tokalator-context-management

Estimate token budgets and preview next-turn costs via Tokalator MCP.

17|1|Updated Feb 7, 2026
One-click install
npx skills add https://github.com/vfaraji89/tokalator --skill tokalator-context-management
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: tokalator-context-management
Source: https://github.com/vfaraji89/tokalator/tree/main/skills/context-management
Command: npx skills add https://github.com/vfaraji89/tokalator --skill tokalator-context-management

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Many coding sessions with AI assistants suffer from hidden token consumption and context overflow, causing degraded responses and wasted time; this Skill gives real-time visibility and actionable warnings so developers can avoid context rot and expensive turns.

Core Features & Use Cases

  • Accurate token counting: Uses Claude BPE tokenizers via the Tokalator MCP to count tokens for texts and files before loading them into prompts.
  • Session budget estimation and turn previews: Estimate total file + conversation usage, preview the cost of the next turn, and receive warnings at configurable thresholds.
  • Model awareness and guidance: List supported models and their context windows, apply per-model rot thresholds, and recommend compaction or new sessions when limits approach.
  • Use Case: Before a refactor or large code review, run an estimate_budget on the target files and preview_turn to decide whether to summarize, compact, or start a fresh session.

Quick Start

Run an estimate_budget on the files you plan to work on with the active model to get an immediate session token baseline.

Frequently Asked Questions about tokalator-context-management

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

FAQPage Schema
How do I estimate token usage for files before loading them into an AI coding session?

You can estimate token usage for files by running the estimate_budget tool on target files with the active model to get an immediate session token baseline. This uses Claude BPE tokenizers via the Tokalator MCP server to calculate precise counts before prompt assembly.

Why does my AI assistant lose context quality during long coding sessions?

AI assistants lose context quality during long coding sessions due to hidden token consumption and context overflow. Real-time token budget awareness provides visibility and actionable warnings so you can avoid context rot and expensive turns.

Can I preview the token cost of the next turn before sending it to the model?

Yes, you can preview the token cost of the next turn using the preview_turn tool. This allows you to decide whether to summarize, compact, or start a fresh session before sending large prompts to the AI model.

How do I check model context windows and apply rot thresholds during editing?

You can check model context windows and apply per-model rot thresholds using the list_models tool. It lists supported models and their context limits, recommending compaction or new sessions when thresholds are approached.

Do I need a running MCP server to count tokens for codebase files?

Yes, you need a running Tokalator MCP server to count tokens for codebase files. The Skill exposes count_tokens, estimate_budget, preview_turn, and list_models tools through this server for precise token calculations.