Skill UI

Manages branching strategies, conventional commits, PR checklists, and daily Git commands so you stay disciplined when creating features, resolving conflicts, and collaborating through pull requests.

Automates Screenshotone workflows through the Composio Screenshotone toolkit and Rube MCP, guiding tool discovery, connection validation, schema-compliant execution, and memory/session handling with a search-first discipline.

A disciplined debugging ritual that forces you to investigate errors, reproduce issues, and analyze patterns before proposing fixes, ensuring root causes are understood across multi-component systems and minimizing risky guesswork under pressure.

Comprehensive TDD workflow guidance covering the Red/Green/Refactor rhythm, Arrange-Act-Assert test structure, Jest/Vitest, pytest, and Go patterns, coverage rules, mocking advice, and anti-pattern warnings to keep teams disciplined across languages.

A detailed, multi-phase workflow that strictly enforces the Test-Driven Development (TDD) discipline using the Red-Green-Refactor cycle. This guide covers the entire software development lifecycle, from test specification and architecture design to minimal implementation, rigorous refactoring, and comprehensive integration testing. It mandates high code and test coverage (e.g., 80% line coverage) to ensure robust, maintainable, and high-quality production code.

Document outlines testing strategies such as contract, snapshot, mutation, property-based, integration with containers, and disciplined test organization to help teams design reliable suites that cover behavior, API contracts, and invariants.

Create a disciplined, developer-ready implementation plan whenever a multi-step spec arises; document every file, command, and bite-sized action with precise steps, tests, and commit guidance so any engineer can execute confidently with DRY/YAGNI/TDD practices.

Guides engineers through a disciplined four-phase debugging workflow so they investigate root causes, analyze patterns, test hypotheses, and implement fixes based on evidence before touching code.

This comprehensive guide outlines a rigorous, human-in-the-loop engineering workflow for leveraging advanced AI agents to complete complex implementation tasks. It mandates defining clear completion criteria, decomposing work into manageable units, and strategically routing models (Haiku, Sonnet, Opus) based on task complexity. The workflow emphasizes continuous evaluation, regression testing, security review of invariants and edge cases, and strict cost discipline to ensure robust and reliable software development using AI assistance.

This skill enforces rigorous governance and discipline when AI modifies systems, debugs code, or makes root-cause claims. It mandates safety gates (e.g., backup, blast radius check), demands evidence for every diagnosis, and prevents common AI shortcuts like guessing or deflecting. Use this for critical code modifications, complex debugging, and high-stakes deployments to ensure thoroughness and safety.

This skill enforces a rigorous, 5-phase debugging protocol (Reproduce, Isolate, Root Cause, Fix, Verify) for AI coding agents. It forces a disciplined approach, preventing agents from guessing fixes or making premature edits. Use this when debugging complex, intermittent, or hard-to-isolate bugs to ensure the root cause is fully confirmed before any code changes are applied, maximizing debugging accuracy.

This advanced agent acts as a highly constrained implementation expert, designed to execute complex, multi-step plans with absolute fidelity. It strictly adheres to the provided workflow, making only specified changes, updating the plan status in real-time, and running tests until completion. Ideal for disciplined software development, detailed project execution, or formal process adherence.