A toolkit that streamlines and automates the generation of model cards

A vendor-neutral, pattern-first AI strategy playbook by Textstone Labs. Practical frameworks, governance models, evaluation tools, and ready-to-use templates to go from business problem → deployment → adoption.

A single-file Python CLI that pre-registers AI/ML accuracy claims with SHA-256. Lock the threshold before the data, or it didn't happen.

LinkML rendering of model cards

Small program to scrape model repository data from the Hugging Face Hub

GitHub-native ML System Cards: evidence-backed, PR-first documentation for ML systems

Data Cards for Time Series

Model validation for GitHub pull requests — regression detection, data quality checks, and model cards in one Action. No server required.

How to document an AI/Machine Learning Model

Evaluation infrastructure for AI systems beyond direct human supervision

License Compliance Checker — Multi-ecosystem license + AI model scanner for EU AI Act Article 53 GPAI compliance. SBOM, SARIF, training-data risk. Apache 2.0.

AI governance for healthcare ML. Bias detection, fairness metrics, FDA-ready model cards, responsible AI.

Foundation Model for Medicine — fairness/bias evaluation CLI for medical AI (research)

AIWG training-complete framework — corpus-to-dataset pipeline with SKILL.md agentic surface and optional Python runtime backend. Marketplace plugin for AIWG.

🤖 Production-ready Python registry for OpenAI models with parameter validation, pricing data, and CLI tools. Supports GPT-5, Azure OpenAI, automated updates, and enterprise deployment.

Model-card templates and release-readiness standards for future Foundation AI releases.

Local-first AI system governance tool for creating structured, inspectable governance passports covering model, data, risk, and lifecycle.

Dataset of Model Cards for Language Models.

🤖 Lead.AI Labs — Hugging Face AI Portfolio | Model Cards, Datasets, Demo Spaces for Fraud Detection, Customer Intelligence & NLP

Reference implementation of governance tooling for healthcare AI programs: risk-classified inventory, model cards, NIST AI RMF risk assessments, fairness reports, and LLM output audits.