Auto-optimize: A Claude Code Plugin for Autonomous Performance Optimization

auto-optimize is a Claude Code plugin that automates the performance optimization cycle: profile, find bottlenecks, write fixes, benchmark, and repeat. The developer, a performance engineer working on a high-performance Java hash table, created it to eliminate manual optimization work.

How It Works

The plugin runs an autonomous loop per experiment with these steps:

• Profile — runs async-profiler and parses flamegraph output
• Plan — structured reasoning before touching code, including Step-Back (identifying bottleneck type abstractly), Chain-of-Thought (enumerating strategies with trade-off analysis), and Pre-mortem (assuming the plan already failed to identify potential issues)
• Implement — writes and applies the change
• Benchmark — runs JMH and compares against baseline
• Reflect — writes reflexion.md documenting what was surprising, what failed, and what to try next

Each subsequent experiment reads reflexion.md before profiling to avoid reproposing previously dropped experiments. Without this, the agent would "repropose the same dropped experiment two iterations later with equally confident reasoning — it had no way to know what it had already learned."

Sub-agent Architecture

Each experiment runs in a dedicated sub-agent. Raw profiling output, disassembly, diffs, and benchmark logs never touch the main context. The orchestrator only sees structured return values: what changed, what the numbers showed, and what to try next.

This architecture prevents context pollution: "When the main context fills up, agent behavior degrades in subtle ways — outputs still look coherent, but it starts reasoning about the wrong problem. Moving everything into sub-agents keeps the orchestrator clean indefinitely."

Installation and Usage

Install with:

claude plugin marketplace add bluuewhale/auto-optimize
claude plugin install auto-optimize@auto-optimize

Then run: /auto-optimize

You provide a goal, a benchmark command, and a success threshold. In one case, the developer prompted the plugin once and got a 27% faster hash table across all benchmark scenarios in approximately 3 hours.