scientist
7,785
Stars
510
Forks
433
Watchers
17
Open Issues
MIT
许可证
Ruby
语言
2014-02-05
创建时间
2026-11-06
最近更新
项目能做什么
用于安全重构关键路径业务代码的 Ruby 实验与对比库。
来源:基于仓库元数据
· 内容以仓库实际 README 为准
使用说明 查看原始 README 原仓库文档
中文使用说明暂未生成。下面保留原仓库 README 摘录,命令与配置均以原文为准。
原始 README 摘录(英文原文,节选前 3000 字符)
# Scientist!
A Ruby library for carefully refactoring critical paths. [](https://github.com/github/scientist/actions/workflows/ci.yml)
## How do I science?
Let's pretend you're changing the way you handle permissions in a large web app. Tests can help guide your refactoring, but you really want to compare the current and refactored behaviors under load.
```ruby
require "scientist"
class MyWidget
def allows?(user)
experiment = Scientist::Default.new "widget-permissions"
experiment.use { model.check_user(user).valid? } # old way
experiment.try { user.can?(:read, model) } # new way
experiment.run
end
end
```
Wrap a `use` block around the code's original behavior, and wrap `try` around the new behavior. `experiment.run` will always return whatever the `use` block returns, but it does a bunch of stuff behind the scenes:
* It decides whether or not to run the `try` block,
* Randomizes the order in which `use` and `try` blocks are run,
* Measures the wall time and cpu time of all behaviors in seconds,
* Compares the result of `try` to the result of `use`,
* Swallow and record exceptions raised in the `try` block when overriding `raised`, and
* Publishes all this information.
The `use` block is called the **control**. The `try` block is called the **candidate**.
Creating an experiment is wordy, but when you include the `Scientist` module, the `science` helper will instantiate an experiment and call `run` for you:
```ruby
require "scientist"
class MyWidget
include Scientist
def allows?(user)
science "widget-permissions" do |experiment|
experiment.use { model.check_user(user).valid? } # old way
experiment.try { user.can?(:read, model) } # new way
end # returns the control value
end
end
```
If you don't declare any `try` blocks, none of the Scientist machinery is invoked and the control value is always returned.
## Making science useful
The examples above will run, but they're not really *doing* anything. The `try` blocks don't run yet and none of the results get published. Replace the default experiment implementation to control execution and reporting:
```ruby
require "scientist/experiment"
class MyExperiment
include Scientist::Experiment
attr_accessor :name
def initialize(name)
@name = name
end
def enabled?
# see "Ramping up experiments" below
true
end
def raised(operation, error)
# see "In a Scientist callback" below
p "Operation '#{operation}' failed with error '#{error.inspect}'"
super # will re-raise
end
def publish(result)
# see "Publishing results" below
p result
end
end
```
When `Scientist::Experiment` is included in a class, it automatically sets it as the default implementation via `Scientist::Experiment.set_default`. This `set_default` call is skipped if you include `Scientist::Experiment` in a module.
Now calls to the `science` helper wi
阅读完整原文
商业机会分析 规则生成(AI 未配置)
综合评分 44.3 分。该项目在“个人可实施性”维度得 45 分,“付费可能性”维度得 20.0 分,差异化空间 70.0 分(评分依据见评分卡)。
面向细分需求的二次开发产品
| 产品构想 | 基于 scientist 的核心能力,聚焦一个更窄的场景(如某平台、某行业)做差异化。 |
| 目标用户 | 该细分场景用户 |
| 难度/周期 | 中 · 2~6 周 |
| 收费模式 | 买断制 / 订阅制 / 广告 |
| 竞争风险 | 细分需求规模需验证 |
| 依据 | AI 推测:基于项目主题标签的判断 |
以上为规则引擎基于仓库公开数据生成的参考结论,包含推测成分,不构成投资或创业承诺;配置 AI 后可获得更深入的分析。
报告会保存到 个人中心
竞品与相似项目 GitHub Search 真实检索
| 项目 | 中文简介 | Stars | 语言 |
|---|---|---|---|
| K-Dense-AI/scientific-agent-skills | 中文介绍暂未生成 Turn any AI agent into an AI Scientist. The #1 Agent Skills |
56,802 | Python |
| SakanaAI/AI-Scientist | 中文介绍暂未生成 The AI Scientist: Towards Fully Automated Open-Ended Scienti |
14,982 | Jupyter Notebook |
| ahmedbahaaeldin/From-0-to-Research-Scientist-resources-guide | 中文介绍暂未生成 Detailed and tailored guide for undergraduate students or an |
7,692 | - |
| dwzhu-pku/PaperBanana | 中文介绍暂未生成 PaperBanana: Automating Academic Illustration For AI Scienti |
7,622 | Python |
| SakanaAI/AI-Scientist-v2 | 中文介绍暂未生成 The AI Scientist-v2: Workshop-Level Automated Scientific Dis |
7,496 | Python |
| MrMimic/data-scientist-roadmap | 中文介绍暂未生成 Toturials coming with the "data science roadmap" picture. |
7,407 | Jupyter Notebook |
以上为通过 GitHub 官方 Search API 检索的相似项目,功能差异与优劣势需进一步人工确认,配置 AI 后可生成对比分析。
项目优势与风险
| 技术成熟度 | 创建于 2014-02-05,近 30 天仍在推送,发布过 1 个 Release |
| 社区活跃 | Star 7,785 / Fork 510 / Issue 17(数据支持,可自行核实) |
| 许可证 | MIT |
| 主要风险 | 上游项目方向可能变化;同赛道竞争者较多(见竞品表);热度与需求不完全等价 |
安装与使用
快速开始(以仓库 README 为准)
git clone https://github.com/github/scientist.git cd scientist # 请阅读仓库 README 获取确切安装步骤(依赖、配置、启动命令)
术语说明:Docker=应用容器(把程序和依赖打包运行);API Key=调用外部服务的密钥;Git Clone=把仓库代码下载到本地。常见错误:API Key 未配置 / 端口占用 / 数据库连接失败,多数项目在 README 的 Troubleshooting 章节有说明。