How does this skill help manage LLM costs?

It provides a specific estimation formula—calculating items, tokens per item, and API price—to predict costs and identifies strategies like context truncation to stay within budget.

When should I choose multi-agent over single-agent architecture?

Use multi-agent architectures when you need context isolation to prevent degradation on long tasks or when parallel exploration of different aspects improves quality; otherwise, keep it simple with a single-agent pipeline.

What is the recommended pipeline structure?

The skill advocates for a five-stage canonical structure: Acquire (fetch data), Prepare (format prompt), Process (LLM execution), Parse (extract data), and Render (generate final output).

Why does it recommend a file system over a database for state?

Using the file system as a state machine provides natural idempotency, makes debugging human-readable, simplifies parallelization, and offers trivial caching across runs.

LLM Project Architect

Name: LLM Project Architect
Author: EricGrill

byEricGrill

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データサイエンスとML

Architects and implements robust LLM-powered projects from initial ideation through deployment using agentic methodologies.

概要

This skill provides a structured framework for designing, building, and scaling LLM-driven applications. It guides developers through critical decision-making processes, such as determining task-model fit, implementing idempotent file-based state machines, and designing reliable multi-stage pipelines (acquire, prepare, process, parse, render). By emphasizing manual prototyping and architectural reduction, it helps teams build maintainable agentic systems that optimize for both cost and performance.

主な機能

Standardized multi-stage pipeline architecture for batch and interactive processing
Comprehensive cost and scale estimation frameworks for API-heavy projects
2 GitHub stars
File system state machine patterns for idempotency and easy debugging
Task-model fit evaluation to identify LLM vs. traditional code suitability
Structured output design strategies to improve parsing reliability

ユースケース

Refactoring complex LLM prompts into manageable, multi-stage architectures
Evaluating and prototyping new agent-based research or automation tools
Building high-volume batch processing pipelines for unstructured data

概要

主な機能

Standardized multi-stage pipeline architecture for batch and interactive processing
Comprehensive cost and scale estimation frameworks for API-heavy projects
2 GitHub stars
File system state machine patterns for idempotency and easy debugging
Task-model fit evaluation to identify LLM vs. traditional code suitability
Structured output design strategies to improve parsing reliability

ユースケース

Refactoring complex LLM prompts into manageable, multi-stage architectures
Evaluating and prototyping new agent-based research or automation tools
Building high-volume batch processing pipelines for unstructured data