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cost-audit

Audit AI inference costs and optimize token usage across multi-model pipelines.

42|9|Updated Mar 16, 2026
Search Tags:#token-optimization#prompt-engineering#model-selection#cost-estimation#inference-costs#cost-audit#fleet-mode
One-click install
npx skills add https://github.com/drvoss/everything-copilot-cli --skill cost-audit
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: cost-audit
Source: https://github.com/drvoss/everything-copilot-cli/tree/main/skills/workflow/cost-audit
Command: npx skills add https://github.com/drvoss/everything-copilot-cli --skill cost-audit

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps teams identify and eliminate unnecessary AI inference costs by right-sizing model selection, pruning prompt context, and reducing redundant calls so spending stays predictable without sacrificing necessary capability.

Core Features & Use Cases

  • Identify high-cost call sites: Find long prompts, full-file contexts, or premium models used where lower tiers would suffice.
  • Measure baseline usage: Tally tokens per call, model mix, and prompt size distributions to quantify current spend patterns.
  • Recommend actionable optimizations: Model downgrades, context pruning, prompt deduplication, batching, and per-agent tier assignment to estimate savings.
  • Use Case: Audit a fleet-mode deployment where multiple agents default to premium models and produce a prioritized remediation plan with estimated monthly savings.

Quick Start

Run a cost audit on recent model call logs, list the top waste patterns, and recommend specific model tier changes and prompt reductions with estimated savings.

Frequently Asked Questions about cost-audit

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

FAQPage Schema
How do I reduce AI inference costs in a multi-model pipeline?▼

Reduce AI inference costs by auditing model call logs to identify high-cost call sites, pruning prompt context, and right-sizing model selection to eliminate unnecessary token usage without sacrificing capability.

What is the best way to optimize token usage across parallel fleet agents?▼

Optimize token usage across parallel fleet agents by measuring baseline usage distributions, assigning per-agent model tiers, and batching redundant calls to estimate and achieve monthly spend savings.

How do I find prompts or contexts causing high inference spend in production?▼

Find high-cost prompts by tallying token counts per call and analyzing model call logs to locate long prompts, full-file contexts, or premium models used where lower tiers would suffice.

Can I estimate cost differences before downgrading models in a pull request review workflow?▼

Estimate cost differences in pull request review workflows by simulating model tier downgrades, applying prompt deduplication, and calculating projected savings from context pruning on existing token counts.

What data do I need to perform an AI cost audit on my application?▼

To perform an AI cost audit, you need access to model call logs, token counts, prompt sizes, and the ability to simulate or estimate cost differences when changing model tiers or batching calls.

Why does my multi-model orchestration setup have unpredictable billing?▼

Multi-model orchestration billing becomes unpredictable when multiple agents default to premium models and use full-file contexts, causing unnecessary token usage that inflates spend without adding necessary capability.

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