Skill UI

This skill provides robust functionality to resolve free-text scientific labels into standard ontology term IDs (CURIEs) and validate existing identifiers against authoritative databases. It is essential for metadata curation, ensuring that submitted biological data (e.g., tissue types, diseases, assays) uses current, correct, and non-obsolete identifiers, thereby maintaining high data quality for large-scale scientific datasets.

This strategy guides researchers in anticipating the specific, rigorous methodological objections raised by top-tier academic referees, especially in applied econometrics. It provides a systematic map connecting econometric designs (like DID, IV, RD) to common threats (e.g., measurement error, bias, specification issues). Users can pre-empt these criticisms by structuring their manuscript to include necessary defenses, robustness checks, and scope limitations before formal submission.

This skill implements an advanced reference pattern for integrating with Monitoring Control Platform (MCP) servers. It ensures accurate data retrieval by first reading a domain-specific 'gotchas' reference file, mitigating query failures caused by complex server behaviors, pagination limits, or subtle API quirks. It analyzes issue data, groups errors by root cause, and generates a structured, actionable markdown report for improved system health visibility.