Skill UI

Provides a detailed, multi-platform workflow to monitor long-running computational experiments. It checks the status of jobs across various environments (SSH, vast.ai, Modal), collects screen logs, fetches structured JSON results, and pulls detailed training metrics (loss curves, accuracy) from Weights & Biases (W&B). Essential for MLOps and automated result review.

Provides a structured workflow for systematically accessing and analyzing official intellectual property data from the USPTO. This skill covers patent search (PatentSearch API), trademark status checks (TSDR), ownership transfer tracking, and building reproducible IP landscape logs. It is designed for data collection and record verification, emphasizing the use of official APIs and rigorous logging practices. (Warning: Not for legal advice.)

This skill integrates with Monte Carlo's observability platform to provide a comprehensive health report for data assets. It checks critical metrics including data freshness, active alerts, monitoring coverage, asset importance, and the health of upstream dependencies. Activates when users inquire about the status, reliability, or quality of a data table.

Provides a structured workflow for systematically accessing and analyzing official intellectual property data from the USPTO. This skill covers patent search (PatentSearch API), trademark status checks (TSDR), ownership transfer tracking, and building reproducible IP landscape logs. It is designed for data collection and record verification, emphasizing the use of official APIs and rigorous logging practices. (Warning: Not for legal advice.)

This skill audits academic literature reviews (like Annual Reviews of Psychology) to ensure they maintain scholarly rigor, balance, and objectivity. It checks for uneven coverage across different labs or theories, ensures evidence is weighted by design strength and replication status (not mere vote-counting), and flags potential self-promotion or bias. Use this when scrutinizing a high-stakes scientific review for academic fairness.

This framework guides the critical appraisal of scientific literature syntheses. It helps users assess the credibility of individual studies—checking for power, replication status, and publication bias—to ensure the overall synthesis accurately reflects the best evidence. It emphasizes weighing conflicting results by construct rather than simple vote-counting, ensuring methodological balance and comprehensiveness in reporting.

This skill connects extracted drug entities with authoritative regulatory facts from the public OpenFDA API. It retrieves Structured Product Labeling (SPL) data, NDC package codes, approved indications, boxed warnings, and current recall/enforcement status. It is essential for enriching drug profiles, providing regulatory context, and supporting unexpectedness checks. Note: This skill provides information, not clinical decision support.