context-mode

Intercept tool calls to save large outputs and retrieve relevant data.

19.7k|1.4k|Updated Feb 23, 2026
One-click install
npx skills add https://github.com/mksglu/context-mode --skill context-mode
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: context-mode
Source: https://github.com/mksglu/context-mode/tree/main/skills/context-mode
Command: npx skills add https://github.com/mksglu/context-mode --skill context-mode

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill addresses the challenge of context window limitations in AI agents by intelligently managing large outputs and ensuring session continuity, preventing information loss and improving agent efficiency.

Core Features & Use Cases

  • Context Saving: Reduces the size of large tool outputs (e.g., Playwright snapshots, logs) before they enter the context window, saving significant space.
  • Session Continuity: Tracks all agent actions and decisions in a database, allowing the agent to resume tasks precisely where they left off even after context compaction.
  • Use Case: When an agent needs to analyze a large log file or process the output of a complex command, context-mode tools prevent the entire output from flooding the context, allowing the agent to focus on relevant information and maintain its train of thought.

Quick Start

Use context-mode tools like ctx_execute or ctx_execute_file when processing large outputs to save context.

Frequently Asked Questions about context-mode

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

FAQPage Schema
How do I manage large outputs and prevent context window flooding in AI agents?

To manage large outputs in AI agents, you can intercept tool calls to save data to files and retrieve only relevant information via search, drastically reducing context window consumption. This prevents large outputs from flooding the context.

How do I maintain AI agent session continuity after context compaction?

To maintain session continuity after context compaction, you can track all agent actions and decisions in a database. This allows the agent to resume tasks precisely where it left off without losing its state.

What is the best way to process large log files without exceeding context limits?

The best way to process large log files without exceeding limits is using context management tools that save output to files and index it server-side. The agent then retrieves only the relevant information via search.

Can I use context management tools to handle Playwright snapshots in AI agents?

Yes, you can use context management tools to handle Playwright snapshots. They reduce the size of large tool outputs before they enter the context window, saving significant space and allowing the agent to focus.

Do I need a database to preserve agent state across interactions?

Yes, you need a database to preserve agent state across interactions. Tracking all agent actions and decisions in a database ensures the agent can resume tasks precisely where it left off after context compaction.

Why does my AI agent lose its train of thought when analyzing complex command outputs?

Your AI agent loses its train of thought because large outputs flood the context window. Interception tools prevent this by saving data to files and indexing it server-side, allowing the agent to focus on relevant information.