What is data-product-hub CLAUDE.md?

A project instruction file for the data-product-hub Python repository. It documents setup, code checks, tests, building, publishing, and the main parts of the application.

Use it when installing dependencies, running linting or type checks, running pytest, building Python packages, publishing to PyPI, or locating the command-line, model-processing, AI, and report-generation code.

It gives a coding agent the repository-specific commands and structure it needs to work consistently. It also records that dbt analysis and OpenAI-based recommendations are part of the project.

How armalite/data-product-hub configures Claude Code

Reuse

Borrowing it

Nothing to install: this file belongs to armalite/data-product-hub. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file

curl -O https://raw.githubusercontent.com/armalite/data-product-hub/main/CLAUDE.md

Clone the repo

git clone --depth 1 https://github.com/armalite/data-product-hub

Made for: Claude Code.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/instructions/armalite/data-product-hub/claude-md.svg)](https://agentmods.dev/instructions/armalite/data-product-hub/claude-md)

Your own site

<a href="https://agentmods.dev/instructions/armalite/data-product-hub/claude-md"><img src="https://agentmods.dev/badge/instructions/armalite/data-product-hub/claude-md.svg" alt="Measured on agentmods" height="20"></a>

Per session 878 This file is loaded in full into every session.

When invoked 878 The same file — it is already loaded in full.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5.1	$0.00878	$0.00878
Opus 5	$0.00439	$0.00439
Sonnet 5	$0.00176	$0.00176
Haiku 4.5	$0.00088	$0.00088

Measured 7d ago against content hash 4cd4452944e0, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-07, from the pricing page.

Security

Grade A, and why

data-product-hub CLAUDE.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

CLAUDE.md · 95 lines

How it starts

The opening of the file, as written. The whole thing — 95 lines — stays where its author put it; the contents beside it link to each section on GitHub.

CLAUDE.md

This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.

Development Commands

Environment Setup

make install    # Create venv, install dependencies, setup git hooks
source .venv/bin/activate  # Activate virtual environment

Code Quality and Testing

make check      # Run linting and type checking
make lint       # Format code with black and run ruff linter
make pyright    # Run pyright type checker
make test       # Run pytest test suite

Building and Publishing

make dist       # Build Python distribution packages
make publish    # Publish to PyPI (requires credentials)

Individual Test Commands

pytest                    # Run all tests
pytest tests/test_*.py    # Run specific test file
pytest -v                 # Verbose test output

Architecture Overview

Core Components

CLI Entry Point: data_product_hub/main.py - Command-line interface with argparse
Model Processor: data_product_hub/dbt.py - DbtModelProcessor class handles dbt project analysis
AI Integration: data_product_hub/ai.py - OpenAI API integration for generating recommendations and models
Report Generation: data_product_hub/report.py - HTML report generation with Jinja2 templates
Configuration: data_product_hub/config.py - Environment variable configuration management
Utilities: data_product_hub/helper.py - YAML processing and utility functions

Key Classes

DbtModelProcessor - Main analysis class that:
- Parses dbt SQL model files and metadata YAML files
- Validates model metadata coverage
- Generates lineage graphs using NetworkX
- Supports multiple database types (Snowflake, PostgreSQL, Redshift, BigQuery)
- Provides both basic and advanced AI recommendations

AI Model Configuration

The application uses environment variables for AI model selection:

DBT_AI_BASIC_MODEL - Model for basic recommendations (default: gpt-4o-mini)
DBT_AI_ADVANCED_MODEL - Model for advanced recommendations (default: gpt-4o)
DBT_AI_FALLBACK_MODEL - Fallback model (default: gpt-3.5-turbo)
DBT_AI_MAX_TOKENS - Maximum tokens per API call (default: 4000)
DBT_AI_TEMPERATURE - AI response temperature (default: 0.1)

Read the full file on GitHub · 95 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

7d ago First seen · 95 lines · 878 tokens per session scan A 4cd4452944e0

Subscribe to this mod's changes

data-product-hub CLAUDE.md is an instructions file published in the GitHub repository armalite/data-product-hub (9 stars, last pushed 10mo ago), licensed MIT. It adds 878 tokens to every session, about $0.0044 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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AGENTS.md instructions for openai/codex, covering rust/codex-rs, the codex-core crate, code review rules, crate api surface and model visible context.

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vscode buildNext.instructions.md

Working notes and architecture documentation for the new esbuild-based build system in build/next. Use when making changes to the new build pipeline (transpile/bundle commands, NLS plugin, source-map handling, resource copying, or self-hosting watch tasks).

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data-product-hub: Instructions file for Claude Code