What problem does it solve? Teams waste days building LLM automation on tasks that are fundamentally mismatched to language models, or over-engineer agent architectures that constrain model performance. This Skill provides a methodology for evaluating task-model fit, structuring staged pipelines, and estimating costs before writing production code. ## Core Features & Use Cases - Task-Model Fit Evaluation: Decision tables and a manual prototype step to validate whether a task suits LLM processing before investing in automation. - Staged Pipeline Architecture: A canonical acquire-prepare-process-parse-render structure with file-system state management, idempotent stages, and a runnable Python template script. - Cost and Architecture Guidance: Token cost estimation formulas, single vs multi-agent selection criteria, and case studies (Karpathy HN Time Capsule, Vercel d0, Manus) showing architectural reduction patterns. - Use Case: When asked to build a batch pipeline that grades 900 discussion threads with an LLM, use this Skill to validate fit with a manual prototype, scaffold the five-stage pipeline from the template script, and estimate token costs before execution. ## Quick Start Ask the agent to help design a batch processing pipeline for your task, starting with a manual prototype validation and a cost estimate using the pipeline template script.
