What problem does it solve?
This skill provides a structured methodology to design, validate, and operate LLM-powered projects, helping teams decide when to use LLMs versus traditional approaches and align architecture with business goals.
Core Features & Use Cases
- Task-model fit evaluation: guides whether a task should be solved with an LLM or with conventional code.
- Pipeline design patterns: canonical stages Acquire → Prepare → Process → Parse → Render with deterministic, idempotent steps.
- File-system as state: use a simple filesystem-based state machine to track progress and enable easy debugging.
- Cost and iteration: guidance for cost estimation, testing, and agent-assisted development to accelerate delivery.
- Reference-driven development: leverage script and reference artifacts to accelerate prototyping across projects.
Quick Start
Start by validating task-model fit with a manual prototype: run a representative example through the target model to confirm readiness. Then set up a minimal 5-stage pipeline and implement each stage as a separate, idempotent step with persistent intermediate outputs. Iterate by adding complexity only after validating the basics; use the provided scripts and references to bootstrap your project.
