intelligems-core

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🇺🇸

Original

English

🇨🇳

Translation

Chinese

/intelligems-core

Shared library that powers all Intelligems Analytics skills. Sets up your workspace with the API client, metric helpers, and configuration.

You rarely need to run this directly — other skills (like

/test-verdict

) automatically check for the workspace and set it up if needed.

为所有Intelligems Analytics技能提供支持的共享库。用于为你的工作区配置API客户端、指标辅助工具及相关设置。

你几乎无需直接运行此库——其他技能（如

/test-verdict

）会自动检查工作区状态，若需要则自动完成设置。

What's Included

包含内容

File	Purpose
`ig_client.py`	API client with automatic retry and rate-limit handling
`ig_metrics.py`	Extract values, uplift, confidence, and CI bounds from API responses
`ig_helpers.py`	Formatting, runtime calculation, variation lookup
`ig_config.py`	Shared thresholds (80% confidence, 10-day minimum, etc.)
`ig_slack.py`	Slack Block Kit formatting and webhook delivery
`setup_workspace.sh`	Creates `~/intelligems-analytics/` with venv and dependencies
`setup_automation.sh`	Creates macOS LaunchAgent for scheduled Slack delivery

文件	用途
`ig_client.py`	具备自动重试和速率限制处理功能的API客户端
`ig_metrics.py`	从API响应中提取数值、提升量、置信度及置信区间边界
`ig_helpers.py`	格式化、运行时计算、变体查询
`ig_config.py`	共享阈值设置（如80%置信度、最小10天周期等）
`ig_slack.py`	Slack Block Kit格式化及Webhook推送
`setup_workspace.sh`	创建包含虚拟环境及依赖项的 `~/intelligems-analytics/` 目录
`setup_automation.sh`	创建用于定时Slack推送的macOS LaunchAgent

Step 1: Get API Key

步骤1：获取API密钥

Ask the user for their Intelligems API key.

"What's your Intelligems API key? You can get one by contacting support@intelligems.io"

Never hardcode or assume an API key.

向用户索要他们的Intelligems API密钥。

“你的Intelligems API密钥是什么？你可以通过联系support@intelligems.io获取。”

绝对不要硬编码或默认假设API密钥。

Step 2: Set Up Workspace

步骤2：设置工作区

Run the setup script to create the workspace:

bash

bash setup_workspace.sh

This creates:

```
~/intelligems-analytics/
```
— working directory
```
~/intelligems-analytics/venv/
```
— Python virtual environment
```
~/intelligems-analytics/.env
```
— API key configuration

Then save the user's API key:

bash

echo "INTELLIGEMS_API_KEY=<user's key>" > ~/intelligems-analytics/.env

运行设置脚本以创建工作区：

bash

bash setup_workspace.sh

此脚本会创建：

```
~/intelligems-analytics/
```
— 工作目录
```
~/intelligems-analytics/venv/
```
— Python虚拟环境
```
~/intelligems-analytics/.env
```
— API密钥配置文件

然后保存用户的API密钥：

bash

echo "INTELLIGEMS_API_KEY=<user's key>" > ~/intelligems-analytics/.env

Step 3: Copy Core Libraries

步骤3：复制核心库

Copy all files from

references/

into the workspace:

bash

cp references/ig_client.py ~/intelligems-analytics/
cp references/ig_metrics.py ~/intelligems-analytics/
cp references/ig_helpers.py ~/intelligems-analytics/
cp references/ig_config.py ~/intelligems-analytics/

将

references/

目录下的所有文件复制到工作区：

bash

cp references/ig_client.py ~/intelligems-analytics/
cp references/ig_metrics.py ~/intelligems-analytics/
cp references/ig_helpers.py ~/intelligems-analytics/
cp references/ig_config.py ~/intelligems-analytics/

Step 4: Verify

步骤4：验证

Activate the environment and verify the client works:

bash

source ~/intelligems-analytics/venv/bin/activate
python3 -c "
from ig_client import IntelligemsAPI
from dotenv import load_dotenv
import os
load_dotenv()
api = IntelligemsAPI(os.getenv('INTELLIGEMS_API_KEY'))
tests = api.get_active_experiments()
print(f'Connected. Found {len(tests)} active experiment(s).')
"

If successful, the workspace is ready for any Intelligems Analytics skill.

激活虚拟环境并验证客户端是否正常工作：

bash

source ~/intelligems-analytics/venv/bin/activate
python3 -c "
from ig_client import IntelligemsAPI
from dotenv import load_dotenv
import os
load_dotenv()
api = IntelligemsAPI(os.getenv('INTELLIGEMS_API_KEY'))
tests = api.get_active_experiments()
print(f'Connected. Found {len(tests)} active experiment(s).')
"

如果验证成功，工作区就可以用于任何Intelligems Analytics技能了。