hralet1/cavalry-assistant

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What it does

• Write & run scripts — Claude executes Cavalry JavaScript live via the Stallion bridge
• Knows the docs — searches 40,000+ chunks of Cavalry documentation, real scenes, and community knowledge semantically
• Scene-aware — reads and saves your active scene
• Stays current — auto-loads a verified best-practices reference on every session

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Requirements

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Quick Setup (pre-built knowledge base)

1. Clone and install

git clone  cavalry-assistant
cd cavalry-assistant
cp .env.example .env
npm install --prefix mcp
npm run build --prefix mcp

2. Download the knowledge base

Download lancedb_public.zip from GitHub Releases, extract into the project:

cavalry-assistant/
  data/
    lancedb_public/    ← extract here

Update .env:

LANCEDB_PATH=./data/lancedb_public

Windows / network drive users: LanceDB requires a local drive. Set an absolute local path: LANCEDB_PATH=C:/Users/yourname/.cavalry-assistant/lancedb_public

3. Start Ollama

ollama pull nomic-embed-text

4. Enable Stallion in Cavalry

Scripts > Stallion — leave it running on port 8080.

5. Connect to Claude Code

The .mcp.json in this folder auto-registers when opened in Claude Code. Or manually:

claude mcp add cavalry-assistant node mcp/dist/index.js

6. Start a session

/cavalry

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Knowledge Base

The public knowledge base does not include Discord community data (private).

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Building the Knowledge Base (contributors)

1. Set up Python

cd etl
pip install -r requirements.txt

2. Configure .env

LANCEDB_PATH=C:/Users/yourname/.cavalry-assistant/lancedb
DISCORD_TOKEN=your_token
DISCORD_CHANNEL_IDS=channel_id_1,channel_id_2
SCENERY_COOKIE=your_session_cookie

3. Run ETL

cd ..  # project root

# Public sources
python etl/ingest.py --source docs
python etl/ingest.py --source scenery
python etl/ingest.py --source manual
python etl/ingest.py --source scripts