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cost-aware-llm-pipeline

Route LLM API tasks by complexity with budget tracking and retry logic.

Updated Mar 26, 2026
Search Tags:#retry logic#model routing#budget tracking#prompt caching#llm pipeline#llm cost optimization#api cost control
One-click install
npx skills add https://github.com/luongldptit/move-ticket --skill cost-aware-llm-pipeline-luongldptit
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: cost-aware-llm-pipeline
Source: https://github.com/luongldptit/move-ticket/tree/main/.agent/skills/cost-aware-llm-pipeline
Command: npx skills add https://github.com/luongldptit/move-ticket --skill cost-aware-llm-pipeline-luongldptit

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill eliminates the risk of unexpected high LLM API bills when building applications that call LLM APIs, especially for batch processing tasks with varying input complexity, by providing built-in cost guardrails and optimization patterns.

Core Features & Use Cases

  • Intelligent Model Routing: Automatically selects low-cost models for simple tasks and reserves expensive, high-capability models for complex tasks to reduce per-request spend.
  • Immutable Budget Tracking: Tracks cumulative API costs with non-mutating records to enforce pre-set budget limits and prevent overspending on batch jobs.
  • Targeted Retry Logic: Retries only on transient errors (network issues, rate limits, server errors) to avoid wasting budget on permanent failures like authentication or bad request errors.
  • Prompt Caching: Caches long, repetitive system prompts to cut down on redundant token costs and reduce latency for repeated requests. Use case: A team running a batch customer support ticket classification pipeline can use this Skill to route simple sentiment analysis tasks to a low-cost model, use a more powerful model only for complex intent detection, and halt processing entirely if the API spend hits the monthly budget cap.

Quick Start

Use the cost-aware-llm-pipeline skill to add budget guardrails and automatic model routing to your LLM API batch processing workflow.

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 cut LLM API costs for batch processing without losing output quality?▼

To cut LLM API costs, you can use intelligent model routing to send simple tasks to low-cost models and reserve expensive models for complex inputs. This reduces per-request spend while maintaining output quality.

What is the best way to enforce a hard budget limit on an LLM pipeline?▼

The best way to enforce a hard budget limit on an LLM pipeline is by using immutable budget tracking. This method tracks cumulative API costs with non-mutating records to halt processing entirely when a pre-set spend cap is reached.

Why does retrying failed LLM API requests waste my budget?▼

Retrying failed LLM API requests wastes budget when you blindly retry permanent failures like authentication or bad request errors. Targeted retry logic avoids this by retrying only transient errors like network issues, rate limits, and server errors.

Can I reduce token costs for repetitive system prompts in my LLM application?▼

Yes, you can reduce token costs for repetitive system prompts by using prompt caching. Prompt caching stores long, repetitive system prompts to cut down on redundant token expenditure and reduce latency for repeated requests.

Does model routing by task complexity work for variable input workloads?▼

Yes, model routing by task complexity works effectively for variable input workloads. It automatically selects the appropriate model based on input difficulty, ensuring simple batch tasks use low-cost APIs while complex tasks get high-capability models.

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