experiment-bridge

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Workflow 1.5: Bridge between idea discovery and auto review. Reads EXPERIMENT_PLAN.md, implements experiment code, deploys to GPU, collects initial results. Use when user says "实现实验", "implement experiments", "bridge", "从计划到跑实验", "deploy the plan", or has an experiment plan ready to execute.

AI & Automation 16,018 stars 1375 forks Updated today MIT

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Skill Content

# Workflow 1.5: Experiment Bridge Implement and deploy experiments from plan: **$ARGUMENTS** ## Overview This skill bridges Workflow 1 (idea discovery + method refinement) and Workflow 2 (auto review loop). It takes the experiment plan and turns it into running experiments with initial results. ``` Workflow 1 output: This skill: Workflow 2 input: refine-logs/EXPERIMENT_PLAN.md → implement → GPT-5.6-Sol review → deploy → collect → initial results ready refine-logs/EXPERIMENT_TRACKER.md code (cross-model) /run-experiment for /auto-review-loop refine-logs/FINAL_PROPOSAL.md ``` ## Constants - **CODE_REVIEW = true** — GPT-5.6-Sol xhigh reviews experiment code before deployment. Catches logic bugs before wasting GPU hours. Set `false` to skip. - **AUTO_DEPLOY = true** — Automatically deploy experiments after implementation + review. Set `false` to manually inspect code before deploying. - **SANITY_FIRST = true** — Run the sanity-stage experiment first (smallest, fastest) before launching the rest. Catches setup bugs early. - **MAX_PARALLEL_RUNS = 4** — Maximum number of experiments to deploy in parallel (limited by available GPUs). - **BASE_REPO = false** — GitHub repo URL to use as base codebase. When set, clone the repo first and implement experiments on top of it. When `false` (default), write code from scratch or reuse existing project files. - **COMPACT = false** — When `true`, (1) read `idea-sta...

Details

Author
wanshuiyin
Repository
wanshuiyin/Auto-claude-code-research-in-sleep
Created
6 months ago
Last Updated
today
Language
Python
License
MIT

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