Skill UI

A comprehensive guide for authors submitting papers to web conferences. It details how to structure empirical claims, ensuring evidence matches the claim's scope (scale, freshness, generalizability). The guide covers avoiding methodological flaws like temporal or popularity leakage, selecting datasets with proper provenance, and performing rigorous ablations and statistical analysis for maximum academic rigor.

A comprehensive guide for ensuring the scientific reproducibility of research papers that rely on dynamic web data, such as web crawls, platform APIs, or live system traffic. It defines three reproducibility regimes (Frozen, Decaying, Unreplayable) and provides detailed instructions on documenting data decay, temporal splits, environment configurations, and computational seeds to maintain the rigor of published findings.

This guide provides a comprehensive framework for designing rigorous and defensible evaluation sections for machine learning and information retrieval papers, particularly for top-tier conferences like WSDM. It covers the four critical evidence quadrants—Effectiveness, Validity, Efficiency, and Robustness—and details necessary protocols, such as temporal splits, proper baseline selection (including heuristics), ablation studies, and hardware-aware performance reporting. Use this when auditing or writing the experimental section of a research paper.

A comprehensive framework for ensuring reproducibility in Web Search and Data Mining (WSDM) research. It addresses unique challenges associated with behavioral data, covering data provenance, temporal splitting discipline, bias assumption reporting, statistical rigor (seeds, variance), and the 'honesty ladder' for non-reproducible results, helping researchers publish robust and verifiable work.

This skill constructs a comprehensive, chronologically sorted patient timeline from raw clinical entities extracted from medical notes. It specializes in temporal normalization, resolving both absolute and relative dates (e.g., 'two days after admission') against a defined anchor date. The resulting sorted sequence of diagnoses, medications, and procedures is ready for immediate visualization or seamless export to FHIR resources.

This skill enhances raw clinical entity recognition by assigning three critical axes: Negation, Temporality, and Uncertainty (ConText). It determines the clinical status of a mention (e.g., affirmed, negated, historical, or uncertain), ensuring that basic NER results are transformed into clinically faithful assertions before grounding to standards like FHIR. Essential for accurate problem list generation and differential diagnosis support.

This skill applies consistent date shifting to clinical text, ensuring that absolute dates are obscured while critically preserving the temporal intervals between medical events. It is essential for longitudinal research or survival analysis where time-to-event data must be preserved under HIPAA Safe Harbor rules. It maintains the integrity of time differences (e.g., length of stay, visit gaps) by applying a stable, per-patient offset.

This framework provides a rigorous pre-execution review mechanism for proposed actions by AI agents or systems. It functions as a critical safety boundary, auditing the entire decision path—from initial request to proposed tool execution—without authorizing or executing anything. It verifies action conformity, evidence integrity, temporal validity, and reviewer independence to ensure system compliance and security.

JurisRank is a computational tool that analyzes citation networks of Argentine Supreme Court (CSJN) jurisprudence using a peer-reviewed PageRank algorithm with temporal decay. It measures the jurisprudential authority of legal precedents, allowing users to rank competing cases by influence, trace the evolution of legal doctrine, and detect potential constitutional drift. Ideal for intensive legal research, due diligence, and academic analysis of legal trends in Argentina.

This sophisticated skill assists in-house legal teams with comprehensive legal risk assessment and classification. It uses a structured framework based on severity and likelihood, but critically, it enforces a rigorous three-step research methodology: adversarial searching for contradicting case law, systematic doctrinal web searches for context, and a temporal confidence check to ensure legal positions are current. It ensures that risk assessments are grounded in the most reliable and updated legal landscape.

Expert in event sourcing, CQRS, and event-driven architecture. Masters event store design, projection building, saga orchestration, and eventual consistency. Ideal for systems requiring audit trails, complex workflows, temporal queries, and event-driven microservices.

Expert guide for building durable distributed systems with the Temporal Go SDK, covering deterministic workflow rules, mTLS worker configuration, advanced patterns, and testing/observability strategies for production workloads.