Skill UI

A comprehensive guide for authors to position their ML systems research by mapping the vast, scattered literature across multiple venues (OSDI, SOSP, NeurIPS, open-source projects). It details how to audit novelty, handle fast-moving advancements, and write a defensible delta statement against both academic and industrial prior work.

This skill acts as a comprehensive workflow router for authors preparing Operations Research (OR) manuscripts for top-tier journals. It guides the user through the entire modeling-to-submission lifecycle, mapping the necessary stages—from initial theory development and literature positioning to computational data analysis and final contribution framing. Its purpose is to ensure methodological rigor and adherence to the journal's specific, multi-stage norms.

This guide details the standards for submitting theoretical computer science papers, focusing on the required "formal-claims package." It emphasizes that the deliverable is not a runnable systems artifact, but a rigorous academic package containing a complete, self-contained proof appendix, a clear mapping of every stated claim to its proof location, and a full, publicly available version on arXiv.

This guide details the rigorous process for packaging and evaluating mechanized proofs (e.g., in Coq, Isabelle, Lean, Agda) submitted for academic conferences. It outlines criteria for achieving Functional, Reusable, and Available badges, focusing heavily on completing the formal development, auditing axioms, and ensuring perfect reproducibility by pinning toolchains and mapping theorems to source files.

This guide provides strict guidelines for authors writing the "Related Work" section for top-tier programming language theory conferences like POPL. It emphasizes moving beyond general literature reviews to pinpoint precise, technical "delta sentences" that prove novelty against neighboring formal systems. It covers citation standards, venue mapping, and attribution verification for maximum academic impact.

This guide details best practices for achieving high standards of reproducibility in visual analytics papers, focusing on open materials, verifiable code, and rigorous documentation. It covers mapping figures to data sources, preregistering studies, documenting data provenance, and defining the level of reproducibility achieved (e.g., turnkey, scripted).

This guide helps researchers move beyond decorative theory to construct 'load-bearing' conceptual or analytical frameworks for World Development (WD) manuscripts. It outlines how to transform vague ideas into structured, testable mechanisms by stating clear mechanisms, deriving discriminating predictions, mapping predictions to evidence, and defining scope conditions, ensuring the theory actively drives the empirical argument.

This specialized skill is designed to trace and prove the complete flow of OAuth or OIDC authentication, acting as a downstream specialization after an orchestrator has established sandbox assumptions. It meticulously tracks the entire journey—from initial redirect and parameter handling (state, nonce, PKCE) through token exchange, claim mapping, and ultimately to the point where an identity or privilege is accepted by the backend service. Use this when the critical task is proving the security and mechanics of the authentication chain.

A specialized downstream skill for deep analysis of complex web rendering pipelines. Use this to trace vulnerabilities or logical flaws residing in Server-Side Rendering (SSR), template data, route loaders, or client-server hydration boundaries. It compares template-level gating against backend handler enforcement to pinpoint the decisive weak point in the render chain.

This tool performs a comprehensive analysis of AI configuration file evolution within a Git repository. It tracks when specific AI configs were first introduced, charts monthly commit velocity related to AI features, highlights key PRs that shaped the development, and synthesizes the project's AI maturity phases. Ideal for mapping AI adoption timelines and assessing technical maturity.

This skill maps clinical concept spans (disorders, anatomy, procedures, etc.) extracted by OpenMed to SNOMED CT concept IDs (SCTID). It performs complex, out-of-process terminology resolution using standard FHIR R4 endpoints ($lookup, $translate, ValueSet/$expand) against a user-supplied, licensed terminology server (e.g., Ontoserver, UTS). Ideal for generating rich, hierarchy-aware clinical codes, performing subsumption queries (ECL), or translating codes between clinical standards (like ICD-10 to SNOMED CT).

This tool analyzes a given codebase to automatically identify high-value, actionable automation routines. It surfaces use cases across three primary trigger types: scheduled cron jobs, external API calls, and GitHub repository events. By mapping manual pain points to autonomous workflows, it helps teams implement robust CI/CD and DevOps practices.