An expert diagnostic tool that identifies why business metrics fluctuate by combining factor decomposition, dimension drilling, and external event correlation.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install metric-attribution
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install metric-attribution using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Metric Attribution Diagnosis?
The Metric Attribution Diagnosis skill is an advanced analytical framework designed to answer the fundamental question: "Why did this metric change?" It moves beyond simple observation to provide a systematic diagnostic workflow that quantifies the impact of different business segments. By integrating with the Aloudata CAN Gateway API, it performs high-precision local calculations to pinpoint whether a change was driven by specific business stages, geographical shifts, or external market forces.
This skill is essential for data analysts and business leads who need to move from 'what happened' to 'why it happened.' It leverages sophisticated mathematical models like Shapley value decomposition to ensure that even complex, multi-factor changes are attributed accurately, providing a clear and actionable diagnostic report.
Metric Attribution Diagnosis Use Cases
- Investigating sudden drops or surges in key performance indicators like GMV or Revenue.
- Performing root cause analysis on conversion rate fluctuations across different sales channels.
- Conducting factor decomposition to see how price vs. volume impacted total sales.
- Correlating internal data trends with external events like holidays, weather, or competitor actions.
- Automating quarterly business reviews with data-driven attribution summaries.
How Metric Attribution Diagnosis Works
- Confirm Fluctuation Facts: Establish the baseline for comparison (MoM, YoY, or target-based) and quantify the exact magnitude of the change.
- Factor Decomposition: Break down complex metrics into business drivers (e.g., GMV = UV × CVR × AOV) to identify which part of the business funnel failed or succeeded.
- Dimension Attribution: Drill down into specific dimensions like region, product line, or channel to locate where the problem is concentrated.
- External Event Correlation: Search for and link external factors such as market policies or weather conditions to the observed data patterns.
- Comprehensive Diagnostic Report: Aggregate all findings into a structured report, often featuring waterfall charts for visual clarity.
Metric Attribution Diagnosis Setup
To utilize this skill within the Openclaw Skills ecosystem, you must configure your API access:
- Obtain your Aloudata CAN Gateway API Key.
- Configure your environment variable in
~/.openclaw/.env:
CAN_API_KEY=your_api_key_here
- Ensure you have the necessary Python dependencies for visualization:
pip install matplotlib
Metric Attribution Diagnosis Data Schema & Taxonomy
The skill organizes data through a structured retrieval and calculation process:
| Data Type | Source | Purpose |
|---|---|---|
| Metric Metadata | Gateway API | Retrieves valid metric names and available dimensions. |
| Fact Data | Gateway API | Raw numerical values for the current and baseline periods. |
| Attribution Results | Local Calculation | Shapley values and contribution percentages for factors/dimensions. |
| Visual Assets | Local Generation | Waterfall charts (PNG/SVG) representing the attribution flow. |
Metric Attribution Diagnosis Advanced Features
- Shapley Value Decomposition: Handles non-linear multi-factor attribution to prevent mathematical errors in complex formulas.
- Automated Waterfall Charting: Generates professional-grade visualizations using matplotlib to show positive and negative contributions.
- Precision Filtering: Supports multi-keyword dimension filtering and complex time constraints using SQL-like expressions.
- Intelligent Baseline Inference: Automatically suggests appropriate comparison periods (like YoY for seasonal industries) if not specified by the user.
SKILL.md
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