Should I use a single-agent or multi-agent approach?

Single-agents are best for independent batch tasks and simpler cost management. Multi-agent architectures are preferred when you need context isolation for specialized subtasks or when a task exceeds a single context window.

How do I know if my task is suitable for an LLM?

Evaluate tasks based on their need for synthesis, subjective judgment, and error tolerance. LLMs excel at combining information but are unsuited for precise math, real-time sub-second requirements, or 100% accuracy constraints.

How can I reduce parsing errors in LLM outputs?

Use explicit section markers and format examples in your prompts, and build robust parsers that use flexible regex patterns to handle minor formatting variations rather than strict JSON parsing.

What is the benefit of a file-system-based pipeline?

Using the file system as a state machine provides natural idempotency (skipping completed steps), human-readable debugging of every stage, and trivial caching of expensive LLM outputs.

LLM Project Development Methodology

Name: LLM Project Development Methodology
Author: guanyang

byguanyang

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生産性とワークフロー

Guides the architecture, evaluation, and implementation of LLM-powered applications using structured pipeline patterns and agent-assisted workflows.

This skill provides a comprehensive methodology for building robust LLM projects, from initial task evaluation to production-scale deployment. It guides users through identifying task-model fit, implementing idempotent pipeline architectures using the file system as a state machine, and choosing between single and multi-agent systems. By emphasizing manual prototyping and structured output design, it helps developers minimize hallucination risks, control API costs, and build maintainable AI workflows that scale effectively.

主な機能

01Cost and scale estimation frameworks for LLM-heavy projects

02Task-model fit evaluation to identify LLM-suitable problems

03442 GitHub stars

04Structured output design patterns for reliable programmatic parsing

05Standardized pipeline architecture (Acquire, Prepare, Process, Parse, Render)

06File-system-based state management for idempotent and cacheable workflows

ユースケース

01Transitioning a manual LLM prototyping workflow into an automated, production-ready system

02Designing a batch processing pipeline for large-scale text analysis or synthesis

03Evaluating whether to use a single-agent or multi-agent architecture for a complex task

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add guanyang/antigravity-skills project-development

For use in Claude.ai and ChatGPT

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