pipeline_core/skills_library/all/model-cost-optimization/SKILL.md

name	description	author	tags
model-cost-optimization	Best practices for reducing expensive model token usage	Hermes Agent	cost-optimization model-selection efficiency

Model Cost Optimization Guide

When to Use Cheap vs Expensive Models

Use Cheap Model For:

• Simple factual questions and basic information
• Short clarifications (under current thresholds)
• Basic tool parameter preparation
• Simple yes/no decisions
• Routing questions like "which skill should I use?"

Use Expensive Model For:

• Complex multi-step reasoning
• Code generation and debugging
• Long-form content creation
• Advanced planning and analysis
• Creative tasks requiring originality

User Query Optimization Tips

1. Break down complex requests into smaller, focused questions
2. Be specific and direct in your queries
3. Ask one thing at a time rather than multiple questions
4. Use clear, concise language to stay under character limits

Current System Behavior

The system automatically routes based on:

• Message character count
• Word count
• Current thresholds are set to maximize cheap model usage while maintaining quality

If you find responses from the cheap model are insufficient, you can:

• Rephrase your query to be more complex (triggering expensive model)
• Explicitly request detailed/complex analysis
• Ask follow-up questions that naturally exceed thresholds

Hermes Delegation Model Strategy

When using delegate_task for subagents, configure a cheaper/faster model to save cost without sacrificing quality on simple tasks.

Setup

hermes config set delegation.provider deepseek
hermes config set delegation.model deepseek-v4-flash

API key auto-resolves from .env (e.g. DEEPSEEK_API_KEY).

Decision Rules — Delegate vs Handle Yourself

Delegate to cheap model	Keep in main (expensive) model
Translation, formatting, boilerplate	Complex architecture design
Simple code snippets (≤1 function)	Multi-step debugging
File batch operations (search, rename, extract)	Tasks needing deep context understanding
Data extraction and parsing	Tasks requiring iterative back-and-forth
Mechanical, well-scoped tasks	Ambiguous tasks with unclear requirements

Litmus test: Can you describe the task in one sentence and does it need no iterative feedback? → delegate to cheap model.

Pitfalls

• Subagent self-reports are unreliable — verify file creation, HTTP responses, and test results yourself after the subagent finishes
• Don't delegate tasks needing user clarification — subagents can't use clarify
• Flash models are more verbose — they may use more tokens per task but are still cheaper overall due to lower per-token pricing
• Don't delegate when context is critical — subagents have no memory of your conversation, so pass all relevant file paths, error messages, and constraints explicitly

Monitoring Effectiveness

See references/deepseek-v4-benchmark.md for real-world speed/token benchmarks of DeepSeek models.

Pay attention to response quality. If simple tasks are getting inadequate responses, the thresholds may need adjustment through configuration changes.