cost-aware-llm-pipeline

Route LLM API requests by task complexity and track budget spend.

Updated Jul 4, 2026

Search Tags:#API #LLM #cost optimization #budget tracking #prompt caching

Ready to run

npx skills add https://github.com/Long-NguyenHai/warehouse-ai --skill cost-aware-llm-pipeline-long-nguyenhai

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: cost-aware-llm-pipeline
Source: https://github.com/Long-NguyenHai/warehouse-ai/tree/main/.agents/skills/cost-aware-llm-pipeline
Command: npx skills add https://github.com/Long-NguyenHai/warehouse-ai --skill cost-aware-llm-pipeline-long-nguyenhai

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill addresses the challenge of optimizing costs when using LLM APIs by implementing model routing, budget tracking, retry logic, and prompt caching.

Core Features & Use Cases

Model Routing by Task Complexity: Automatically select cheaper models for simple tasks, reserving expensive models for complex ones.
Immutable Cost Tracking: Track cumulative spend with frozen dataclasses, ensuring state does not mutate.
Narrow Retry Logic: Retry only on transient errors, failing fast on permanent issues.
Prompt Caching: Cache long system prompts to avoid resending them on every request.
Use Case: Ideal for building applications that call LLM APIs (Claude, GPT, etc.) and need to control API spend without compromising on quality.

Quick Start

Use the cost-aware-llm-pipeline skill to optimize the cost of LLM API calls in your application.

Frequently Asked Questions about cost-aware-llm-pipeline

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I reduce LLM API costs when routing tasks of varying complexity?▼

To reduce LLM API costs, implement model routing to select cheaper models for simple tasks and reserve expensive models for complex ones. This approach optimizes overall spend without compromising quality.

What is the best way to track cumulative LLM API spend without state mutation issues?▼

The best way to track cumulative LLM API spend is by using immutable cost tracking with frozen dataclasses. This ensures the spend state does not mutate, providing reliable and accurate budget monitoring.

How does prompt caching help avoid resending long system prompts on every LLM API request?▼

Prompt caching helps avoid resending long system prompts by storing them locally. This means the cached prompts are reused across subsequent LLM API requests, reducing token processing volume and lowering costs.

Can I use narrow retry logic to handle transient LLM API errors and fail fast on permanent issues?▼

Yes, you can use narrow retry logic to handle transient LLM API errors. It retries only on transient failures and fails fast on permanent issues, ensuring robust error recovery and preventing wasted API spend.

Does this approach to LLM cost optimization suit applications with strict budget constraints?▼

Yes, this approach to LLM cost optimization is ideal for applications with strict budget constraints. It combines model routing, immutable budget tracking, and prompt caching to robustly control API spend across varying task complexities.