Scripting

TypeScript development best practices including SOLID, KISS, YAGNI, DRY principles, design patterns, and anti-patterns to avoid.

Conduct comprehensive web research on any topic using available MCP tools including Perplexity for current information, Playwright for web automation, and DeepWiki for encyclopedia-style content. Use when users request research, information gathering, or need to investigate a topic.

Analyze, update, and manage Python dependencies in pyproject.toml, checking for version compatibility, security vulnerabilities, and suggesting upgrades.

This skill should be used when configuring or using the OpenCode CLI for headless LLM automation. Use when the user asks to "configure opencode", "use opencode cli", "set up opencode", "opencode run command", "opencode model selection", "opencode providers", "opencode vertex ai", "opencode mcp servers", "opencode ollama", "opencode local models", "opencode deepseek", "opencode kimi", "opencode mistral", "fallback cli tool", or "headless llm cli". Covers command syntax, provider configuration, Vertex AI setup, MCP servers, local models, cloud providers, and subprocess integration patterns.

Systematic technical debt reduction and code optimization agent. Use when refactoring code, reducing complexity, eliminating code smells, improving performance, cleaning up unused code, or modernizing legacy patterns. Handles dependency updates, architecture improvements, and codebase health metrics.

Master Python from fundamentals to advanced OOP, async programming, and web frameworks.

This skill should be used when the user asks about "namespaces", "singleton", "TileManager", "GameManager", "TokenManager", "InputManager", "data flow", "class responsibilities", "layers", "folder structure", "code organization", "design patterns", "ScriptableObject", "databases", or discusses Zero-Day Attack codebase architecture and patterns.

Asyncio patterns in Python for high-concurrency IO-bound tasks. Includes coroutines, task management, and asynchronous resource handling. Triggers: asyncio, python-async, coroutine, await, async-gather, async-generator, event-loop.

ESLint 9.x flat configuration patterns. Use when setting up ESLint with TypeScript.

Provides patterns and examples for generating test data using factory libraries. Use this skill when you need to create realistic and maintainable test data for your application. It covers factory patterns (e.g., Factory Boy for Python, Polly.js for JavaScript), handling relationships between models, using traits for data variations, sequence generation for unique values, and cleanup strategies. Trigger this skill for tasks involving test fixtures, data seeding for tests, or factory implementation.

Best practices when developing in Ruby codebases

Locate code, trace flows, and find usages in codebases. Use proactively for "where is", "how does", "find", "called", "used", or any code search question.

Concurrent branch development with devcontainers using clone-based isolation

Automatically reviews DevPrep AI code for quality standards including ESLint compliance, TypeScript strict mode, 180-line file limits, complexity under 15, proper naming conventions, import patterns, and architectural compliance with the 6-folder structure

This skill should be used when the user asks to "create a codex prompt", "write a CTM", "format task for codex", "optimize prompt for codex", "submit to codex", or needs to generate a Codex Task Manifest (CTM) for delegation to OpenAI's Codex agent.

Expert assistance with GraphPad Prism scientific graphing and statistics software, specializing in Prism scripting for automation, batch processing, and data analysis workflows.

Guides on analyzing and improving test coverage for Python (pytest-cov) and JavaScript (c8) projects. Use when a user asks to set up test coverage, generate coverage reports, understand why coverage is low, or improve their test coverage.

Invoke IMMEDIATELY via python script when user requests codebase analysis, architecture review, security assessment, or quality evaluation. Do NOT explore first - the script orchestrates exploration.

Machine learning in Python with scikit-learn. Use when working with supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), model evaluation, hyperparameter tuning, preprocessing, or building ML pipelines. Provides comprehensive reference documentation for algorithms, preprocessing techniques, pipelines, and best practices.

Best practices for writing Python tests - use when creating or improving test coverage