When should I use the Project Development skill?

Activate this skill when starting a new LLM-based project, evaluating if a task is right for an AI agent, or designing complex multi-stage processing pipelines.

What is the benefit of the 'File System as State Machine' approach?

It provides natural idempotency, easy debugging, and simple parallelization by using directories and files to track the progress of your LLM pipeline stages.

How does this skill help with cost management?

It provides frameworks for estimating token usage, input/output costs, and API overhead, allowing you to project expenses before scaling your application.

Why is the 'Manual Prototype' step emphasized?

Validating a single representative input manually ensures the model is capable of the task, preventing hours of wasted development on unfeasible automation.

AI Project Development

Name: AI Project Development
Author: goodnight000

bygoodnight000

데이터 과학 및 ML

Architects and manages the end-to-end lifecycle of LLM-powered applications using agentic development methodologies.

소개

This skill provides a comprehensive methodology for designing and building LLM-integrated projects, from initial task evaluation to production deployment. It guides developers through identifying LLM-suited tasks, building robust staged pipelines, implementing file-based state management, and optimizing for both cost and performance. By emphasizing manual prototyping and architectural reduction, it helps teams avoid common AI development pitfalls while accelerating the delivery of sophisticated multi-agent or batch processing systems.

주요 기능

Staged Pipeline Architecture
Structured Output Parsing
Task-Model Fit Evaluation
File-System State Management
Cost and Scale Estimation
0 GitHub stars

사용 사례

Designing a batch processing pipeline for large-scale data analysis
Deciding between single-agent and multi-agent architectures for complex research tasks
Transitioning a manual prototype into an automated production-ready AI application

소개

주요 기능

Staged Pipeline Architecture
Structured Output Parsing
Task-Model Fit Evaluation
File-System State Management
Cost and Scale Estimation
0 GitHub stars

사용 사례

Designing a batch processing pipeline for large-scale data analysis
Deciding between single-agent and multi-agent architectures for complex research tasks
Transitioning a manual prototype into an automated production-ready AI application