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Guides authors through adapting test-driven development to agent skills documentation, covering when to create skills, required background, directory structure, and how to run pressure scenarios to ensure compliance before publishing.

This guide provides a comprehensive workflow for Test-Driven Development (TDD) within Laravel applications. It covers best practices for writing unit, feature, and integration tests using modern tools like Pest and PHPUnit. Learn how to manage database states, mock external services, and achieve high code coverage efficiently.

A comprehensive guide to writing robust and maintainable tests in Rust. It covers various advanced testing patterns, including Unit, Integration, Async, and Property-Based testing. Learn how to apply the TDD (Test-Driven Development) cycle (RED-GREEN-REFACTOR) to enhance code reliability and ensure high code coverage.

Summarizes the Test-Driven Development workflow with RED/GREEN/REFACTOR phases, the AAA pattern, prioritization cues, anti-patterns, and AI-augmented agent roles to guide implementation.

A software development methodology where tests are written before implementation code. It follows the red-green-refactor loop to ensure code quality and reliability. Ideal for bug fixing, new feature implementation, and maintaining existing functionality. Helps prove code correctness and reduces technical debt.

This guide implements the Red-Green-Refactor cycle, the core methodology of Test-Driven Development (TDD). It teaches developers to first write a failing test (RED), then implement the minimum required code to make the test pass (GREEN), and finally improve the structure and clarity of the code without changing its behavior (REFACTOR). Use this guide when mastering TDD, ensuring robust unit test coverage, or following best practices for writing resilient software.

Performs a systematic and structured investigation to determine the true source of bugs, errors, and unexpected system behavior, rather than merely treating symptoms. This method utilizes techniques like the Five Whys, hypothesis-driven testing, stack trace analysis, and gathering evidence (logs, git diffs) across multiple phases. It is ideal for complex, intermittent, or deeply rooted technical issues in software development.

This skill guides the end-to-end process of building automated evaluation pipelines for Python applications powered by Large Language Models (LLMs). It teaches how to define evaluation criteria, instrument the application, build golden datasets, and run real-world evaluations using tools like pixie, ensuring the app's logic (not just the LLM) is rigorously tested. Ideal for QA, benchmarking, and improving LLM-based services.

This skill enables personal execution of implementation plans task-by-task in the current session without spawning subagents per task. It employs Test-Driven Development (TDD) for verification, tracks progress via a ledger, and resolves conflicts autonomously. Suitable for inline execution choices or when subagent tools are unavailable, it reduces context costs while ensuring quality through final review.

Implements approved development tasks using Test-Driven Development methodology with autonomous or manual sub-agent dispatch. Supports multiple review modes (required/inline/off) and integrates with project specifications, requirements, and design documents for comprehensive implementation validation. Automatically discovers validation commands and establishes repository baselines before execution.

Test-Driven Development (TDD) is a development methodology that emphasizes writing failing tests first, followed by writing minimal code to pass them (Red -> Green), and finally refactoring the code (Refactor). This approach ensures that code is always verified against its intended public behavior, making the system robust, maintainable, and resilient to internal changes. It teaches developers to focus on 'what' the system does, rather than 'how' it does it.

This skill guides a systematic process for debugging reported issues. It explores the codebase to identify the root cause, traces the code path, and then generates a comprehensive GitHub issue. The fix plan strictly follows the Test-Driven Development (TDD) Red-Green cycle, ensuring proposed solutions are robust and durable, surviving major refactors.