Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add dralkh/iktinah --skill shapgit clone --depth 1 https://github.com/dralkh/iktinahWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/dralkh/iktinah/shap)<a href="https://agentmods.dev/skills/dralkh/iktinah/shap"><img src="https://agentmods.dev/badge/skills/dralkh/iktinah/shap.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00109 | $0.04313 |
| Opus 5 | $0.00055 | $0.02157 |
| Sonnet 5 | $0.00022 | $0.00863 |
| Haiku 4.5 | $0.00011 | $0.00431 |
Grade A, and why
shap scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
98% identical to shap — 4 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 564 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SHAP (SHapley Additive exPlanations)
Overview
SHAP is a unified approach to explain machine learning model outputs using Shapley values from cooperative game theory. This skill provides comprehensive guidance for:
- Computing SHAP values for any model type
- Creating visualizations to understand feature importance
- Debugging and validating model behavior
- Analyzing fairness and bias
- Implementing explainable AI in production
SHAP works with all model types: tree-based models (XGBoost, LightGBM, CatBoost, Random Forest), deep learning models (TensorFlow, PyTorch, Keras), linear models, and black-box models.
When to Use This Skill
Trigger this skill when users ask about:
- "Explain which features are most important in my model"
- "Generate SHAP plots" (waterfall, beeswarm, bar, scatter, force, heatmap, etc.)
- "Why did my model make this prediction?"
- "Calculate SHAP values for my model"
- "Visualize feature importance using SHAP"
- "Debug my model's behavior" or "validate my model"
- "Check my model for bias" or "analyze fairness"
- "Compare feature importance across models"
- "Implement explainable AI" or "add explanations to my model"
- "Understand feature interactions"
- "Create model interpretation dashboard"
Quick Start Guide
Step 1: Select the Right Explainer
Decision Tree:
-
Tree-based model? (XGBoost, LightGBM, CatBoost, Random Forest, Gradient Boosting)
- Use
shap.TreeExplainer(fast, exact)
- Use
-
Deep neural network? (TensorFlow, PyTorch, Keras, CNNs, RNNs, Transformers)
- Use
shap.DeepExplainerorshap.GradientExplainer
- Use
-
Linear model? (Linear/Logistic Regression, GLMs)
- Use
shap.LinearExplainer(extremely fast)
- Use
-
Any other model? (SVMs, custom functions, black-box models)
- Use
shap.KernelExplainer(model-agnostic but slower)
- Use
-
Unsure?
- Use
shap.Explainer(automatically selects best algorithm)
- Use
See references/explainers.md for detailed information on all explainer types.
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 4d ago First seen · 564 lines · 109 tokens per session scan A 334558777c65
shap is a skill published in the GitHub repository dralkh/iktinah (77 stars, last pushed 2mo ago), licensed MIT. It adds 109 tokens to every session and 4,313 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 98% identical to shap, differing in 4 lines, and is treated as a copy.
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vector-and-embedding-weaknesses
Hunt vector / embedding weaknesses (OWASP LLM08:2025) — adversarial inputs against the RAG / similarity layer that cause cross-tenant leak, embedding-inversion privacy loss, semantic confusion, and retriever-driven prompt injection.
shap
Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias or fairness, comparing…
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