Turn any codebase, with its docs, SQL schemas, configs, and PDFs, into a queryable knowledge graph. A /graphify skill for Claude Code, Cursor, Codex, and Gemini CLI: local deterministic AST parsing, every edge explained, no vector store.
match command-line arguments to their help text
Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
Algorithms for explaining machine learning models
Interpretability and explainability of data and machine learning models
- explainable-ai
- explainable-ml
- trusted-ai
- trusted-ml
moDel Agnostic Language for Exploration and eXplanation
[CONTRIBUTORS WELCOME] Generalized Additive Models in Python
Python package for stacking (machine learning technique)
- stacking
- stacked-generalization
- explain-stacking
- stacking-tutorial
Engine for AI/ML/Data tracking, visualization, explainability, drift detection, and dashboards for Polyaxon.
A git hook that won't let you push code you can't explain. Quizzes you on your own diff. Works with Claude Code, Codex, Cursor.
Build AI text detectors whose decisions are mechanistically grounded and human-auditable (EMNLP 2026)
- ai-text-detection
- explainable-ai
- interpretability
- mechanistic-interpretability
The official implementation of "The Shapley Value of Classifiers in Ensemble Games" (CIKM 2021).
- shapley
- shapley-value
- owen
- voting-classifier
Do stock correlations predict the market? 66 pre-registered tests, the data, and a triple pendulum that explains why not. If you have an idea you want me to try, submit it as a PR and I will test it and get back to you.
Simple Reviewer: a Codex skill for AI-assisted coding and Vibe Coding. Explain code, visualize workflows, and verify goals with evidence. | 简单的审阅者:协助 AI 编程与 Vibe Coding,解释代码、可视化流程,以证据复核目标。
Your AI wrote the code. You still have to explain it before it ships.
A Python type explainer!
Contrastive Explanation (Foil Trees), developed at TNO/Utrecht University
Code for the experiment proposed in Section 4.2 of the paper "The Bouncer Problem: Challenges to Remote Explainability".