Mila Skill

Access and analyze comprehensive drug information from the DrugBank database including drug properties, interactions, targets, pathways, chemical structures, and pharmacology data. This skill should be used when working with pharmaceutical data, drug discovery research, pharmacology studies, drug-drug interaction analysis, target identification, chemical similarity searches, ADMET predictions, or any task requiring detailed drug and drug target information from DrugBank.

Guide for implementing combinational/sequential logic circuits using gate-level descriptions in text-based simulators. This skill applies when building circuits for mathematical functions like integer square root, Fibonacci sequences, or similar computations that require both combinational logic (arithmetic operations) and sequential logic (feedback loops, state machines). Use this skill when the task involves generating gate netlists, implementing multi-bit arithmetic circuits, or debugging event-driven circuit simulators.

Use this skill for setting up vector similarity search with pgvector for AI/ML embeddings, RAG applications, or semantic search. **Trigger when user asks to:** - Store or search vector embeddings in PostgreSQL - Set up semantic search, similarity search, or nearest neighbor search - Create HNSW or IVFFlat indexes for vectors - Implement RAG (Retrieval Augmented Generation) with PostgreSQL - Optimize pgvector performance, recall, or memory usage - Use binary quantization for large vector datasets **Keywords:** pgvector, embeddings, semantic search, vector similarity, HNSW, IVFFlat, halfvec, cosine distance, nearest neighbor, RAG, LLM, AI search Covers: halfvec storage, HNSW index configuration (m, ef_construction, ef_search), quantization strategies, filtered search, bulk loading, and performance tuning.

This skill empowers Claude to perform feature engineering tasks for machine learning. It creates, selects, and transforms features to improve model performance. Use this skill when the user requests feature creation, feature selection, feature transformation, or any request that involves improving the features used in a machine learning model. Trigger terms include "feature engineering", "feature selection", "feature transformation", "create features", "select features", "transform features", "improve model performance", and similar phrases related to feature manipulation.

This skill sets up log aggregation solutions using ELK (Elasticsearch, Logstash, Kibana), Loki, or Splunk. It generates production-ready configurations and setup code based on specific requirements and infrastructure. Use this skill when the user requests to set up logging infrastructure, configure log aggregation, deploy ELK stack, deploy Loki, deploy Splunk, or needs help with observability. It is triggered by terms like "log aggregation," "ELK setup," "Loki configuration," "Splunk deployment," or similar requests for centralized logging solutions.

This skill should be used when setting up, auditing, or enforcing internationalization/localization in UI codebases (React/TS, i18next or similar, JSON locales), including installing/configuring the i18n framework, replacing hard-coded strings, ensuring en-US/zh-CN coverage, mapping error codes to localized messages, and validating key parity, pluralization, and formatting.