SHAP model explanations — Shapley values, plots and feature importance

Prerequisites: Python with shap installed and a trained model to explain — the skill is an explainability guide the agent follows, not software Install "SHAP model explanations — Shapley values, plots and feature importance" for me. It gives my agent @davila7's SHAP walkthrough: TreeExplainer and KernelExplainer usage, the full plot vocabulary (summary/beeswarm, dependence, force, waterfall, decision), global vs local interpretability, and common pitfalls. NOTE: a SHAP fiche already exists in the catalog (k-dense-ai-scientific-agent-skills-shap) — check it before installing a second. MIT-licensed. Repository: https://github.com/davila7/claude-code-templates/blob/main/cli-tool/components/skills/scientific/shap/SKILL.md 1. Fetch the SKILL.md file (and any helper files) from the repository path into a temporary folder and summarize what it does in one or two sentences. 2. Safety check: review the SKILL.md and scripts for anything suspicious (unexpected network calls, shell commands, credential harvesting). This repo should contain zero secrets in code. Verify that holds here; STOP on any red flag and tell me. 3. Install it as a skill: copy SKILL.md and its helper files into the agent's skills directory, in a folder named "shap". 4. Verify with no network calls: frontmatter valid, files in place. 5. Report what was installed, where, and what I still need to do myself (e.g. pip install shap and point the agent at the trained model to explain; nothing else — it's an explainability guide). GitHub is optional: if I have a GitHub account or the gh CLI, you may use it; otherwise public access is fine. Never require it unless it's in the prerequisites above. Rules: don't touch anything outside the temp folder and the install target. If anything looks off, stop and ask me.