Create, optimize, critique, and programmatically structure prompts for AI systems. Use this skill whenever the user is designing or improving a static prompt, system prompt, coding prompt, agent prompt, workflow prompt, MCP-oriented prompt package, or an algorithmic prompt optimization pipeline. Also use it when the user asks to turn vague AI behavior into a precise instruction set, tool policy, agent spec, evaluation metric, or prompt architecture.
Enforce and manage DESIGN.md specifications, extract design systems from URLs, and combine design reasoning with token roles to prevent drift.
Assumption-first architecture review skill to stress-test project plans and expose hidden risks.
Forces the agent to act with a Claude-like product mindset, prioritizing user journey, UX states, and visual quality before coding.
Compiles and extracts session knowledge into a living, interconnected LLM-Wiki. Instead of writing isolated logs, it identifies key entities, updates cross-referenced topic files in docs/knowledgelib/, and maintains an index and chronological log. Use this to ensure persistent, compounding project knowledge.
Overrides the agent's behavior to enter an interactive, consulting mode. Instead of guessing or immediately executing a task, the agent will analyze the request and ask guiding, targeted questions to clarify intent, constraints, and requirements first.
Expert Coolify management skill for self-hosted infrastructure. Deploy, manage, provision, and debug services, handle environment variables, and automate workflows with or without MCP.
Generates an idiomatic SDK skeleton for Python, TypeScript, Go, or Kotlin from an OpenAPI spec file. Rather than auto-generated machine code, it designs client code with built-in features like retry/timeout/pagination, at the quality level a real developer would write.