Skill UI

A comprehensive guide for auditing and planning the analysis of linguistic manuscripts. It emphasizes that the analysis must credibly support the theoretical claim, covering quantitative modeling (mixed-effects models in R), phonetic measurements, and corpus statistics. Use this skill to stress-test the analysis chain, ensuring rigor and transparency in all generalizations.

This guide provides comprehensive guidelines for authors preparing linguistic manuscripts, focusing on data documentation, reproducibility, and ethical sharing practices. It details how to properly share quantitative data, elicited fieldwork corpora, and phonetic measurements while adhering strictly to community consent, licensing terms, and best academic practices. It emphasizes that transparency must be coupled with ethical responsibility.

Systematically transforms high-level ideas or goals into a detailed, structured, and executable taskbook designed for AI agents. This process involves deep research, validation against existing codebases, adherence to strict formatting rules, and generating a single, copy-pastable block of instructions for immediate agent execution. It distinguishes between execution and exploration tasks, ensuring all steps are verifiable and anti-cheating measures are included.

Performs a comprehensive security audit of Model Context Protocol (MCP) implementations, including servers, clients, and tools. This skill reviews code against critical security baselines (MCP-01 to MCP-05), OWASP MCP Top 10, and common Remote Code Execution (RCE) vectors (e.g., command injection, deserialization, SSRF). The output provides a detailed report with file and line references, ensuring compliance and identifying vulnerabilities before release. Use this when auditing network-exposed services or client security posture.

This guide provides a rigorous framework for evaluating computer architecture designs. It covers critical steps including selecting the appropriate simulation instrument (from analytical models to silicon measurements), constructing robust baselines, performing ablation studies, and reporting comprehensive metrics like geomeans and full overhead accounting to ensure scientific validity.

A detailed guide on how to write a rigorous and comparative 'Related Work' section for top-tier microarchitecture conferences (e.g., MICRO). It teaches authors to structure the section as a 'mechanism taxonomy,' differentiating designs based on structural and cost differences rather than merely listing citations. Key areas covered include sweeping academic venues, integrating industry disclosures, and verifying all venue attributions using tools like dblp.

This skill guides users through transforming vague ideas into sharp, defensible, and rigorously structured academic arguments. It emphasizes explicitly stating a deniable thesis, laying out numbered premises, verifying logical validity, and ensuring the soundness of all supporting claims. It is essential for advanced academic writing in fields like philosophy.

This skill guides researchers in refining broad philosophical topics into sharply focused, significant, and defensible theses. It teaches the rigorous process of ensuring a proposed project meets high academic standards—specifically requiring a clear, debatable claim, relevance to live debates, and scope manageable within a defined length. Use this to pressure-test your ideas before committing to writing.

This skill refines complex philosophical prose, ensuring rigorous arguments are highly accessible and easy to follow for a broad academic readership. It focuses on maximizing clarity, achieving linguistic economy, and ensuring all technical concepts are accompanied by plain-language exposition. Ideal for final drafting passes, improving readability, and adhering to strict word limits.

This comprehensive guide assists authors in developing rigorous methodology sections for academic papers in Marketing Science. It covers advanced techniques such as structural econometrics, causal ML, dynamic games, and quasi-experiments. Key focus areas include ensuring model identifiability, specifying estimators (GMM, MLE), and rigorously defending exogeneity assumptions.

Provides a comprehensive, time-sensitive playbook for authors responding to reviews (rebuttals) at MLSys. It details how to triage objections, prioritize critical missing measurements, address systems objections regarding workload representativeness and baseline tuning, and structure anonymous, data-driven replies within a compressed rebuttal window.

This comprehensive guide instructs researchers on how to design rigorous and credible evaluations for Machine Learning systems. It covers critical areas such as workload selection, baseline comparison discipline, reporting four key coordinates (throughput, latency tails, memory, cost), structuring ablations, and generating scaling evidence. It ensures measurements meet academic benchmarking standards.