What problem does it solve?
This skill provides a practical methodology for identifying tasks that are well-suited to LLM processing, designing end-to-end project architectures, and iterating rapidly from ideation to deployment.
Core Features & Use Cases
- Task-model fit assessment: Quickly validate whether a task benefits from LLM automation through manual prototyping and architecture sketching.
- Canonical pipeline design: Guidance on the Acquire → Prepare → Process → Parse → Render stages and how to implement each deterministically.
- File-system as state: Use a simple, transparent on-disk state to debug, cache, and reproduce results.
- Structured output and parsing: Emphasize parseable outputs with explicit format requirements to ensure robust downstream processing.
- Cost-aware iteration: Start small, measure token use, and scale responsibly with clear gates for expansion.
Quick Start
Copy this skill's content into your Claude environment and begin by outlining a representative task. Apply the canonical pipeline (acquire → prepare → process → parse → render) to create a minimal, end-to-end example, then incrementally expand stages and add parsing rules as needed.
