retrieval▌

Use when planning, debugging, tuning, evaluating, exporting, or deploying public Nemotron `embed`/`rerank` retrieval recipes.

Create evidence-based learning plans that maximize long-term retention through spaced repetition, retrieval practice, and interleaving.

Retrieve protein structures with disambiguation, quality assessment, and comprehensive metadata.

Retrieve comprehensive chemical compound data with proper disambiguation and cross-database validation.

This skill provides icon search and SVG string retrieval capabilities. It helps users find appropriate icons for various use cases including infographics, web development, design, and more.

Progressive context refinement loop for multi-agent workflows solving the subagent context problem. \n \n Four-phase cycle (dispatch, evaluate, refine, loop) that iteratively narrows retrieval to high-relevance files, capping at 3 iterations to balance token usage and context quality \n Scores retrieved files on a 0–1 relevance scale and explicitly identifies missing context gaps to drive the next refinement cycle \n Learns codebase terminology and patterns during the first cycle, improving subs

Comprehensive patterns for building production RAG systems. Each category has individual rule files in rules/ loaded on-demand.

Retrieve gene expression experiments and multi-omics datasets with disambiguation and quality assessment.

Retrieve DNA, RNA, and protein sequences from NCBI and ENA with automatic gene disambiguation and cross-database handling. \n \n Searches NCBI Nucleotide by organism, gene name, strain, and sequence type; automatically disambiguates genes across species and resolves accession prefixes to the correct database \n Handles RefSeq (NC_, NM_, NP_) and GenBank accessions with intelligent fallback between NCBI and ENA; never attempts ENA queries on RefSeq-only accessions \n Returns detailed sequence pro