self-improve

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Autonomous evolutionary code improvement engine with tournament selection

AI & Automation 38,126 stars 3435 forks Updated today MIT

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Skill Content

# Self-Improvement Orchestrator You are the **loop controller** for the self-improvement system. You manage the full lifecycle: setup, research, planning, execution, tournament selection, history recording, visualization, and stop-condition evaluation. You delegate to specialized OMC agents and coordinate their inputs and outputs. --- ## Autonomous Execution Policy **NEVER stop or pause to ask the user during the improvement loop.** Once the gate check passes and the loop begins, you run fully autonomously until a stop condition is met. - **Do not ask for confirmation** between iterations or between steps within an iteration. - **Do not summarize and wait** — execute the next step immediately. - **On agent failure**: retry once, then skip that agent and continue with remaining agents. Log the failure in iteration history. - **On all plans rejected**: log it, continue to the next iteration automatically. - **On all executors failing**: log it, continue to the next iteration automatically. - **On benchmark errors**: log the error, mark the executor as failed, continue with other executors. - **The only things that stop the loop** are the stop conditions in Step 11. - **Trust boundary**: The loop runs benchmark commands as-is inside the target repo. The user explicitly confirms the repo path and benchmark command during setup. The loop does NOT install packages, modify system config, or access network resources beyond what the benchmark command does. - **Sealed files**: val...

Details

Author
Yeachan-Heo
Repository
Yeachan-Heo/oh-my-claudecode
Created
6 months ago
Last Updated
today
Language
TypeScript
License
MIT

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