agnes-the-ai-analyst/CLAUDE.md

AI Data Analyst

Open-source data distribution platform for AI analytical systems. Syncs data from various sources, converts to Parquet, and distributes to analysts who use Claude Code for local analysis.

First-Time Setup

When a user opens this project for the first time, guide them through interactive setup:

Step 1: Gather Information

Ask the user for:

1. Company domain (e.g., "acme.com") - used for Google OAuth
2. Data source type: keboola / csv / bigquery (future)
3. Instance name (e.g., "Acme Data Analyst")

Step 2: Generate Configuration

1. Copy config/instance.yaml.example to config/instance.yaml
2. Fill in values from Step 1
3. If Keboola: ask for Storage API token, stack URL, project ID
4. Create .env from config/.env.template

Step 3: Generate Data Description

1. If Keboola adapter: use the API to fetch table metadata and generate docs/data_description.md
2. If CSV: ask user to describe their data files
3. The file defines tables, sync strategies, and schema

Step 4: Server Setup (if deploying)

1. Guide VM provisioning (or use existing server)
2. Run server/setup.sh on the target VM
3. Run server/webapp-setup.sh for the web portal
4. Set up CI/CD from .github/workflows/deploy.yml.example

Project Structure

├── src/                    # Core data sync engine
│   ├── adapters/           # Data source adapters (Keboola, CSV, etc.)
│   ├── config.py           # Configuration from data_description.md
│   ├── data_sync.py        # Sync orchestration
│   ├── parquet_manager.py  # Parquet file management
│   └── profiler.py         # Data profiling
├── webapp/                 # Flask web portal (login, dashboard, API)
├── server/                 # Server deployment (systemd, scripts)
├── scripts/                # Utility scripts (sync, DuckDB setup)
├── config/                 # Configuration templates
│   ├── instance.yaml.example
│   └── data_description.md.example
├── docs/                   # Documentation
└── tests/                  # Test suite

Architecture

Data Source (Keboola / CSV / BigQuery)
      │
      ▼
┌─────────────────────────────────┐
│  Data Broker Server             │
│  ├── /data/src_data/parquet/    │  Converted data
│  ├── /data/docs/                │  Documentation
│  └── /data/scripts/             │  Helper scripts
└─────────────────────────────────┘
      │ rsync (via ~/server/ symlinks)
      ▼
┌─────────────────────────────────┐
│  Analyst (local machine)        │
│  ├── ./server/  (read-only)     │  parquet, docs, scripts
│  └── ./user/    (workspace)     │  duckdb, notifications
└─────────────────────────────────┘

Configuration

Instance-specific config is in config/instance.yaml. See config/instance.yaml.example for all options.

Environment variables go in .env (never committed to git).

Data schema is defined in docs/data_description.md (YAML blocks in markdown).

Development

# Setup
python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

# Run webapp locally
flask --app webapp.app run --debug

# Run tests
pytest tests/ -v

# Sync data
python -m src.data_sync

Data Source Adapters

The platform supports pluggable data sources via src/adapters/:

• Keboola (keboola): Syncs from Keboola Storage API
• CSV (csv): Import from local CSV files (planned)
• BigQuery (bigquery): Query from Google BigQuery (planned)

Configure in config/instance.yaml under data_source.type.

Server Management

# Add analyst user
sudo add-analyst username "ssh-rsa AAAA..."

# Add privileged analyst
sudo add-analyst username "ssh-rsa AAAA..." --private

# List analysts
list-analysts

# Server monitoring
uptime && free -h && df -h /data

Returning Users

When reopening the project in Claude Code:

1. Sync latest data: bash server/scripts/sync_data.sh
2. Verify DuckDB: ls -lh user/duckdb/analytics.duckdb
3. Start analyzing with Claude Code

Key Implementation Details

Config Loading Chain

1. config/loader.py loads instance.yaml (checks $CONFIG_DIR, then ./config/)
2. webapp/config.py calls _load_instance_config() at module level
3. _get(config, *keys, default="") traverses nested dicts safely
4. inject_config() context processor exposes Config to all Jinja templates
5. Templates use {{ config.INSTANCE_NAME }}, {{ config.INSTANCE_SUBTITLE }}, etc.

Adapter Pattern

• Factory: src/adapters/__init__.py -> create_data_source(adapter_type, **kwargs)
• ABC: DataSource class in src/data_sync.py (lines 149-172)
• Keboola: src/adapters/keboola_adapter.py -> thin facade wrapping LocalKeboolaSource
• Core Keboola logic: src/keboola_client.py (788 lines, Keboola Storage API wrapper)

Server Patterns

• Atomic JSON writes: tempfile.mkstemp() + os.fchmod(fd, 0o660) + os.replace()
• User home writes: sudo /usr/bin/install -o {user} -g {user} pattern
• Staging dir: /tmp/data_analyst_staging (deploy.sh creates it with setgid)
• Dev docs: dev_docs/server.md documents all established patterns

Files NOT to modify (stable infrastructure)

• src/parquet_manager.py - Parquet conversion engine
• connectors/jira/file_lock.py - Advisory file locking
• connectors/jira/incremental_transform.py - Jira monthly Parquet transform
• server/ws_gateway/ - WebSocket notification gateway

Git Commits & Pull Requests

• Keep commit messages clean and concise
• Do not include AI attribution in commits or PRs