Machine Learning

Guide Claude through omicverse's single-cell clustering workflow, covering preprocessing, QC, multimethod clustering, topic modeling, cNMF, and cross-batch integration as demonstrated in t_cluster.ipynb and t_single_batch.ipynb.

Read and write Obsidian vault files, manage wiki-links, process markdown with YAML frontmatter. Use when working with vault file operations, creating notes, or managing links.

Validate AI/ML models and datasets for bias, fairness, and ethical concerns.Use when auditing AI systems for ethical compliance, fairness assessment, or bias detection.Trigger with phrases like "evaluate model fairness", "check for bias", or "validate AI ethics".

Automatically detect performance regressions in CI/CD pipelines by comparing metrics against baselines. Use when validating builds or analyzing performance trends. Trigger with phrases like "detect performance regression", "compare performance metrics", or "analyze performance degradation".

Track and manage regression test suites across releases.Use when performing specialized testing.Trigger with phrases like "track regressions", "manage regression suite", or "validate against baseline".

Execute clustering algorithms (K-means, DBSCAN, hierarchical) to identify data groups. Use when requesting "run clustering", "cluster analysis", or "group data points".

Split datasets into training, validation, and testing sets for ML model development. Use when requesting "split dataset", "train-test split", or "data partitioning".

Build and evaluate classification models for supervised learning tasks with labeled data. Use when requesting "build a classifier", "create classification model", or "train classifier".

Create, select, and transform features to improve machine learning model performance. Handles feature scaling, encoding, and importance analysis. Use when asked to "engineer features" or "select features".

Validate application performance against defined budgets to identify regressions early. Use when checking page load times, bundle sizes, or API response times against thresholds. Trigger with phrases like "validate performance budget", "check performance metrics", or "detect performance regression".

Train machine learning models with automated workflows. Analyzes datasets, selects model types (classification, regression), configures parameters, trains with cross-validation, and saves model artifacts. Use when asked to "train model" or "evalua...

Execute proactive YAML intelligence: automatically activates when working with YAML files. Use when appropriate context detected. Trigger with relevant phrases based on skill purpose.

Build automated machine learning pipelines with feature engineering, model selection, and hyperparameter tuning.Use when automating ML workflows from data preparation through model deployment.Trigger with phrases like "build automl pipeline", "automate ml workflow", or "create automated training pipeline".

Setup machine learning experiment tracking using MLflow or Weights & Biases. Configures environment and provides code for logging parameters, metrics, and artifacts. Use when asked to "setup experiment tracking" or "initialize MLflow".

Convert documents and files to Markdown using markitdown. Use when converting PDF, Word (.docx), PowerPoint (.pptx), Excel (.xlsx, .xls), HTML, CSV, JSON, XML, images (with EXIF/OCR), audio (with transcription), ZIP archives, YouTube URLs, or EPubs to Markdown format for LLM processing or text analysis.

Guide for creating properly structured YAML configuration files for MassGen. This skill should be used when agents need to create new configs for examples, case studies, testing, or demonstrating features.

Guidance for Worktrunk, a CLI tool for managing git worktrees. Covers configuration (user config at ~/.config/worktrunk/config.toml and project hooks at .config/wt.toml), usage, and troubleshooting. Use for "setting up LLM", "configuring hooks", "automating tasks", or general worktrunk questions.

AI가 만든 모든 코드 변경사항을 reviews 폴더에 기록하고 간단한 HTML 뷰어로 웹 브라우저에서 실시간 확인할 수 있습니다. 매 수정마다 문서가 생성되고 Python 서버로 즉시 확인 가능합니다.

Validate OpenAPI, Swagger, and GraphQL schemas match backend implementation. Detect breaking changes, generate TypeScript clients, and ensure API documentation stays synchronized. Use when working with API spec files (.yaml, .json, .graphql), reviewing API changes, generating frontend types, or validating endpoint implementations.

Track ML experiments, manage model registry with versioning, deploy models to production, and reproduce experiments with MLflow - framework-agnostic ML lifecycle platform