tools to optimize AI usage: rtk, CodeGraph

How I Stopped Running out of Tokens · Daniela Baron

After hitting her organization's monthly spend limit while using Claude Code for daily engineering work, Daniela Baron established a setup to track, optimize, and drastically reduce her token consumption without sacrificing code quality or speed.

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1. Monitoring Token Usage

• Claude Desktop Usage Meter: Built-in settings window to keep an eye on official account usage limits.

• Claude Code Usage Monitor: A live CLI tool that sits alongside terminal sessions to estimate current burn rates, cost, and estimated time before limits are hit.

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2. Optimization Tools

• rtk (Rust Token Killer) — Saves Input Tokens: Acts as a proxy for CLI commands (like git log), compressing raw terminal output before it gets sent as input context to the model.

• Caveman — Saves Output Tokens: A Claude Code plugin that strips filler, pleasantries, and hedging from AI responses, enforcing concise fragments, bullet points, and front-loaded file/line references.

• CodeGraph — Saves Both: Maps the codebase into a local SQLite graph database so Claude can query relationships directly instead of grepping and reading large files.

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3. Key Habits & Settings

• Default to Sonnet with Opus as Advisor: Uses Sonnet for standard tasks and configures the /advisor feature to automatically delegate complex edge cases to Opus only when needed.

• Audit CLAUDE.md: Replaced broad, auto-loaded @ file imports in the project's config with conditional triggers (e.g., "When asked about X, load Y"), sharply reducing ambient context size across the team.

• Clear Context Frequently (/clear): Resets stateless conversation history between tasks so long chat histories aren't repeatedly resent as costly input tokens on every turn.

• Scope Prompts & Check /context: Inspects active context regularly and strictly limits prompts to only the files relevant to the immediate task.

rtk-ai/rtk: CLI proxy that reduces LLM token consumption by 60-90% on common dev commands. Single Rust binary, zero dependencies @GitHub

colbymchenry/codegraph: Pre-indexed code knowledge graph, auto syncs on code changes, for Claude Code, Codex, Gemini, Cursor, OpenCode, AntiGravity, Kiro, and Hermes Agent — fewer tokens, fewer tool calls, 100% local @GitHub

The fastest complete code graph · surgical context · built for how agents actually work · 100% local
**Kernel powered by Rust**

codegraph — Understand any codebase as a graph

CodeGraph is a local-first code-intelligence tool. It parses your codebase with tree-sitter, stores every symbol, edge, and file in a local SQLite database, and exposes the result as a queryable knowledge graph — over the Model Context Protocol (MCP), a CLI, and a TypeScript library.

A local-first code-intelligence tool that turns any codebase into a queryable knowledge graph for AI coding agents.

Introduction - Tree-sitter

Tree-sitter is a parser generator tool and an incremental parsing library. It can build a concrete syntax tree for a source file and efficiently update the syntax tree as the source file is edited.