[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6CX26MNFYGYSA4WS496RSKB":3,"skill-same:01M6CX26MNFYGYSA4WS496RSKB":52},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":17,"publishedAt":24,"linkOnly":25,"markdownContent":26,"markdownBody":27,"frontMatter":28,"refName":32,"commitSha":33,"skillDirectory":5,"forksCount":34,"repositoryHtmlUrl":35,"repositoryHomepageUrl":6,"ownerHtmlUrl":36,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":37,"install":38,"resources":51},"01M6CX26MNFYGYSA4WS496RSKB","explainable-ai",null,"Explainable AI (XAI) techniques","ffsshhttiikk\u002Fopencode-agents-skills","ffsshhttiikk","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F226346827?v=4","01M6CWD9RR3Y7VRQX3MXYC8FYH","01M6CWD9WVPYAZW0J82G1VKGHY",3,"MIT","https:\u002F\u002Fgithub.com\u002Fffsshhttiikk\u002Fopencode-agents-skills\u002Fblob\u002Fmain\u002Fexplainable-ai\u002FSKILL.md",[],[18,19,20,21,22,23],"explainable ai","machine-learning","learning","xai","opencode","intelligence","2026-11-10 10:12:30",false,"---\nname: explainable-ai\ndescription: Explainable AI (XAI) techniques\nlicense: MIT\ncompatibility: opencode\nmetadata:\n  audience: machine-learning-engineers\n  category: artificial-intelligence\n---\n\n## What I do\n\n- Apply XAI techniques to models\n- Generate model-agnostic explanations\n- Create interpretable features\n- Build trust in AI systems\n- Handle fairness and bias\n\n## When to use me\n\nUse me when:\n- Regulatory compliance (GDPR, EU AI Act)\n- High-stakes decisions\n- Debugging model behavior\n- Building trust with users\n\n## Key Concepts\n\n### XAI Methods\n- **SHAP**: Shapley additive explanations\n- **LIME**: Local interpretable model-agnostic\n- **Counterfactuals**: What-if analysis\n- **Feature importance**: Permutation, tree-based\n\n### SHAP Framework\n```python\nimport shap\n\n# Kernel SHAP for any model\nexplainer = shap.KernelExplainer(model.predict, X_background)\nshap_values = explainer.shap_values(X_test)\n\n# Tree SHAP for tree-based\nexplainer = shap.TreeExplainer(model)\nshap_values = explainer.shap_values(X_test)\n\n# Visualization\nshap.force_plot(\n    explainer.expected_value, \n    shap_values[0,:], \n    X_test.iloc[0,:]\n)\n```\n\n### Counterfactual Explanations\n```python\nfrom dice_ml import Explainer\n\n# Create explainer\nexplainer = Explainer(model, X_train, outcome_class=\"target\")\n\n# Generate counterfactuals\nexplanation = explanation.generate_counterfactuals(\n    X_test[0:1], \n    total_CFs=3, \n    desired_class=\"opposite\"\n)\n```\n\n### Fairness\n- Demographic parity\n- Equalized odds\n- Individual fairness\n- Bias detection metrics\n","## What I do\n\n- Apply XAI techniques to models\n- Generate model-agnostic explanations\n- Create interpretable features\n- Build trust in AI systems\n- Handle fairness and bias\n\n## When to use me\n\nUse me when:\n- Regulatory compliance (GDPR, EU AI Act)\n- High-stakes decisions\n- Debugging model behavior\n- Building trust with users\n\n## Key Concepts\n\n### XAI Methods\n- **SHAP**: Shapley additive explanations\n- **LIME**: Local interpretable model-agnostic\n- **Counterfactuals**: What-if analysis\n- **Feature importance**: Permutation, tree-based\n\n### SHAP Framework\n```python\nimport shap\n\n# Kernel SHAP for any model\nexplainer = shap.KernelExplainer(model.predict, X_background)\nshap_values = explainer.shap_values(X_test)\n\n# Tree SHAP for tree-based\nexplainer = shap.TreeExplainer(model)\nshap_values = explainer.shap_values(X_test)\n\n# Visualization\nshap.force_plot(\n    explainer.expected_value, \n    shap_values[0,:], \n    X_test.iloc[0,:]\n)\n```\n\n### Counterfactual Explanations\n```python\nfrom dice_ml import Explainer\n\n# Create explainer\nexplainer = Explainer(model, X_train, outcome_class=\"target\")\n\n# Generate counterfactuals\nexplanation = explanation.generate_counterfactuals(\n    X_test[0:1], \n    total_CFs=3, \n    desired_class=\"opposite\"\n)\n```\n\n### Fairness\n- Demographic parity\n- Equalized odds\n- Individual fairness\n- Bias detection metrics\n",{"name":5,"license":14,"metadata":29,"description":7,"compatibility":22},{"audience":30,"category":31},"machine-learning-engineers","artificial-intelligence","main","5f7a3e453c98261f93d19a6a7de44ad8f7b72d41",2,"https:\u002F\u002Fgithub.com\u002Fffsshhttiikk\u002Fopencode-agents-skills","https:\u002F\u002Fgithub.com\u002Fffsshhttiikk","2026-03-01 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