What problem does it solve?
Projects that intend to use LLMs often fail because teams misunderstand task-model fit, bake in non-idempotent architectures, or underestimate token costs and parsing fragility; this Skill helps teams evaluate, plan, and structure LLM-powered projects to avoid those mistakes and iterate rapidly.
Core Features & Use Cases
- Task-Model Fit Evaluation: Practical checklists and a manual-prototype step to decide whether to use LLMs, traditional code, or agents.
- Staged Pipeline Patterns: Canonical acquire → prepare → process → parse → render architecture with guidance on idempotency, caching, and file-system state as the source of truth.
- Agent & Multi-Agent Guidance: When to use single-agent vs multi-agent designs, architectural reduction patterns, and sub-agent isolation strategies.
- Parsing, Validation & Costing: Prompt formats for structured output, robust parsing strategies with graceful degradation, and simple token-cost estimation formulas for batch workloads.
- Use Cases: Batch content analysis, text-to-structured-data pipelines, interactive agent applications, and research orchestration with parallel sub-agents.
Quick Start
Run a manual prototype: copy one representative input into the model, verify output format and quality, then scaffold an acquire/prepare/process/parse/render directory for that item.
