Agent skill · marketing growth · erichowens
llm-router
Selects the optimal LLM model and provider for each task based on complexity, cost budget, and capability requirements. Routes cheap tasks to Haiku/GPT-4o-mini and complex tasks to Sonnet/Opus/o1.
Why this skill is useful
Adds a decision-making framework for model selection that optimizes costs and performance across multiple LLMs.
What it needs
About 4k tokens when loaded. Last updated 2026-07-14. 180 stars on the source repository.
What this skill does
LLM Router Selects the optimal LLM model for each task. The single biggest cost lever in multi-agent systems — intelligent routing saves 45-85% while maintaining 95%+ of top-model quality. --- When to Use ✅ Use for: Deciding which model to call for a specific task Assigning models to DAG nodes in agent workflows Optimizing LLM API costs across a system Building cascading try-cheap-first patterns ❌ NOT for: Prompt engineering (use prompt-engineer) Model fine-tuning or training Comparing model architectures (academic research) --- Routing Decision Tree --- Tier Assignment Table Task Type Tier Models Cost/Call Why This Tier ----------- ------ -------- ----------- --------------- Classify input type 1 Haiku, GPT-4o-mini ~$0.001 Deterministic categorization Validate schema/format 1 Haiku, GPT-4o-mini ~$0.001 Mechanical checking Format output / template 1 Haiku, GPT-4o-mini ~$0.001 Structured transformation Extract structured data 1 Haiku, GPT-4o-mini ~$0.001 Pattern matching Summarize text 1-2 Haiku → Sonnet ~$0.001-0.01 Short summaries: Haiku; nuanced: Sonnet Write content/docs 2 Sonnet, GPT-4o ~$0.01 Creative quality matters Implement code 2 Sonnet, GPT-4o ~$0.01 Correctness + style Review code/diffs 2 Sonnet, GPT-4o ~$0.01 Needs judgment, not just pattern matching Research synthesis 2 Sonnet, GPT-4o ~$0.01 Multi-source reasoning Decompose ambiguous problem 3 Opus, o1 ~$0.10 Requires deep understanding Design architecture 3 Opus, o1 ~$0.10 Complex system reasoning Judge output quality 3 Opus, o1 ~$0.10 Meta-reasoning about quality Plan multi-step strategy 3 Opus, o1 ~$0.10 Long-horizon planning --- Three Routing Strategies Strategy 1: Static Tier Assignment (Start Here) Assign model by task type at DAG design time. No runtime logic. Gets 60-70% of possible savings. Strategy 2: Cascading (Try Cheap First) Try the cheap model; if quality is below threshold, escalate. Adds ~1s latency but saves 50-80% on nodes where cheap succeeds. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills erichowens/llm-router