interstat

Benchmark token efficiency of agent workflows by analyzing prompt and model interactions.

3|1|Updated Feb 15, 2026

Search Tags:#token efficiency #workflow analysis #cost reduction #llm optimization #agent benchmarking

Ready to run

npx skills add https://github.com/mistakeknot/Demarch --skill interstat

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: interstat
Source: https://github.com/mistakeknot/Demarch/tree/main/.gemini/generated-skills/interstat
Command: npx skills add https://github.com/mistakeknot/Demarch --skill interstat

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill addresses the challenge of optimizing agent workflows by measuring and improving token efficiency, reducing operational costs and speeding up processing.

Core Features & Use Cases

Token Usage Analysis: Benchmarks the token consumption of different agent configurations and prompts.
Efficiency Reporting: Generates reports highlighting areas for token optimization.
Use Case: Developers can use this Skill to compare the token efficiency of two different prompt strategies for a summarization task, identifying which one provides better results for fewer tokens.

Quick Start

Benchmark the token efficiency of the current agent configuration.

Frequently Asked Questions about interstat

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

FAQPage Schema

How do I benchmark token efficiency for LLM agent workflows?▼

You can benchmark token efficiency by analyzing prompt and model interactions with detailed logging of token usage, then comparing multiple execution paths to identify the most cost-effective configuration.

What is the best way to reduce LLM operational costs in conversational AI?▼

To reduce LLM operational costs, analyze token consumption across different agent configurations and prompts to identify which strategies provide better results for fewer tokens.

Can I compare two different prompt strategies for token usage?▼

Yes, you can compare prompt strategies by logging token usage for each execution path and generating an efficiency report that highlights areas for token optimization.

Do I need detailed logging to analyze agent token consumption?▼

Yes, detailed logging of token usage is required to perform comparative analysis of multiple execution paths and generate accurate efficiency reports for your workflows.

How does workflow analysis help speed up LLM processing times?▼

Workflow analysis speeds up processing by measuring token consumption across agent configurations, allowing you to select optimized execution paths that reduce response times.

What limitations exist when benchmarking agent token optimization?▼

Benchmarking agent token optimization is limited to analyzing prompt and model interactions where detailed token usage logs are available, requiring comparative data from multiple execution paths.