Skill UI

Generates comprehensive, structured documentation for microservices or internal platforms. This skill covers service identity, architecture context, APIs, SLAs, data classification, and operational runbooks, making it ideal for developer portals, service READMEs, and internal service registries. It ensures new team members can quickly understand how to use and depend on a shared service.

This skill acts as a comprehensive maintenance orchestrator for the brooks-lint plugin repository. It executes a rigorous, sequential subagent pipeline—including authoring, evaluation, QA, trigger auditing, and release management. Use this when adding, editing, or updating skills to ensure all related components (manifests, README, CHANGELOG) are synchronized and adhere to strict quality gates throughout the development lifecycle.

This skill guides users on optimizing their GitHub presence for maximum discoverability and professional appeal. It covers best practices for writing compelling README files, structuring comprehensive profile READMEs, and strategically implementing trust badges and marketing elements to enhance project visibility and adoption.

Automates the creation and publication of polished, functional Gradio demos on Hugging Face Spaces for any user-provided LoRA model. This skill handles the complex workflow of designing the optimal UI, selecting the correct inference pipeline (e.g., text-to-image, video-to-video), and generating all necessary files (app.py, requirements.txt, README.md) to deploy a complete, ready-to-use AI playground.

Provides a comprehensive guide for authentically marketing and growing open source projects. Covers best practices for GitHub optimization (README, repository setup), community building, and implementing sustainable growth strategies, ensuring the project is discoverable and valuable.

An interactive skill designed to interview users to capture project vision, core decisions, and technical preferences. It systematically converts raw discussions into structured documentation, maintaining the project's history in ADRs (Architectural Decision Records) and its overarching goal in the README.

This skill manages the secure transition of private internal projects to public open-source releases. It executes a three-stage pipeline: Forking (removing secrets), Sanitizing (verifying clean code), and Packaging (generating required documentation like README, setup.sh, and CLAUDE.md). It ensures code safety and readiness before public deployment.

This package assists researchers in assembling a compliant data and code deposit for AEJ: Economic Policy manuscripts. It ensures adherence to the AEA Data and Code Availability Policy by structuring the openICPSR deposit, generating the required README, and managing restricted data access paths. It is critical for passing the mandated pre-publication reproducibility check.

This guide outlines the rigorous standards for creating a research replication package required by major academic journals (like The Economic Journal). It details the necessary components—including a comprehensive README, raw data, executable master code, and output logs—to ensure that all figures, tables, and results are fully reproducible by third parties. Crucial for pre-acceptance checks and maintaining academic rigor.

This comprehensive guide outlines the strict requirements for preparing replication and data packages for submission to The Econometrics Journal (EctJ). It covers everything from README structure and detailed code inclusion to ensuring Monte Carlo determinism and managing restricted data, ensuring the method's credibility for reviewers.

This guide outlines the mandatory structure for creating a reproducible research package, required by academic journals like the European Economic Review (EER). It details how to properly assemble raw data, master analysis scripts, environmental specifications, and comprehensive documentation (README) to ensure that reported results can be independently verified by others. It is essential for any paper involving empirical analysis or simulations.

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.