Token Optimizer

What Token Optimizer Does

Token Optimizer is a high-performance context-pruning skill designed specifically for agentic AI tools like Claude Code, Cursor, and Aider. It intercepts file reads, terminal commands, and tool calls in real time to eliminate unnecessary context bloat before it hits your LLM prompt.

The Problem It Solves

When autonomous AI coding agents read entire source files, run test suites, or re-verify edits, they rapidly inflate the context window. This creates three major issues:

• Exploding API Bills: Uncompressed sessions on frontier models can cost $2.00+ per task in re-read input tokens.
• Degraded Reasoning & Hallucinations: Flooding context with thousands of lines of passing test logs or unedited boilerplate degrades model focus and accuracy.
• Increased Latency: Larger prompt payloads mean slower time-to-first-token response times.

Key Features

• AST Code Skeletonization: Parses source files using Tree-sitter to return structural outlines (classes, method signatures, type definitions) without loading function bodies—saving 85–94% of file-reading tokens.
• Terminal & Test Log Distillation: Intercepts test runners (jest, pytest, cargo test, go test) to strip passing assertions and progress bars, returning only failure stack traces and error codes.
• Git Diff Deduplication: Automatically converts repeated file verification calls into compact git diff patches instead of re-passing full source code.
• Zero Added Latency: Performs all transformations locally via native code parsers, adding 0ms of delay to your AI response loop.
• Prompt Cache Optimization: Produces stable, deterministic context prefixes that maximize provider prompt caching discounts (up to 90% off cached input reads).

Proven Token Savings

Because LLMs re-process the entire conversation history (context window) on every turn, saving tokens compounds massively over time. We simulated typical development sessions (Read Code → Run Test → Edit → Run Test) to measure cumulative billed tokens.

Single-Turn Savings

• File Structure Read: 1,662 tokens → 157 tokens (90.6% Reduction)
• Single Function Read: 1,662 tokens → 544 tokens (67.3% Reduction)
• Terminal Execution: 745 tokens → 18 tokens (97.6% Reduction)

Cumulative Session Cost & Token Savings

Because LLMs re-process the entire context window on every single turn, saving tokens early compounds massively. We simulated 100-turn development sessions across three standard personas using an average LLM input cost of $3.00 per 1M tokens.

🚨 Without Token Optimizer, long sessions quickly breach the 200k token limit of models like Claude 3.5 Sonnet, causing memory crashes and extreme latency.

🕵️ The Explorer (Heavy Codebase Reading)

10 Turns $0.53 $0.05 32k → 3k tokens 89.2% 50 Turns $12.26 $1.34 160k → 17k tokens 88.9% 100 Turns $48.51 $5.35 320k → 35k tokens 88.9%

🐛 The Debugger (Heavy Test Running & Error Traces)

10 Turns $1.53 $0.09 88k → 5k tokens 93.9% 50 Turns $33.39 $2.11 431k → 27k tokens 93.6% 100 Turns $131.09 $8.35 857k → 55k tokens 93.6%

💻 The Developer (Balanced Edit/Diff/Test)

10 Turns $0.58 $0.06 43k → 4k tokens 89.3% 50 Turns $16.00 $1.52 218k → 20k tokens 90.4% 100 Turns $64.70 $6.07 436k → 40k tokens 90.6%

A standard LLM crashes at ~50 turns for a Debugger because the context hits 431,000 tokens. With Token Optimizer, the context stays at a lean 27,000 tokens, allowing infinite debugging loops!

Why You Should Buy / Use It

Whether you are an individual developer wanting to keep your monthly AI spending under control or an engineering lead managing a team on Claude Code, Token Optimizer delivers an immediate 10x+ ROI by saving $100–$400+ per developer/month in API costs while making your AI agent faster, cheaper, and smarter.