check-context

Monitor context window usage, token consumption, and session costs from statusLine JSON.

1.1k|123|Updated Feb 2, 2026
One-click install
npx skills add https://github.com/zylos-ai/zylos-core --skill check-context
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: check-context
Source: https://github.com/zylos-ai/zylos-core/tree/main/skills/check-context
Command: npx skills add https://github.com/zylos-ai/zylos-core --skill check-context

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides clear visibility into the AI's current context window usage and token consumption, helping to manage resources and understand operational costs.

Core Features & Use Cases

  • Context Monitoring: Tracks the percentage of the context window used and remaining.
  • Token Count: Reports input and output tokens used, along with the total window size.
  • Cost Tracking: Displays the estimated session cost in USD.
  • Use Case: Before initiating a long-running task, you can use this skill to check if the current context is sufficient, preventing potential truncation or errors due to exceeding token limits.

Quick Start

Check the current context window usage and token consumption.

Frequently Asked Questions about check-context

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

FAQPage Schema
How do I check LLM context window usage and token consumption?

Check AI context window usage by parsing the statusLine JSON file to report input and output tokens, total window size, and remaining context capacity for real-time resource monitoring.

What is the best way to track session costs and token usage during extended AI interactions?

Track session costs and token usage by monitoring real-time metrics that display estimated USD costs alongside input and output token consumption throughout extended LLM interactions.

How do I prevent context truncation before initiating a long-running LLM task?

Prevent context truncation by checking if the current context window is sufficient before starting a long-running task, ensuring the remaining token capacity can handle the extended interaction.

Does context monitoring work with real-time statusLine JSON metrics?

Context monitoring works by parsing the statusLine JSON file to extract real-time metrics for context window usage, token consumption, and session costs without requiring external dependencies.

Can I estimate operational costs for LLM resource allocation?

Estimate operational costs for LLM resource allocation by tracking the estimated session cost in USD, helping you manage resources and understand financial expenditures during extended AI operations.

What are the limitations of monitoring AI context window usage?

Context monitoring limitations include relying entirely on parsing the statusLine JSON file for metrics, meaning it cannot track historical usage or predict future token consumption beyond the current session's real-time data.