Skill UI

Automatically activates when chart type recommender needs arise, guiding data analysts through SQL, visualization, statistics, and BI patterns to deliver production-ready recommendations.

This advanced skill analyzes an existing codebase to identify architectural patterns, dependencies, and technical stacks. It recommends tailored Claude Code automations—including Hooks, Subagents, Skills, Plugins, and MCP Servers—to optimize development workflows, improve code consistency, and enhance overall project scalability. Use this when setting up a new project or looking to refine existing Claude Code configurations.

Assists API developers by recommending appropriate status codes, explaining patterns, and generating production-ready REST/GraphQL/OpenAPI snippets with validations.

A comprehensive guide and script collection for auditing Google Cloud Platform IAM permissions. It helps identify overly permissive bindings, unused service accounts, primitive role usage, and cross-project access risks. It utilizes gcloud CLI, Policy Analyzer, and IAM Recommender to ensure compliance and minimize the blast radius of misconfigurations.

Reads `.agent.md` or `.prompt.md` files, assesses task complexity, tooling, and requirements, then recommends the most cost-efficient GitHub Copilot model(s) (e.g., GPT-5, Claude Sonnet) with rationale for users across different subscription tiers and priorities.

Scans repository context and chat history to recommend awesome-copilot Custom Agent files that are missing or outdated locally, outlines relevance/rationale, and waits for user approval before installing or updating.

Compute weighted similarity between two harness fingerprints using cosine, categorical agreement, and topology Jaccard. Use it to rank templates, detect drift, decide reuse vs scaffold, and generate confidence scores for recommender workflows.

This skill is a comprehensive strategic guide for authors targeting the ACM Conference on Recommender Systems (RecSys). It helps assess the fit of a manuscript, guides the re-framing of work from journal or arXiv styles into a specialized conference narrative, and outlines specific requirements for evidence, evaluation (including offline metrics, user context, and leakage-aware methods), and contribution framing necessary for top-tier CS venues.

A comprehensive guide on preparing and structuring evidence packages for recommender system research, especially for academic submissions. It details how to ensure that all ranking claims, off-policy estimates, and evaluation metrics are end-to-end reproducible by providing artifacts like datasets, split scripts, model checkpoints, and propensity logs in an anonymous, verifiable repository.

A comprehensive framework for designing, auditing, and reporting empirical results in recommender systems research. It covers best practices for handling temporal splits, mitigating evaluation bias (e.g., exposure/popularity), utilizing advanced off-policy estimators (IPS, SNIPS), and structuring the critical link between offline metrics and real-world A/B test results. Essential for academic submissions.

A comprehensive guide for structuring the Related Work section for top-tier recommender systems conferences (RecSys). It advises authors on auditing novelty by positioning their contribution against adjacent research fields (e.g., SIGIR, KDD, WSDM), addressing critical lines (like reproducibility and off-policy evaluation), and ensuring the work is technically distinct from prior or concurrent studies.

A comprehensive guide detailing best practices for achieving high reproducibility in recommender system research. It covers critical areas such as dataset version pinning, defining symmetric tuning protocols, selecting full-ranking metrics, handling non-determinism via multiple seeds, and structuring rigorous divergence analysis for academic publications.