shike-autoresearchCPU-based autonomous optimization loop for skill quality improvement. Runs experiments, evaluates results, keeps improvements. Use when: 自主优化, skill optimiza...
Install via ClawdBot CLI:
clawdbot install sjj2026/shike-autoresearchGrade Limited — based on market validation, documentation quality, package completeness, maintenance status, and authenticity signals.
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https://github.com/karpathy/autoresearchAudited Apr 17, 2026 · audit v1.0
Generated Oct 4, 2026
技能开发者利用该循环自动测试不同prompt配置与技能结构，通过评估成功率找到最优版本。检查点机制确保每次改进都经过人工确认，避免无效改动。
量化研究员将策略参数与评估指标写入config.py，让Agent自动修改experiment.py中的参数组合并回测收益率。results.tsv记录每轮结果，git revert保证未改进时快速回滚。
内容团队设定质量评估指标后，让系统自动生成不同写作风格并评估内容质量分数。通过多轮迭代找到转化率最高的文案策略，人工在检查点把关调性。
游戏策划用该框架自动调整关卡参数（敌人血量、掉落率等），以玩家留存或通关率作为评估指标。每10轮复盘一次，平衡挑战性与可玩性。
教育科技团队对AI助教的提示词进行自主优化，以学生答题准确率作为评估指标。通过迭代找到最能提升学习效果的提示词结构，并保留人工审核环节。
核心工具以MIT-0协议免费开源吸引用户，同时提供付费定制优化服务，针对客户特定技能包或策略进行深度调优。
面向企业客户提供本地化部署与技术支持，帮助团队搭建自主优化流水线并集成到现有CI/CD流程中。
将自主优化循环封装为云端服务，用户按迭代次数或实验时长付费，无需本地配置环境即可运行优化任务。
💬 Integration Tip
需确保工作目录已初始化git仓库并安装python3，首次运行建议将迭代次数调小（如--iterations 10）以验证流程；检查点1-3必须人工确认，切勿跳过。
Scored Jun 4, 2026
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