shap-model-explainability SKILL.md skillA
shap-model-explainability from jaechang-hits/sciagent-skills is an agent skill: a SKILL.md folder an AI agent loads when a task calls for it.
mdr add jaechang-hits/sciagent-skills/shap-model-explainability@git:20260528.a0c28a2Pins git:20260528.a0c28a2 in mdr.lock. Pin the fingerprint instead.
- Audit grade
- A, 17 of 17 checks passed
- Kind
- SKILL.md skill
- Repository
- jaechang-hits/sciagent-skills
- Path
skills/scientific-computing/shap-model-explainability/SKILL.md- Versions recorded
- 1
- Latest version
- git:20260528.a0c28a2, committed May 28, 2026
- Size
- 15,040 bytes
- Repository stars
- 381
- License
- a license file GitHub does not identify
Model interpretability via SHAP (Shapley values from game theory). Covers explainer choice (Tree, Deep, Linear, Kernel, Gradient, Permutation), feature attribution, and plots (waterfall, beeswarm, bar, scatter, force, heatmap). Use to explain ML predictions, rank features, debug models, audit fairness, or compare models. Works with tree, deep, linear, and black-box models.
Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.
What the file says
# SHAP Model Explainability ## Overview SHAP (SHapley Additive exPlanations) is a unified framework for explaining machine learning model predictions using Shapley values from cooperative game theory. It quantifies each feature's contribution to individual predictions and provides both local (per-instance) and global (dataset-level) explanations with theoretical guarantees of consistency and additivity. ## When to Use - Explaining which features drive a model's predictions (global importance) - Understanding why a model made a specific prediction (local explanation) - Debugging model behavior and identifying data leakage - Analyzing model fairness across demographic groups - Comparing feature importance across multiple models - Generating interpretable model explanations for stakeholders - For tree-based model interpretation, prefer SHAP over permutation importance or Gini importance (more accurate, instance-level) - For deep learning interpretation on images, consider GradCAM; use SHAP for tabular/structured data ## Prerequisites ```bash pip install shap matplotlib # Optional: xgboost lightgbm tensorflow torch (depending on model) ``` ## Quick Start ```python import shap …
Read the whole file at its exact version.
How to install
mdr add jaechang-hits/sciagent-skills/shap-model-explainability@sha256:d6308cc6bec03a03The line at the top of this page pins the label git:20260528.a0c28a2, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.
[](https://markdownregistry.com/a/art_ld5q75oexphu5gc6)
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Versions
The registry has recorded 1 version of this file, committed on May 28, 2026.
Audit of the latest version
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Source
jaechang-hits/sciagent-skills · 381 stars · license a license file GitHub does not identify · pushed 2026-10-16 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_ld5q75oexphu5gc6 GET https://markdownregistry.com/api/v1/resolve?ref=jaechang-hits/sciagent-skills/shap-model-explainability GET https://markdownregistry.com/api/v1/blob/d6308cc6bec03a03775cc0bce45f736975876202ed82de198fd565c77f9581b4
Your AI assistant does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.
Guides: the SKILL.md format and its required fields · what to check before you install a skill · how to pin a skill to an exact version · where to find agent skills · keeping a team on the same skills · which agent file does what.