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agentsop-cost-tiered-models

Split LLM pipeline calls into strong decision and cheap execution tiers.

287|16|Updated May 20, 2026
Search Tags:#workflow design#agent orchestration#model routing#speculative decoding#llm cost optimization#tiered prompting#escalation valve
One-click install
npx skills add https://github.com/agentsope/SkillAlchemy --skill agentsop-cost-tiered-models
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: agentsop-cost-tiered-models
Source: https://github.com/agentsope/SkillAlchemy/tree/main/skills/agentsop-cost-tiered-models
Command: npx skills add https://github.com/agentsope/SkillAlchemy --skill agentsop-cost-tiered-models

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

It helps you design multi-call LLM pipelines so only the few high-cognitive-load steps use a strong (expensive) model, while the many mechanical steps use a cheap (fast) model—preserving quality with far lower total cost.

Core Features & Use Cases

  • Cost-aware tiering by cognitive demand: assigns each LM call to a Tier-S (decide) or Tier-E (execute) role based on whether it needs planning/routing/judgment versus formatting/rewrite/extraction/application.
  • Escalation valve for long-tail failures: detects Tier-E degradation (e.g., formatting/schema/test failures) and upgrades only the failing step back to Tier-S.
  • Cross-framework operational SOP: provides the same “strong reasoner + cheap executor” shape across Aider (architect+editor), DSPy (optimizer-LM vs task-LM), LangGraph (supervisor+worker), and vLLM (speculative draft+target), plus anti-patterns and boundaries.
  • Use case examples: coding/agent pipelines, automated refactoring/edit workflows, document transformation chains, and multi-agent orchestrations where most calls are mechanical post-processing.

Quick Start

Ask the system to redesign your multi-step LLM workflow by listing all LM calls, tagging each as “thinking” or “doing,” mapping them to Tier-S vs Tier-E, and adding a step-level escalation rule for when the cheap executor fails.

Frequently Asked Questions about agentsop-cost-tiered-models

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

FAQPage Schema
How do I reduce LLM costs in multi-call agent pipelines?▼

Reduce LLM costs by splitting cognitive-demand roles: route high-load planning and routing steps to a strong decision model, while assigning mechanical formatting and extraction to a cheap execution model.

What is tiered prompting for LLM cost optimization?▼

Tiered prompting assigns each LLM call to a Tier-S decide role or a Tier-E execute role based on cognitive demand, ensuring only critical steps use expensive models to lower total workflow cost.

How do I handle cheap executor model failures in agentic workflows?▼

Implement an escalation valve that detects Tier-E degradation through formatting or schema test failures, upgrading only the failing step back to the strong Tier-S model to restore quality.

Does this multi-model routing approach work with LangGraph and Aider?▼

Yes, the cross-framework operational SOP applies the strong reasoner and cheap executor pattern across Aider, DSPy, LangGraph, and vLLM, providing consistent model routing and anti-pattern boundaries.

When should I not use tiered model routing for document transformation?▼

Avoid tiered model routing when workflow steps lack clear boundaries between mechanical formatting and complex judgment, as ambiguous tasks prevent reliable Tier-S and Tier-E separation.

How do I start redesigning an LLM workflow for cost-aware tiering?▼

List all LLM calls, tag each as thinking or doing, map them to Tier-S or Tier-E based on cognitive demand, and define step-level escalation rules for cheap executor failures.

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