Skill UI

This system acts as an orchestrator for a three-tier model team (Advisor, Workers). It is designed for complex, large-scale tasks that exceed the capability of a single model pass, requiring decomposition, parallelization, and mandatory verification loops. Use cases include researching multiple competitors, executing multi-stage plans, or managing iterative development cycles involving different specialized AI agents.

This guide details the rigorous data analysis and reporting standards required for top-tier academic journals, such as AJPS. It focuses intensely on reproducibility, ensuring that all results—from primary findings to exploratory models—are generated by a single, seeded master script. Key requirements include reporting uncertainty intervals, proving robustness, adhering to pre-registration discipline, and accommodating mandatory third-party code verification. Use this when conducting complex empirical research in social sciences or political science, needing to withstand intensive methodolo...

A comprehensive guide for authors submitting revisions to top-tier academic journals, such as AJPS. It details the strategy, structure, and specific constraints required for the response memorandum following a Revise & Resubmit (R&R) decision. The guide emphasizes addressing every reviewer comment, reconciling conflicting demands, maintaining double-blind anonymity, and ensuring all new analyses are fully reproducible for third-party verification.

Provides a detailed guide on the mandatory pre-publication replication and verification process for American Journal of Political Science (AJPS). It instructs authors on building a robust, reproducible package—including code, data, and documentation—that an independent third party can re-run to confirm all numerical findings match the main text before publication.

This guide details the multi-stage, rigorous review process for the American Journal of Political Science (AJPS). It covers the mechanics of double-blind screening, editorial fit assessment, expert peer review, and the unique, critical post-acceptance third-party verification step. It is designed to help authors anticipate and mitigate potential rejection issues by structuring and refining their manuscripts to meet top-tier academic standards.

This guide outlines the stringent standards for creating tables and figures (exhibits) for the American Journal of Political Science (AJPS). It emphasizes that exhibits must be self-contained, highly accessible, and, critically, regenerated exactly by deposited code for third-party verification. It provides best practices for structuring research results, managing word counts by differentiating between core arguments in the main text and supporting details in the Supporting Information (SI).

A comprehensive guide to adhering to ASA/ASR data-sharing and transparency standards for social science research. It helps users prepare thorough documentation, define data provenance (codebooks, scripts), manage confidentiality constraints (IRB, proprietary data), and ethically prepare materials for academic publication. Use this when structuring a manuscript's data section or anticipating reviewer questions about data verification.

This guide outlines the comprehensive process for submitting and evaluating Computer Aided Verification (CAV) research artifacts. It details the criteria for earning three badges (Available, Functional, Reusable), the two-phase review structure (smoke-test and full review), and best practices for packaging scientific tools, including the use of containers, pinned benchmarks, resource limits, and proof witnesses. This ensures reproducibility and rigor in academic research.

This guide outlines best practices for designing and executing Computer Aided Verification (CAV) empirical evaluations. It emphasizes ensuring evidence matches claims by using standard, pinned benchmark sets, selecting fair baselines, respecting equal resource limits, and employing rigorous reporting methods like cactus and scatter plots to establish technical soundness and performance.

Provides detailed guidelines for authors on how to accurately position their research novelty within the complex landscape of formal verification techniques, tools, and academic literature. It emphasizes shifting from a simple citation catalog to a 'delta-first' contrast, clearly articulating technical differences, crediting specific tool and benchmark lineages, and understanding the scope of related venues (e.g., CAV, TACAS, FMCAD).

This guide provides detailed protocols for achieving high levels of reproducibility in Computer Aided Verification (CAV) research. It emphasizes the necessity of pinning benchmark sets, recording exact tool versions, documenting resource limits (time/memory), and providing checkable proof witnesses to ensure that evaluation claims can be independently validated by competitors or reviewers.

Provides strict guidelines for assembling supplementary material for Computer Aided Verification (CAV) papers. It details how to separate decision-critical content (main theorems, proof ideas) into the primary body, while moving voluminous details (full proofs, massive benchmarks) to the optional appendix or artifact, ensuring the paper remains judgeable from the main text alone.