Skill UI

A comprehensive, model-agnostic toolkit for advanced LLM development. It enables iterative prompt optimization through static analysis (token usage, clarity), measures critical RAG metrics (relevance, faithfulness, coverage), and validates complex agent workflows and configurations. Essential for rigorous LLM quality assurance and cost management.

A powerful agentic workflow for complex software development. Cascade Write mode acts as an autonomous coding agent, allowing users to build features, refactor code across multiple files, run terminal commands, and debug errors—all from a single natural language prompt. Ideal for full-stack development, complex system building, and large-scale code refactoring.

This workflow suite provides robust tools for automating complex, multi-step software development tasks. It allows users to define reusable workflows (like feature scaffolding, deployment pipelines, and quality checks) that are triggered via slash commands. By integrating persistent 'Memories', it maintains project context across sessions, significantly reducing repetitive prompting and standardizing development practices.

This skill enables teams to create, manage, and organize custom prompt libraries for developer workflows within the Cascade environment. Users can build reusable prompt templates for common tasks, including code generation, code review, documentation, debugging, and general software development, ensuring consistency and boosting team efficiency.

Guides an agent to critically review a written implementation plan, execute every outlined step with required verifications, and hand off completion via finishing-a-development-branch procedures, ensuring blockers prompt clarification.

This skill manages complex coding tasks from initial implementation through testing, review incorporation, deployment, and final runtime verification. It ensures the task meets explicit Definition of Done (DoD) criteria iteratively, minimizing the need for manual prompting across multiple development stages.

A forensic tool for performing post-mortem analysis on AI-assisted coding sessions. It goes beyond simply stating what was done, determining *why* work deviated (e.g., spec ambiguity, repo friction, agent error), identifying scope creep, and classifying rework patterns. It provides evidence-backed recommendations to improve prompts, codebase health, and future development workflows.

Hookify rules enable developers to define custom, event-driven workflows using Markdown files with YAML frontmatter. These rules monitor specific operational events (such as bash commands, file edits, or user prompts) and can trigger warnings or block operations when predefined regex patterns are matched. They are essential for enforcing coding standards, preventing dangerous operations, and guiding development adherence in complex software environments.

This advanced prompt acts as a structured guide for generating comprehensive, enterprise-grade technical implementation plans. It forces the detailed breakdown of a feature based on a PRD, covering system architecture (Mermaid diagrams), database schema design, API specifications, frontend component hierarchy, and deployment strategies, all while adhering to a monorepo structure. Ideal for large-scale SaaS development.

This tool generates a complete, production-ready project scaffold for a Model Context Protocol (MCP) server written in Ruby. It includes the necessary file structure, gem dependencies, Rake tasks, and boilerplate code for defining tools, prompts, and resources, accelerating backend development for AI applications.

This micro-prompt guides AI agents to adopt the mindset of an interactive programmer. It emphasizes using the REPL (Read-Eval-Print Loop) for systematic system exploration and modification, treating the system state as the ultimate source of truth. It also reminds the agent to utilize structural editing tools and maintain todo lists, significantly improving the accuracy and efficiency of the development workflow in live coding environments.

This advanced skill optimizes LLM context usage by implementing strict rules for prompt caching, efficient log pruning, AST-based code inspection, and compact data structuring. It minimizes token consumption and maximizes information density, suitable for highly technical and iterative development workflows.