Skill UI

Generates HTML5 Canvas-based breadboard mockups and diagrams for retro computing builds, showing component placement, wiring, and labeled 6502/555 timer layouts as visual references or teaching aids.

Converts Oracle PL/SQL stored procedures and functions into PostgreSQL PL/pgSQL equivalents by translating Oracle-specific syntax, keeping control flow and signatures intact, honoring type-anchored parameters, and applying COLLATE "C" or orafce helpers when needed during database migrations.

This guide provides comprehensive best practices for creating optimized, secure, and efficient multi-stage Dockerfiles. Learn how to separate build environments (builder stage) from minimal runtime environments, significantly reducing image size and attack surface. Topics covered include layer caching optimization, security hardening (non-root users), and using minimal base images for reproducible builds.

This guide provides proven architectural patterns for building complex, reliable, and scalable workflows using n8n. It covers core patterns like Webhook processing, HTTP API integration, database synchronization, AI agent design, and scheduled tasks. Learn how to select the right pattern and utilize advanced components for robust automation.

Automates Notion pages, databases, blocks, comments, and users through Composio’s Notion toolkit via Rube MCP, guiding connection setup, tool sequencing, and schema-aware workflows for operations.

A structured methodology for coordinating multiple, simultaneous investigation threads to determine the root cause of complex system failures, performance regressions, or integration bugs. This skill decomposes a large problem into independent tracks (e.g., database, application code, infrastructure), assigns ownership, and manages structured sync points (Continue/Pivot/Converge) to converge on a solution faster than serial debugging. Essential for high-stakes production incident response.

A sophisticated, file-based system designed for organizing and tracking complex, multi-step projects. It utilizes structured Markdown files (task_plan.md, progress.md, findings.md) to maintain a complete record of plans, research findings, and progress updates. Ideal for project analysis, workflow management, and ensuring data integrity throughout the planning lifecycle.

This file-based system provides a structured method for organizing and tracking the progress of complex, multi-stage projects or extensive research tasks. It automatically generates and manages key files like `task_plan.md`, `findings.md`, and `progress.md`. It is ideal for users needing to deconstruct, plan, and maintain comprehensive visibility over large, complex workflows, supporting automatic session recovery and integrity checks.

Scaffold a production-ready Python MCP server with uv-based tooling, FastMCP configuration, async tools, structured schemas, and comprehensive error handling plus deployment/testing guidance.

A comprehensive, multi-phase quality engineering audit tool designed to test codebases beyond standard unit tests. It first explores the codebase's domain and architecture to derive behavioral requirements, then executes a rigorous sequence including spec-traced functional tests, three-pass code reviews, and a multi-model specification audit (Council of Three). It is specifically engineered to find the critical 35% of real-world defects that structural code review alone might miss. Works with any programming language.

This skill generates a detailed, multi-phase plan for complex, multi-file refactoring projects. It ensures safety by first analyzing the codebase, identifying dependencies, and structuring changes in a safe sequence (e.g., Types first, then implementations, followed by tests and cleanup). It forces the AI to output a comprehensive plan, including rollback steps and verification points, and waits for user confirmation before making any actual code changes.

A persistent, graph-based knowledge base designed to accumulate structured knowledge throughout the entire research lifecycle. It stores and connects diverse entities—including published papers, novel ideas, conducted experiments, and testable claims. Inspired by LLM Wiki patterns, it ensures knowledge compounding, meaning the wiki becomes progressively smarter with every piece of input, rather than requiring re-derivation for every query.