Skill UI

Generates Python code and workflows for collaborative, content-based, or hybrid recommendation models, handling data prep, training, evaluation, and scalability so Claude can deliver tailored suggestions across domains.

This skill provides comprehensive, structured audit logging for OpenRouter API interactions. It automatically captures essential metadata like total cost, token usage, model details, latency, and status, while also performing PII redaction. The resulting audit trail is stored in an append-only SQLite database, making it ideal for building compliance-ready systems, detailed cost monitoring, and deep debugging of production environments.

Guides production ML engineering with patterns for deploying models, building MLOps pipelines, monitoring drift, integrating LLMs and RAG systems, and enforcing feature-store, retraining, and cost controls in live environments.

The LinkedIn Automation CLI Tool allows users to programmatically interact with LinkedIn content and functionality. It supports fetching detailed profiles, searching people and companies, and managing connections directly from scripts or AI agents. Ideal for building automated research, recruiting, or outreach workflows, providing structured JSON output for reliable integration into custom systems.

Run OpenMM-powered molecular dynamics simulations and MDAnalysis trajectory workflows to build protein or small-molecule systems, minimize energy, equilibrate conditions, and characterize RMSD/RMSF, contacts, and free-energy surfaces for structural biology or drug studies.

COBRApy is a powerful Python library for Constraint-Based Reconstruction and Analysis (COBRA). It enables deep dives into cellular metabolism, allowing users to build, simulate, and analyze complex metabolic models (e.g., genome-scale models). Core functionalities include Flux Balance Analysis (FBA), Flux Variability Analysis (FVA), performing gene/reaction knockouts, and optimizing growth media. It supports standard metabolic model formats like SBML, JSON, and YAML.

LiteParse is a fast, open-source tool for local, layout-aware text extraction from PDFs, Office files, and images. It provides structured JSON output with precise bounding boxes and supports OCR, making it ideal for building robust RAG systems, citation grounding, and multimodal agents. All processing is done locally without needing cloud APIs.