intelligems-segment-analysis
npx skills add https://github.com/victorpay1/intelligems-plugins --skill intelligems-segment-analysis
Agent 安装分布
Skill 文档
/intelligems-segment-analysis
Analyze segment-level performance of active Intelligems experiments.
Shows which segments (device, visitor type, traffic source) each experiment is winning in, with:
- Visitors and orders per segment
- Which variation is performing best
- RPV lift and confidence levels
- GPV lift (if COGS data exists)
Prerequisites
- Python 3.8+
- Intelligems API key (get from Intelligems support)
Step 1: Get API Key
Ask the user for their Intelligems API key.
If they don’t provide it upfront, ask:
“What’s your Intelligems API key? You can get one by contacting support@intelligems.io“
IMPORTANT: Never use a hardcoded or default API key. The user must provide their own.
Step 2: Set Up Project
Create a project directory with the analysis script:
mkdir -p intelligems-segment-analysis
cd intelligems-segment-analysis
Create config.py
Copy from references/config.py:
# Intelligems Segment Analysis Configuration
# API Configuration
API_BASE = "https://api.intelligems.io/v25-10-beta"
# Thresholds (Intelligems Philosophy: 80% is enough)
MIN_CONFIDENCE = 0.80 # 80% probability to beat baseline
MIN_RUNTIME_DAYS = 14 # Don't make status judgments until test runs 2+ weeks
# Segment types to analyze
SEGMENT_TYPES = [
("device_type", "BY DEVICE"),
("visitor_type", "BY VISITOR TYPE"),
("source_channel", "BY TRAFFIC SOURCE"),
]
Create segment_analysis.py
Copy the full script from references/segment_analysis.py.
Create .env
Create .env file with the user’s API key:
INTELLIGEMS_API_KEY=<user's key here>
Install Dependencies
pip install requests python-dotenv tabulate
Step 3: Run Analysis
python segment_analysis.py
Step 4: Interpret Results
Status meanings:
- Doing well – Variant beating control with 80%+ confidence
- Not doing well – Control beating variant with 80%+ confidence
- Inconclusive – Not enough confidence either way
- Too early – Test running less than 2 weeks (don’t trust yet)
- Low data – Not enough orders to calculate confidence
Example output:
======================================================================
Homepage Price Test
Runtime: 21 days | Visitors: 45,000 | Orders: 320
======================================================================
ð± BY DEVICE
âââââââââââ¬âââââââââââ¬âââââââââ¬ââââââââââââ¬ââââââââââââââ¬âââââââââââ¬âââââââââââââ®
â Segment â Visitors â Orders â Variation â Status â RPV Lift â Confidence â
âââââââââââ¼âââââââââââ¼âââââââââ¼ââââââââââââ¼ââââââââââââââ¼âââââââââââ¼âââââââââââââ¤
â Mobile â 32,000 â 180 â +5% â Doing well â +12.3% â 87% â
â Desktop â 13,000 â 140 â +5% â Inconclusiveâ +4.2% â 62% â
â°ââââââââââ´âââââââââââ´âââââââââ´ââââââââââââ´ââââââââââââââ´âââââââââââ´âââââââââââââ¯
This shows the +5% price variant is winning on mobile (87% confidence) but inconclusive on desktop.
Troubleshooting
“INTELLIGEMS_API_KEY not found”
- Ensure
.envfile exists with the key - Or export:
export INTELLIGEMS_API_KEY=your_key
“No active experiments found”
- Check that experiments have status “started” in Intelligems dashboard
“Error fetching experiments”
- Verify API key is correct
- Check network connection
References
references/segment_analysis.py– Full analysis scriptreferences/config.py– Configuration filereferences/setup-guide.md– Detailed setup instructions