Skill UI

This guide details the USENIX FAST artifact evaluation process, providing systematic advice for researchers submitting system papers. It explains the requirements for earning the three USENIX badges (Available, Functional, Reproduced). Key topics include managing hardware dependencies in storage systems, packaging best practices (e.g., Docker containers, raw logs, comprehensive READMEs), and ensuring the final artifact is maximally reproducible for external evaluation.

This guide details best practices for submitting research artifacts to academic conferences, following the ACM Artifact Review and Badging scheme. It covers everything from required packaging plans (Docker, README, data provenance) to achieving various levels of validation (Functional, Reusable, Reproduced). Learn how to structure your submission to ensure maximum reproducibility and pass rigorous evaluator checks on a clean machine.

This guide provides authors with comprehensive best practices for submitting research artifacts (e.g., compilers, benchmarks, proofs) for post-acceptance evaluation at PLDI. It details how to structure the submission, ensuring reproducibility, containerization, and clear documentation to achieve Functional, Reusable, or Available badges.

This comprehensive guide outlines the requirements for submitting computational artifacts for post-acceptance evaluation at PPoPP. It details PPoPP's unique badge policy (Functional, Reusable, Results Reproduced), addresses the challenges of ensuring results reproduce across different hardware architectures, and provides a detailed packaging plan, including containerization, README structure, and provenance documentation for maximizing reproducibility.

This guide details the methodology for packaging scientific artifacts (code, solvers, data, logs) for machine learning submissions. It focuses on creating double-blind, self-contained ZIP archives that allow reviewers to independently and quickly verify complex probabilistic claims, even under strict time constraints. It covers best practices for anonymity, structured README generation, and handling data/licensing.

Creates task-oriented technical documentation with progressive disclosure for READMEs, API docs, architecture docs, and markdown. Also generates formal security reports (reverse engineering, penetration testing, CTF, malware/APT analysis) in the user's project directory with Evidence-Finding-Path chain.

This skill generates complete, deploy-ready static landing pages by analyzing a source code repository. It reads key files (README.md, CHANGELOG.md, etc.), extracts structured content (features, installation guides, FAQ), and compiles it into a professional, responsive HTML/CSS/JS site. Ideal for standardizing open-source project showcases, converting technical documentation into marketing pages, or quickly building a project homepage.

This skill guides the creation of technical documentation (such as RFCs, READMEs, PR descriptions, and architecture guides) that minimizes the mental effort required for the reader. It emphasizes structural patterns like 'Lead with the answer,' 'Chunking,' and 'Progressive disclosure' to ensure content is scannable, actionable, and easy to review, significantly reducing cognitive load for maintainers and contributors.

Run a read-only audit to detect documentation drift against code, APIs, and configurations. Identifies stale, missing, or inconsistent docs across READMEs, API docs, and user guides. Compares documentation claims with actual implementation to ensure accuracy without modifying files.

This comprehensive workflow bundle guides the entire documentation lifecycle for software projects. It covers planning, API specification (OpenAPI), architectural design (C4 model), code commenting (JSDoc/TSDoc), generating READMEs, and maintaining structured knowledge bases (Wikis, Changelogs). Use this system when documenting a new product, maintaining an evolving codebase, or creating comprehensive technical guides.

This guide provides comprehensive templates and best practices for structuring all types of technical documentation. It covers essential formats including README files, REST API endpoints, code comments (JSDoc), Changelogs, Architecture Decision Records (ADR), and AI-optimized documentation. Use these templates to ensure your project documentation is standardized, scannable, and easily consumable by both human developers and AI agents.

Generate comprehensive, absurdly thorough README.md documentation for any software project, covering installation, usage, configuration, and contribution guidelines.