rescue-tokens

Detects token waste symptoms and applies immediate context and model optimizations.

Updated Aug 16, 2026
One-click install
npx skills add https://github.com/three1324/yeonjinautomotive --skill rescue-tokens-three1324
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: rescue-tokens
Source: https://github.com/three1324/yeonjinautomotive/tree/main/.claude/skills/rescue-tokens
Command: npx skills add https://github.com/three1324/yeonjinautomotive --skill rescue-tokens-three1324

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Token exhaustion often appears as sudden rate limit errors after long conversations, wrong model choices, bloated context, or expensive file reads silently consume quota. This Skill detects those warning signs early and acts immediately instead of wasting tokens on lengthy explanations. ## Core Features & Use Cases - Emergency Red Flag Detection: Auto-activates when any of seven triggers appear, including rate limit warnings, context over 40%, conversations over 90 minutes, 5+ MCP plugins, or Opus used for simple tasks. - Action Matrix: Maps each symptom to a concrete fix such as compacting context, starting a fresh conversation with a 3-sentence handoff, switching from Opus to Sonnet, or disabling unused MCP plugins. - Terse Response Discipline: Under rate limit pressure, responses shrink to under 100 words with no markdown sections, reasoning blocks, or permission requests. - Use Case: You say "don't lose context" while attaching a 40-page PDF near your quota limit. The Skill asks for the relevant pages instead of reading the whole file, compacts context, and continues in terse plain sentences. ## Quick Start Tell the assistant you are hitting rate limit warnings or that context is getting long, and it will compact, switch models, or start a fresh conversation automatically.

Frequently Asked Questions about rescue-tokens

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

FAQPage Schema
How do I reduce token usage in Claude Code conversations?

Compact context when it passes 40%, start a fresh conversation with a short handoff summary past 70%, and match the model to the task. Use Sonnet for implementation work and reserve Opus for architecture and planning.

What causes unexpected rate limit errors in Claude Code?

Rate limits usually result from accumulated token waste: eternal conversations, verbose output, wrong model choice, MCP plugin bloat, and reading large PDFs or images in full. Plans count messages, but tokens are the real cost driver.

When should I use Sonnet instead of Opus?

Use Sonnet for all implementation, refactoring, and debugging tasks, since it excels at pattern-based coding work. Reserve Opus for architecture and deep planning only, as a long Opus conversation can cost as much as 50 short Sonnet ones.

Should I use sub-agents to save context tokens?

Use sub-agents only for isolated, disposable tasks like independent research. Never use them for work with shared context dependencies such as migrations or refactors, because duplicating shared context multiplies token cost roughly fivefold.

How do I handle large PDF files without hitting token limits?

Ask for a text conversion or specific page ranges instead of reading the entire document. Reading a full PDF can cost 10 to 50 times more tokens than extracting only the relevant excerpts.