Skill UI

Performs multi-baseline counterfactual cost analysis by comparing actual session spending against hypothetical costs generated by fixed, non-optimal models (e.g., always-haiku, always-sonnet). This tool quantifies the financial savings realized by the smart routing mechanism, answering the critical question: 'Is the routing earning its keep?' It is ideal for quarterly cost reviews, setting CI performance gates, and validating routing configurations.

This tool scans ingested OHLCV market data to automatically detect and classify various candlestick patterns, such as dojis, engulfing patterns, and head-shoulders formations. It not only identifies the pattern name but also assigns a type (reversal/continuation), direction, and a quantitative reliability score. It is an essential utility for technical analysis, helping users identify potential market turning points or continued trends.

This skill dispatches computationally intensive quantitative trading jobs (such as multi-year walk-forward analysis, large Monte-Carlo simulations, parameter sweeping, or deep model training) to a managed cloud agent runtime. It is designed for large-scale, production-grade backtesting that exceeds local machine capacity, ensuring robust execution and artifact management via Anthropic's cloud services.

A comprehensive guide outlining rigorous data analysis and reporting standards for geographic manuscripts. It covers spatial statistics, remote sensing accuracy, and qualitative interpretation, emphasizing spatial honesty, quantifying uncertainty, and ensuring results are robust across different scales and units.

A comprehensive guide for reporting rigorous educational and social science analyses, adhering to high standards of warrant and transparency (AERA). It covers advanced quantitative methods (HLM, IRT, quasi-experimental), qualitative coding, and mixed-methods integration, ensuring results are reproducible and evidence-based for publication.

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 structuring and defending the methodological design of social science manuscripts, particularly for high-impact journals like AJS. It covers diverse traditions—including quantitative, comparative-historical, ethnographic, and network analysis—ensuring the design rigorously supports the theoretical claim, rather than just presenting data. Use this when justifying case selection, identifying assumptions, or linking field observations to major theoretical arguments.

A comprehensive guide for conducting rigorous analysis for Accounting, Organizations, and Society (AOS) manuscripts. It covers both qualitative methods—such as coding, building evidence chains, and weighing counter-evidence—and quantitative techniques, including experimental, survey, and archival model estimation. The focus is on maintaining a fully reproducible audit trail and reconciling mixed-methods findings.

A comprehensive guide for authors facing a revise-and-resubmit (R&R) status in academic journals. It outlines the entire revision lifecycle, from building a structured response matrix and performing substantive re-analysis (re-coding, new experiments) to strategically drafting the point-by-point rebuttal letter. Focuses on maintaining academic rigor, reconciling conflicting reviews, and ensuring methodological consistency before final resubmission.

This guide outlines the rigorous execution and reporting standards required for quantitative research intended for top-tier academic journals, such as APSR. It covers best practices for ensuring reproducibility, assessing robustness, reporting uncertainty honestly (using credible intervals), and correctly applying advanced statistical inference (e.g., clustering, randomization inference). It is essential for researchers conducting empirical work in political science, economics, or social sciences.

This guide assists researchers in strengthening the methodological rigor of academic work, particularly in political science. It provides structured methods for defending research design, covering rigorous causal identification for quantitative work (e.g., DiD, IV, RDD) and systematic case selection/process tracing for qualitative analysis. Use this when facing reviewer skepticism or preparing a pre-analysis plan to ensure the evidence credibly connects to the theoretical argument.

A methodological guide for documenting the rigor of literature reviews, especially in sociology. It details how to build a comprehensive 'coverage account' (a near-PRISMA record of the search process) and how to ensure any quantitative synthesis (meta-analysis, bibliometrics) conducted within the review is fully reproducible by depositing data and code.