Skill UI

This guide provides detailed stylistic advice for authors submitting papers to WSDM. It emphasizes structuring work around observed user and web behaviors, adopting a self-contained voice that preemptively addresses reviewer objections, and ensuring technical precision in all sections, including ethics and limitations. It transforms raw research into a publication-ready, principle-grounded narrative.

This skill guides legal scholars in transforming a broad legal topic into a single, sharp, contestable, and generalist thesis statement suitable for top-tier law journals. It forces the definition of the contribution's nature—whether descriptive, doctrinal, theoretical, or normative—and structures the argument into a clear, defensible 'contribution contract' for academic editors.

A guide on how to sharpen the marginal contribution of an academic manuscript for specialized journals (like JEG). It teaches authors to transform vague statements into precise, one-sentence claims that clearly advance understanding in growth theory or empirical econometrics, focusing on mechanisms and causal channels.

This skill guides researchers in transforming raw empirical findings into robust, portable, field-level theoretical contributions suitable for top-tier journals. It provides a structured framework for elevating results by defining explicit communication mechanisms, establishing scope conditions, and articulating observable implications. It ensures the work contributes to the core body of theoretical communication knowledge, moving beyond simple effect documentation.

Astropy is the essential Python package for astronomical research and data analysis. It provides a comprehensive suite of tools for handling celestial coordinate transformations (e.g., ICRS, Galactic), managing physical units and quantities, processing FITS files, performing complex cosmological calculations, and ensuring precise time handling. It is indispensable for any project involving scientific data in the field of astronomy and astrophysics.

Cirq is a powerful, open-source Python framework developed by Google Quantum AI. It allows users to design, simulate, and execute complex quantum circuits. It supports advanced features including parameterized circuits, noise modeling, circuit transformation, and seamless integration with multiple quantum hardware backends (e.g., IonQ, Google Quantum, Pasqal).

A detailed guide for authors writing the policy implications section of high-level financial research journals. It teaches how to transform abstract academic findings into concrete, actionable policy recommendations for monetary authorities, regulators, and financial institutions, ensuring suggestions are evidence-based, avoid vague generalizations, and specify clear mechanisms and boundaries.

Polars is a lightning-fast DataFrame library designed for efficient, memory-efficient data processing in Python and Rust. Built upon Apache Arrow, it supports lazy evaluation and parallel execution, making it ideal for ETL pipelines, large-scale data transformations, and scenarios where standard Pandas performance is insufficient. It excels at handling datasets up to 100GB that fit within available RAM.

This skill helps developers and QA engineers transform vague, intermittent, or poorly documented bug reports into a minimal, reproducible, and evidence-backed brief. It guides the user to isolate the exact failure conditions, separate expected outcomes from actual errors, and reduce the issue to the smallest safe failing fixture, ensuring diagnosis occurs before premature repair.

This skill transforms raw genomic identifiers (VCF, HGVS, rsID) into highly annotated, consequence-predicted records. It utilizes powerful, open-source tools like Ensembl VEP and gnomAD to predict variant consequences (e.g., missense, nonsense), map coordinates, and attach population allele frequencies. Furthermore, it links these molecular findings to clinical and phenotype context extracted by OpenMed, forming a comprehensive research record.

This skill converts unstructured documents (PDF, EPUB, DOCX, etc.) into actionable, structured agent skills. Instead of providing simple summaries, it deep-dives to extract core frameworks, mental models, key principles, techniques, and anti-patterns from educational material. Use this when you need to transform academic knowledge into a reusable, machine-readable toolkit for agents like Copilot CLI, Amp, or Claude Code.

A specialized downstream skill for complex CTF challenges, focusing on cryptographic analysis, steganography, and mobile system forensics. It guides the user through recovering complex data chains, such as decoding blobs, tracing cryptographic transforms (keys, IVs, nonces), inspecting hidden media payloads, and analyzing mobile trust boundaries (e.g., APK/IPA signing logic, local storage, SSL pinning). This skill is designed to be used only after a main sandbox orchestrator has established the operational context.