- Python 3.10+ with pip
- Claude Code CLI installed (docs)
- Your data in CSV, DuckDB, or a supported warehouse (Postgres, BigQuery, Snowflake)
git clone <repo-url> ai-analyst
cd ai-analyst
pip install -e ".[dev]"claudeOn first launch, Claude will detect a fresh install and start the interactive setup interview. It walks you through:
- Your role and team -- so Claude adapts its communication style
- Your data source -- CSV directory, DuckDB file, or warehouse connection
- Your business context -- what your company does, key metrics, team structure
- Your preferences -- output formats, chart style, export channels
You can also run the setup manually at any time:
/setup
Once setup is complete, just ask a question:
What's our conversion rate by device type?
Or run the full analysis pipeline:
/run-pipeline
Place your CSV files in a directory (e.g., data/my_dataset/) and tell Claude
during setup. Each .csv file becomes a queryable table.
Point Claude to a .duckdb file during setup. DuckDB provides fast SQL
queries over local data.
For Postgres, BigQuery, or Snowflake connections, you'll need to configure
MCP (Model Context Protocol) servers. Run /connect-data and follow the
prompts.
To start fresh:
/setup reset
- Tier 1 reset -- clears your profile and preferences
- Tier 2 reset -- clears everything including dataset connections
python -m pytest tests/ -vai-analyst/
.claude/skills/ -- Claude skill definitions (auto-applied behaviors)
.knowledge/ -- Knowledge system (populated by setup and usage)
agents/ -- Agent prompt templates (multi-step workflows)
helpers/ -- Python utility modules
tests/ -- Pytest test suite
data/ -- Your datasets (gitignored)
docs/ -- Documentation
outputs/ -- Analysis outputs (charts, decks, narratives)
working/ -- Intermediate work files