Skill UI

Provides detailed evaluation of machine learning models using plugin metrics to assess accuracy, precision, recall, F1, and other key indicators, helping users compare models and validate performance before deployment.

Provide REST API access to ClinPGx, the successor to PharmGKB, for querying gene-drug interactions, CPIC/DPWG/FDA guidelines, allele functions, and evidence that supports precision dosing and pharmacogenomics-driven clinical decisions.

Query the PrimeKG precision medicine knowledge graph to explore genes, drugs, diseases, phenotypes, and their relationships, supporting drug discovery, repurposing, and phenotype analysis through programmatic scripts.

This skill guides security teams on systematically tuning SIEM detection rules in platforms like Splunk and Elastic. It focuses on reducing alert fatigue and false positives by analyzing historical alert volumes, establishing environmental baselines, creating context-aware whitelists, and measuring detection efficacy using metrics like precision and recall. This is crucial for maturing a Security Operations Center (SOC).

Collection of battle-tested PyTorch training recipes covering architecture selection, optimizers, LR scheduling, precision tips, and debugging to train or fine-tune LLMs, vision, diffusion, and biomedical models with high GPU throughput.

This skill enhances research query formulation by translating vague, colloquial, or generalized descriptions into precise, standard academic terminology. When initial search terms are broad (e.g., "conjugal happiness"), this technique suggests multiple plausible academic synonyms (e.g., "marital satisfaction," "relationship quality"). By improving the precision of the search query and incorporating structured constraints (like keywords, years, or sample sizes), it significantly boosts the relevance and effectiveness of academic paper retrieval.

This technique guides users to use precise chemical compound names, specific concentration values (e.g., "1.5 mg/L"), and taxonomic synonym counts as exact-match search anchors when querying biological literature. By leveraging quotation marks and combining multiple specific terms, it uniquely identifies research papers or species details, greatly improving search precision in biochemistry and botany.

This technique guides users on how to construct highly specific search queries for academic papers. Instead of relying solely on broad topic keywords, users should prioritize combining concrete numerical identifiers—such as exact sample sizes, population percentages, concentration levels, or specific year ranges—with methodological terms and discipline keywords. Using these numerical anchors significantly increases the uniqueness and precision of the search results, helping to pinpoint the exact study required.

Query the PrimeKG precision medicine knowledge graph to search genes, drugs, diseases, and phenotypes, retrieve associated neighbors, analyze disease context, and explore drug repurposing paths.

A comprehensive guide to diagnosing and enhancing search relevance within Qdrant. This skill addresses reported issues such as low precision, low recall, or irrelevant matches. It guides users through determining if the problem lies in the embedding model, Qdrant configuration, or the query strategy. Techniques covered include optimizing chunk splitting, tuning HNSW parameters, implementing hybrid search, and utilizing reranking for superior result quality.

This guide covers advanced retrieval strategies that significantly boost search quality beyond basic vector similarity search. Techniques include Hybrid Search (combining dense and sparse indexing), Reranking (improving result order), Relevance Feedback (guiding search with iterative user scores), and Maximal Marginal Relevance (MMR) for diversity. Use these methods when standard search results lack relevance, miss critical keywords, or exhibit poor precision.

This utility evaluates complex arithmetic expressions using arbitrary-precision decimal mathematics. It is specifically designed for scientific, financial, or engineering calculations where standard floating-point arithmetic causes unacceptable rounding errors (e.g., 0.1 + 0.2). Supports addition, subtraction, multiplication, division, exponents, and parentheses.