cost-tracking

Name: cost-tracking
Availability: InStock
Author: bennybennison

Track token usage and estimate costs for conversational AI sessions.

Updated Apr 11, 2026

Search Tags:#budgeting #prompt-caching #model-routing #cost-tracking #token-usage #context-compaction #session-monitoring

Ready to run

npx skills add https://github.com/bennybennison/agent-toolkit --skill cost-tracking-bennybennison

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: cost-tracking
Source: https://github.com/bennybennison/agent-toolkit/tree/main/skills/cost-tracking
Command: npx skills add https://github.com/bennybennison/agent-toolkit --skill cost-tracking-bennybennison

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps developers and agent operators track token consumption and estimate monetary cost for conversational AI sessions so they can choose appropriate models, manage context size, and avoid budget overruns.

Core Features & Use Cases

Per-session tracking: Maintain rough token counts and cost estimates for ongoing conversations and tasks.
Model-aware routing: Recommend the cheapest model tier capable of the task and surface relative cost multipliers to guide selection.
Context management: Recommend when to compact context, spawn subagents, or stop and re-evaluate to prevent excessive token use.
Use Case: During extended research, identify when to compact context, switch to a lower-cost model for routine checks, or run parallel subagents for independent exploration.

Quick Start

Ask the assistant to report current session token usage, estimated cost, and recommend a lower-cost model if appropriate.

Frequently Asked Questions about cost-tracking

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

FAQPage Schema

How do I track token usage and estimate costs for conversational AI sessions?▼

Token usage tracking for conversational AI sessions is done by maintaining rough per-session token counts and applying cost multipliers by model tier to estimate monetary expense. It helps developers manage context size and avoid budget overruns.

When should I compact context or spawn subagents to prevent excessive token use?▼

Context compaction or subagent spawning should be triggered when extended conversations risk excessive token use. The system recommends these actions to manage context size, run parallel exploration, or stop and re-evaluate the workflow.

Can I set fail-fast thresholds for budget overruns during agent sessions?▼

Fail-fast thresholds for budget overruns can be applied to developer workflows and agent sessions. This prevents excessive token consumption by stopping operations when predefined token budget limits are reached.

What's the best way to manage token budgets across different model tiers?▼

Managing token budgets across model tiers involves tracking per-session token counts, applying cost multipliers for each tier, and utilizing context compaction recommendations. This ensures you choose appropriate models and avoid budget overruns.