Skill UI

A comprehensive CI/CD quality gate checklist for Spring Boot projects. It guides developers through mandatory steps including building, static analysis (SpotBugs, PMD), running unit/integration tests, measuring code coverage, performing security scans (OWASP), and reviewing diffs before merging or deploying.

This comprehensive guide demonstrates how to achieve highly testable Swift code using protocol-based dependency injection. By abstracting external concerns—such as file systems, networking, and external APIs—into small, focused protocols, developers can write deterministic unit tests that mock side effects and error paths, ensuring robust architecture across different environments (App, Test, Preview).

This skill automates the Test-Driven Development (TDD) repair loop. Given a failing unit test, it spawns a bounded, headless LLM agent to automatically modify the source code until the test passes. It closes the development loop by treating the test's pass/fail status as the definitive fitness function, ideal for CI failure triage and local TDD workflows.

This workflow guides developers through Test-Driven Development (TDD) principles. It mandates writing tests (unit, integration, E2E) before writing implementation code, ensuring minimum 80% test coverage. The structured process (Red -> Green -> Refactor) guarantees robust code quality, effective bug fixing, and safe feature addition, while integrating Git checkpoint commits for full traceability.

This tool helps development teams identify gaps in their current test coverage. It analyzes code paths and detects areas that lack adequate unit or integration tests, generating actionable suggestions for improvement. Use it when assessing code quality after a feature implementation, or when a user needs to know which parts of the codebase require further testing.

Architectural guidance for building modules with the tinystruct Java framework: @Action routing, dual CLI/HTTP mode, bin/dispatcher scripts, ActionRegistry, unit testing, JSON handling and database persistence.

This guide helps researchers determine the most appropriate top-tier academic conference (e.g., CoNEXT, SIGCOMM, NSDI, IMC) and submission cycle for their networking research. It provides detailed criteria, routing tables, and tests (e.g., platform-realism test, model-swap test) to ensure the contribution is evaluated on a real target and fits the target community's scope and rigor.

A comprehensive guide for researchers to determine if their human-centered research belongs in CSCW. It provides a critical unit-of-analysis test (group, community, organization) and a routing decision process to ensure the paper's focus is on cooperative practice, not just individual interaction. Use this before drafting to optimize submission to the correct academic venue.

A comprehensive guide for ensuring rigorous statistical inference in experimental economics. It covers critical areas such as power justification, determining the correct unit of observation (cluster-robust SEs), handling multiple comparisons, and applying non-parametric tests. This framework helps researchers transition from preliminary findings to statistically robust, publishable conclusions.

A comprehensive guide for authors publishing in economic geography journals, detailing how to structure manuscripts to satisfy both economic and geographical communities. It provides methodological advice on transitioning between formal NEG/quantitative-spatial models and conceptual/evolutionary frameworks, ensuring that the underlying mechanisms and assumptions are transparent and testable.

A comprehensive academic guide for refining research questions intended for the Journal of Economic Geography (JEG). It implements a rigorous "two-community test," ensuring the topic successfully bridges geographical economics (quantitative models) and human geography (place-based studies). Use this checklist before major data collection to validate the mechanism and justify the spatial role of the research.

This guide provides an advanced framework for conducting rigorous causal inference and policy evaluation, essential for academic journals. It covers state-of-the-art methods including DiD, RDD, Synthetic Control, and experimental analysis. The core focus is moving beyond simple p-values to estimate effect magnitudes in tangible 'decision units' (e.g., dollars per household, percentage points). It mandates comprehensive stress-testing, including pre-trend checks, placebo outcomes, and detailed subgroup heterogeneity analysis, ensuring results are immediately actionable by regulators.