Skill UI

This reference architecture provides a comprehensive blueprint for building a robust revenue intelligence data platform integrated with Clari. It covers the entire data lifecycle, including API data extraction (ETL pipeline), data warehousing design (Snowflake/BQ), change detection logic, and advanced analytics/alerting layers. Use this guide when designing a scalable forecast tracking system, planning complex data integrations, or establishing best practices for enterprise revenue analytics.

This guide provides a comprehensive reference architecture for building production-ready, enterprise-grade applications on Palantir Foundry. It covers the entire data lifecycle, detailing best practices for the standard data pipeline (Raw > Clean > Model), Ontology design, external API integration patterns, and robust multi-layered security models. Use it when designing, planning, or optimizing complex data infrastructure within Foundry.

This skill allows for the generation and execution of complex, optimized SQL queries to derive deep business insights. It supports sophisticated data workflows, including multi-table joins, multi-dimensional analysis (e.g., trend analysis, attribution modeling, comparative analysis), and subsequent recommendations for appropriate data visualizations (e.g., line charts, waterfall charts). It goes beyond simple SELECT statements by planning the query structure, handling schema relationships, and ensuring adherence to best practices.

This guide provides a comprehensive methodology for load testing and capacity planning within Snowflake data warehouses. It covers optimizing query performance by adjusting warehouse sizes (Scale UP), simulating high concurrency using multi-threading (Scale OUT), and configuring advanced scaling policies (Standard, Economy, Maximized). Users can benchmark critical queries using SQL and simulate real-world user traffic using Python, ensuring robust and cost-efficient production environments.

This comprehensive checklist guides the deployment of Snowflake data pipelines and integrations into a production environment. It covers critical areas such as authentication best practices, warehouse configuration, data pipeline readiness, access control, monitoring setup, and disaster recovery planning. Use it when preparing for a go-live or validating the stability of your production Snowflake setup.

This advanced skill acts as a primary research orchestrator. It deterministically analyzes user queries, routing them to specialized modules (e.g., patent landscape, academic literature review, funding intelligence) or executing a comprehensive fallback search workflow. It is designed for complex, multi-faceted research requiring high rigor, structured planning, and verifiable citations.

This skill provides comprehensive tools for planning studies by calculating the required sample size, minimum detectable effect (MDE), and statistical power. It supports both closed-form statistical tests (t-tests, ANOVA, chi-square) and advanced simulation-based designs (mixed models, logistic/Poisson regression). Use this when planning a study, submitting an IRB protocol, or justifying sample size for a grant.

This skill guides the design of statistically rigorous A/B tests for optimizing product features, UI/UX changes, onboarding flows, and pricing models. It generates a complete test plan, including formulating hypotheses, calculating required sample sizes, estimating test duration, defining primary and guardrail metrics, and providing a detailed guide on interpreting results (ship, iterate, or reject).

This tool provides forward-looking cost projection by calculating the average daily burn rate from a specified measurement window. It linearly extrapolates future spending across multiple time horizons (7d, 30d, 90d, 365d) and alerts users to the projected date of budget exhaustion. It is essential for proactive financial planning, SRE cost monitoring, and setting CI/CD build cost gates.

A comprehensive guide for strengthening the methodological rigor of academic manuscripts, particularly for high-stakes journals. It covers advanced techniques for causal identification (e.g., Difference-in-Differences, Instrumental Variables, Regression Discontinuity), pre-analysis planning, linking theory to empirical tests, and defending claims against skeptical reviewers. This skill focuses on methodological architecture, not code.

This guide provides detailed standards for planning, auditing, and executing data analysis for sociological manuscripts, specifically tailored to the standards of the American Journal of Sociology (AJS). It stresses that analysis must credibly support a theoretical claim, rather than simply showcasing technical proficiency. Key areas covered include quantitative robustness checks, comparative-historical logic, ethnographic procedure, and the crucial principle of triangulation across diverse data sources.

A comprehensive guide for planning and auditing highly rigorous biological experimental protocols, particularly for cancer mechanism studies. It emphasizes orthogonal validation across independent systems (in vitro, in vivo, human), detailing requirements for controls, replicates, sample size justification, randomization, and blinding to ensure scientific rigor.