A specialized skill for PMO Agents to initialize and automate long-term data analysis workflows with external information enhancement.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install data-analysis-init
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 data-analysis-init 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 Data Analysis Initialization Skill?
This Openclaw Skills component is designed to empower PMO Agents to move beyond manual, one-off reporting into a continuous, automated business monitoring ecosystem. By integrating internal data sources like Feishu spreadsheets and local CSV/Excel files with intelligent web-based external attribution, it creates a robust closed-loop system for strategic decision-making.
The skill focuses on aligning high-level business objectives with technical data pipelines. It doesn't just generate a single report; it establishes a standardized framework for monitoring KPIs, identifying anomalies, and augmenting internal data with market context to provide deep, actionable insights over the long term.
Data Analysis Initialization Skill Use Cases
- Periodic business reviews tracking core KPIs like GMV, DAU, and ROI for management decision support.
- Competitive intelligence monitoring to track price changes and market shifts automatically.
- Traffic growth attribution where internal logs are analyzed alongside external platform algorithm updates.
- Strategic PMO monitoring for complex projects requiring standardized data schemas and consistent reporting cycles.
How Data Analysis Initialization Skill Works
- Objective & Strategic Anchoring: The agent interacts with the user to clarify goals, core indicators, and the target audience.
- Data Pipeline Integration: The skill connects to local files or Feishu spreadsheets, verifying access and parsing initial data samples.
- Schema Definition: An automated parser analyzes headers and data types to generate a technical configuration file.
- Analysis Logic Design: The agent designs the reporting template and defines calculation logic for metrics like WoW or YoY growth.
- Search Strategy Configuration: A dynamic strategy is established to trigger web searches for external context based on data fluctuations.
- Trial Run & Calibration: A first-run report is generated to validate calculation accuracy and search relevance before moving to routine execution.
- Lifecycle Monitoring: The system enters a routine phase of data cleaning, trend calculation, and automated reporting.
Data Analysis Initialization Skill Setup
To use this skill within your Openclaw Skills environment, ensure you have the required Python dependencies installed:
pip install "pandas>=2.0.0" "openpyxl>=3.1.0"
Initialize your data source by running the provided schema parser script to generate your configuration:
python scripts/data_schema_parser.py --file ./user-data/your-data.csv --preview-rows 10
Data Analysis Initialization Skill Data Schema & Taxonomy
The skill organizes its operations through a structured set of files within the workspace:
| File Name | Purpose |
|---|---|
data_schema_config.json |
Stores the parsed data structure, including dimension/measure classifications. |
search_strategy_config.md |
Defines both fixed search themes and trigger-based rules for anomaly detection. |
analysis_report_template.md |
A standardized Markdown template used to generate the final decision reports. |
analysis_report.md |
The final output containing internal metrics and external market insights. |
Data Analysis Initialization Skill Advanced Features
- Intelligent Anomaly Detection: Automatically triggers web searches when metrics fluctuate beyond a predefined threshold (e.g., >10%).
- Multi-Source Support: Seamlessly handles local datasets and cloud-based Feishu/Lark spreadsheets.
- External Attribution: Merges internal performance data with industry news and competitor dynamics using natural language reasoning.
- Strategic Review Cycles: Built-in tasks for quarterly or monthly re-evaluations of search strategies and KPI effectiveness.
SKILL.md
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