alexgreensh-token-optimizer

Your AI is getting dumber and you can't see it.

Find the ghost tokens. Survive compaction. Track the quality decay.

Opus 4.6 drops from 93% to 76% accuracy across a 1M context window. Compaction loses 60-70% of your conversation. Ghost tokens burn through your plan limits on every single message. Token Optimizer tracks the degradation, cuts the waste, checkpoints your decisions before compaction fires, and tells you what to fix.

Install

/plugin marketplace add alexgreensh/token-optimizer

Then in Claude Code: /token-optimizer

Please enable auto-update after installing. Claude Code ships third-party marketplaces with auto-update off by default, and plugin authors cannot change that default. So you won't get bug fixes automatically unless you turn it on. In Claude Code: /plugin → Marketplaces tab → select alexgreensh-token-optimizer → Enable auto-update. One-time, 10 seconds, and you'll never miss a fix again. Token Optimizer also prints a one-time reminder on your first SessionStart so you don't forget.

Also available as a script install, which auto-updates daily via git pull --ff-only with no toggles required:

git clone https://github.com/alexgreensh/token-optimizer.git ~/.claude/token-optimizer
bash ~/.claude/token-optimizer/install.sh

Works on Claude Code and OpenClaw. Each platform gets its own native plugin (Python for Claude Code, TypeScript for OpenClaw). No bridging, no shared runtime, zero cross-platform dependencies.

What makes this different?

/context tells you your context is 73% full. Token Optimizer tells you WHY, shows you which 12K tokens are wasted on skills you never use, checkpoints your decisions before compaction destroys them, and gives you a quality score that tracks how much dumber your AI is getting as the session goes on.

One shows the dashboard light. The other opens the hood.

Why install this first?

Every Claude Code session starts with invisible overhead: system prompt, tool definitions, skills, MCP servers, CLAUDE.md, MEMORY.md. A typical power user burns 50-70K tokens before typing a word.

At 200K context, that's 25-35% gone. At 1M, it's "only" 5-7%, but the problems compound:

• Quality degrades as context fills. MRCR drops from 93% to 76% across 256K to 1M. Your AI gets measurably dumber with every message.
• You hit rate limits faster. Ghost tokens count toward your plan's usage caps on every message, cached or not. 50K overhead x 100 messages = 5M tokens burned on nothing.
• Compaction is catastrophic. 60-70% of your conversation gone per compaction. After 2-3 compactions: 88-95% cumulative loss. And each compaction means re-sending all that overhead again.
• Higher effort = faster burn. More thinking tokens per response means you hit compaction sooner, which means more total tokens consumed across the session.

Token Optimizer tracks all of this. Quality score, degradation bands, compaction loss, drift detection. Zero context tokens consumed (runs as external Python).