tokalator-context-management

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.