alterlab-shap

alterlab-shap is a Model training & evaluation skill for AI agents, published by AlterLab-IEU in the AlterLab-Academic-Skills repository on GitHub. Skillspace rates alterlab-shap C (77/100) and it passed the safety scan; the rating is based on static analysis only.

Model interpretability and explainability with SHAP (SHapley Additive exPlanations) — feature importance and plots (waterfall, beeswarm, bar, scatter, force, heatmap). Use when explaining ML model predictions, computing feature importance, debugging models, analyzing bias or fairness, comparing models, or implementing explainable AI across tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box model. Part of the AlterLab Academic Skills suite.

Grade
C
Quality
77 / 100
Safety
Passed
GitHub stars
43

Unproven: rated by static analysis only, not yet compared head-to-head.

Not launch-quality. It is graded C with a clean safety verdict, but our quality filters exclude it: it is a near-duplicate variant of another skill. So it is not in our launch-quality set, the skills our MCP server searches and recommends. How launch-quality is decided

This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.

Our review

Architecture
100 / 100
Maturity
Packaged: licensed, with a version or references
Security scan
Low-risk patterns
License
MIT
Adoption
Adopted by 1 other repositories, in 0 collections

What the scanner flagged

  • over-broad allowed-tools ('*')

Not checked yet

  • Category is a classifier prediction; no model has read this skill yet
  • Not judged on the rubric yet (not a contender in its category)
  • No blind task eval yet
  • Scope (portable / vendor / repo-internal) not assessed; only the top of each category is read for scope
  • Sibling files (scripts, references) not fetched; security scan covers SKILL.md only

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