow It Works
TrustMeBro runs a three-step pipeline:
- Fetch — Pulls content since
startDate(or last sync) from all configured sources - Process — Chunks, tags, and enriches content with LLMs using your configured tag schema
- Write — Outputs grep-friendly Markdown organized by source, publisher, and date
After indexing, your workspace looks like this:
~/stocks/
.trustmebro/ # Internal state (auto-managed)
data/
social/
raw/ # Original fetched content
youtube/
everythingmoney/
2025-12/
2025-12-15-nvidia-earnings-analysis.md
reddit/
investing/
2025-12/
2025-12-20-best-etf-for-long-term-growth.md
processed/ # LLM-enriched, search-optimized content
youtube/
everythingmoney/
2025-12/
2025-12-15-nvidia-earnings-analysis.md
reddit/
investing/
2025-12/
2025-12-20-best-etf-for-long-term-growth.md
config.yamlAI Agent Integration
The real power of TrustMeBro is pairing indexed content with AI agents that can search and reason over it.
Generate Agent Skills
TrustMeBro can generate skills that teach your agent to search indexed content with ripgrep:
cd ~/stocks
trustmebro generate skillsSelect your agent type and TrustMeBro will generate a skill per source (e.g., search-youtube, search-reddit) tailored to your topic, tags, and directory structure.
These skills are a good starting point — customize them to fit your content domain.
Start Chatting
Once set up, ask Claude Code (or any other agent):
"What's the sentiment on NVIDIA in the last month?"
"Find all strong buy recommendations from December 2025"
"Compare sentiment on Tesla across YouTube vs Reddit"
"What narratives are trending in tech stocks this quarter?"
"Show me dividend stock discussions with hold or sell recommendations"
Your agent will use ripgrep to search the indexed content and synthesize insights across sources.
Use Cases
Stock Market Research
- Create a workspace
~/stockswith your favorite YouTube finance channels and investing subreddits - Connect finance-related MCP servers (Yahoo Finance, Seeking Alpha, etc.)
- Store your portfolio holdings and watchlists alongside the indexed content
- Ask your agent to find sentiment, recommendations, narratives, and insights
Health & Wellness
- Create a workspace
~/healthwith YouTube health channels and medical subreddits - Connect medical MCP servers (PubMed, Medscape, etc.)
- Store your medical history and test results in the workspace
- Ask your agent about trends, new research, supplements, and recommendations specific to your goals
Real Estate
- Create a workspace
~/realestatewith real estate channels and subreddits for your market - Connect real estate MCP servers and write skills for accessing government APIs (property records, zoning, etc.)
- Ask your agent about market trends, specific properties, suburbs, and investment opportunities
License
MIT © Sergii Vashchyshchuk
