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cost-aware-llm-pipeline

Routes LLM calls by task complexity to optimize cost and performance using BudgetTracker and caching.

4|Updated Dec 5, 2025
Search Tags:#routing#llm#budget#retry#batch-processing#cost#prompt-caching
One-click install
npx skills add https://github.com/aaaa47080/stock_agent --skill cost-aware-llm-pipeline-aaaa47080
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: cost-aware-llm-pipeline
Source: https://github.com/aaaa47080/stock_agent/tree/main/.opencode/skills/cost-aware-llm-pipeline
Command: npx skills add https://github.com/aaaa47080/stock_agent --skill cost-aware-llm-pipeline-aaaa47080

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Automates cost control for AI features by routing LLM calls based on task complexity and tracking spend across requests.

Core Features & Use Cases

  • Model routing by task complexity to balance cost and performance.
  • Budget tracking and enforcement to prevent overspending.
  • Retry logic and prompt caching to improve reliability and reduce API usage.
  • Batch processing support for efficient throughput in multi-task workflows.

Use Case: A feature that calls multiple LLMs for drafting responses, code generation, and data analysis can automatically select appropriate models, reuse prompts, and stay within budget.

Quick Start

Configure a daily budget and enable task-based model routing to automatically select a cost-appropriate LLM and cache prompts for reuse.

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 route LLM calls by task complexity to reduce API costs?▼

Route LLM calls by task complexity through explicit model constants to balance cost and performance. The routing mechanism selects appropriate models for drafting responses or code generation, ensuring cost-effective task execution.

How does budget tracking enforce limits across multiple LLM API requests?▼

Budget tracking enforces limits across multiple LLM API requests using a BudgetTracker class. It monitors spend across requests to prevent overspending, applying configured daily budgets to automatically control API expenditure.

Can I use prompt caching across batches for LLM batch processing?▼

Prompt caching across batches is supported for LLM batch processing to reuse prefixes. This caching strategy reduces API usage and improves throughput efficiency in multi-task workflows.

What is the best way to handle LLM API failures with retry logic?▼

Handle LLM API failures with retry logic using exponential backoff. This mechanism improves reliability by systematically retrying failed calls, ensuring transient errors do not disrupt automated workflows.

Does this cost-aware routing approach work with multiple LLM providers?▼

This cost-aware routing approach works with features calling multiple LLM providers. It automatically selects cost-appropriate models and applies prompt caching across different providers to stay within budget.

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