Skill UI

This skill generates highly disciplined, forceful, and timeless English prose, specifically designed to bypass modern AI writing clichés and filler language. It is ideal for essays, literary rewrites, or demanding analytical writing that requires sharp, concrete, and confident phrasing. It enforces strict rules against common AI cadences, therapeutic fluff, and vague transitions, ensuring the output is muscular and substantive.

Use before creative or constructive work (features, architecture, behavior). Transforms vague ideas into validated designs through disciplined reasoning and collaboration.

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.

Structured manuscript and grant review guidance that walks reviewers through methodology, statistics, reporting compliance, and constructive feedback for rigorous scientific evaluation across disciplines.

A powerful tool for architecting production-grade multi-agent workflows. It allows users to define complex, multi-step AI pipelines using structured patterns (sequential, parallel, hierarchical). Key features include defining explicit handoff contracts, implementing robust failure handling, and managing context/cost discipline across long-running processes, ensuring reliable and scalable AI system deployment.

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.