🚨 token-slayer

Stop wasting 70–80% of your Claude/GPT context window.

If you’re building in crypto, writing Solidity, auditing protocols, or grinding monorepos — you’re probably burning most of your tokens before your model even does useful work.

This repo is a collection of real-world patterns + tools to reduce hidden token waste in AI-assisted development.

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🧠 The Problem

Most token waste doesn’t come from prompts.

It comes from invisible layers:

• Claude.md bloat
• history re-reads
• plugin injection
• tool schema overhead
• unnecessary context loading
• logs and terminal noise
• over-generation loops

Result:

👉 73–80% of tokens are spent before actual reasoning begins.

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⚖️ Reality Check

Claude is powerful for large context (200k–1M tokens),
but uses more tokens per prompt (~2× vs GPT in many cases).

So efficiency = everything.

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🛠️ Tools (Real Workflow Stack)

This repo documents + organizes tools that reduce token waste:

Output reduction

• caveman claude — reduces verbose outputs (~75%)

Terminal / logs filtering

• rtk (rust token killer) — filters terminal output (~60–90%)

Code-level efficiency

• code review graph — focuses only on relevant code (~49× reduction)

Context isolation

• context mode — stores output externally (sqlite), avoids context pollution (~98%)

Prompt optimization

• claude token optimizer — reduces full-project prompt cost (~90%)

Hidden waste detection

• token optimizer — finds invisible token leakage

MCP optimization

• token optimizer mcp — caching + compression (~95%+)

Codebase search efficiency

• claude context — vector search across repo (~40% reduction)

Strict formatting control

• claude token efficient — enforces minimal context usage

Navigation optimization

• token savior — symbol-based navigation (~97% reduction)

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🧠 Suggested Stack (by use case)

• Large monorepos → code review graph + token savior
• Heavy logs → rtk
• MCP-heavy workflows → context mode
• Fast wins → caveman + token efficient

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⚡ Real Impact Example

A 50k-line Solidity monorepo audit:

• before: context exhausted before useful output
• after: ~49× reduction using structured context filtering

Large logs:

• 300k tokens → <10k after filtering

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🎯 Goal

Not to “use AI more”

But to:

• use fewer tokens
• get faster outputs
• reduce API cost
• increase signal-to-noise ratio

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📉 Summary

Most devs are not limited by AI quality.

They are limited by context inefficiency.

Fix that → everything improves.

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🍒 Contact

https://x.com/withmewtwo

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💎 License

MIT