Loading...
Loading...
Found 19 Skills
Advanced sub-skill for scikit-learn focused on model interpretability, feature importance, and diagnostic tools. Covers global and local explanations using built-in inspection tools and SHAP/LIME integrations.
Data validation and pipeline testing utilities for ML training projects. Validates datasets, model checkpoints, training pipelines, and dependencies. Use when validating training data, checking model outputs, testing ML pipelines, verifying dependencies, debugging training failures, or ensuring data quality before training.
Runs the Metabase semantic checker against a tree of Representation Format YAML files to verify that all references resolve — cross-entity references (collection_id, dashboard_id, parent_id, parameter source cards, snippet references, transform tags, etc.) and references to columns inside MBQL and native queries. Use when the user asks to "semantic check", "check references", "validate queries against the schema", or diagnose a broken reference. Requires database metadata on disk (by default `.metabase/metadata.json`).
Document solved problems for knowledge persistence
驗證規格檔案的完整性與一致性，確保所有必要的規格元素都已定義且符合標準格式。
Comprehensive toolkit for validating, linting, and testing Kubernetes YAML resources. Use this skill when validating Kubernetes manifests, debugging YAML syntax errors, performing dry-run tests on clusters, or working with Custom Resource Definitions (CRDs) that require documentation lookup.
Verifica a Stack do N8N. Além disso analisa parâmetros, rotas Traefik, volumes, recursos e conformidade do stack N8N de Acordo com as Recomendações da Promovaweb.