Skill UI

TaskEither is a functional programming utility from fp-ts designed for handling asynchronous operations that might fail. It provides a robust, type-safe mechanism for managing complex async workflows, such as chained API calls or promise wrappers. Instead of relying on traditional try/catch blocks where errors can be lost, TaskEither forces the developer to explicitly handle both success and failure paths, making error management predictable and composable.

A comprehensive quick reference guide for functional programming types in TypeScript using the fp-ts library. It helps users decide which type (`Option`, `Either`, `Task`, etc.) to use based on whether operations are synchronous, asynchronous, and if they involve errors or missing values. It provides decision trees, common import patterns, and utility function patterns for robust TypeScript development.

This skill systematically audits a given project by analyzing its historical memory, rollout summaries, and local skill definitions. It identifies repeated complex workflows, recurring operational patterns, or areas where existing skills are stale. The audit process guides the user to recommend the highest-value skills to either add (if a new pattern emerges) or update (if current skills are outdated or incomplete), ensuring the AI agent operates with the most accurate and context-specific knowledge base for optimal project execution.

A library providing type-safe monadic abstractions for Go, including Option, Result, and Either. It allows developers to eliminate nil pointer risks and manage complex error handling using composable, functional pipelines, significantly improving code robustness and readability in Go applications.

Command-line helper to renew or reactivate Google Workspace Events subscriptions, either by specifying a subscription name or by bulk renewing those expiring within a configurable window, ideal for automating cron-based maintenance.

This skill is designed to retrieve and display detailed usage metrics for GitHub Copilot within organizations and enterprises. Users can analyze adoption rates, total suggestions, acceptance rates, and chat interactions, broken down either by the entire organization/enterprise or on a per-user basis. It supports querying data for specific historical dates using GitHub CLI and REST API.

This agent guides users in selecting the optimal Azure role assignment that strictly adheres to the principle of least privilege. Given a set of desired permissions, it utilizes specialized tools to either identify a suitable built-in role or generate a custom role definition. Finally, it provides actionable code snippets, including CLI commands and Bicep templates, ensuring proper and secure role assignment.

Conducts STRIDE-A threat modeling on repositories or systems, running either a full single-pass analysis with architectures, DFDs, prioritized findings, and executive assessments, or an incremental update that compares a baseline report to the current code and highlights deltas.

This comprehensive guide details the idiomatic and efficient way to write Scala code. It covers crucial modern practices, such as utilizing functional transforms over traditional loops, embracing pattern matching, using Option/Either for robust error handling instead of null checks or exceptions, and managing concurrency safely. Essential reading for any Scala developer aiming for high-quality, maintainable, and performant codebases.

This guide outlines the rigorous standards for achieving robust reproducibility in robotics research papers, especially those involving physical hardware. It details a two-tier approach, defining results as either fully *rerunnable* (code/simulation) or fully *auditable* (hardware logs/protocols). Key requirements include creating comprehensive hardware specification ledgers, logging detailed sensor data (rosbags), separating deterministic from stochastic claims, and structuring the entire repository for peer review.

This guide establishes standards for reporting reproducibility in robotics research, addressing the difference between traditional ML and physical systems. It mandates that every claim must be classified as either 'rerunnable' (executable code/simulation) or 'auditable' (detailed logs, hardware specs, and protocols). It covers creating a complete evidence map, detailing the hardware specification ledger, explicitly reporting the sim-to-real gap, and writing an honest availability statement, all within the main paper body.

This tool provides a comprehensive pre-submission checklist designed for authors submitting survey papers (either a proposal or a full draft) to the Journal of Economic Literature (JEL) via the AEA portal. It ensures compliance by verifying JEL codes, disclosure statements, data availability, and adherence to style guides, helping authors finalize their submission package.