A sophisticated AI skill for querying, visualizing, and analyzing datasets using the Powerdrill API.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install powerdrill-data-analysis-skill
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 powerdrill-data-analysis-skill 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 Powerdrill Data Analysis?
The Powerdrill Data Analysis skill enables AI agents to interact directly with the Powerdrill REST API v2 for sophisticated data exploration. It provides a robust Python-based interface to manage datasets, upload diverse file formats like CSV or PDF, and perform natural-language queries to extract meaningful insights. By integrating this capability through Openclaw Skills, developers can build agents that not only read data but also generate visual charts and structured tables. This skill is designed for high-performance data workflows, offering seamless session management to maintain conversational context during complex analyses. Whether you are automating business intelligence or conducting research, Openclaw Skills ensure your agent has the tools needed to turn raw data into actionable knowledge.
Powerdrill Data Analysis Use Cases
- Automating the upload and synchronization of local datasets for immediate AI analysis using Openclaw Skills.
- Performing natural-language queries to identify trends, outliers, or specific data points within large files.
- Generating visual charts and data tables from raw formats like Excel, JSON, or CSV.
- Maintaining multi-turn conversational analysis within persistent sessions to explore complex data relationships.
- Cleaning up temporary data environments after an automated analysis job completes to maintain security.
How Powerdrill Data Analysis Works
- Initialize the environment by setting the required Powerdrill API credentials and user IDs via environment variables.
- Create or select a dataset and upload local source files using the built-in multipart upload utility.
- Monitor the synchronization status using polling functions to ensure data is fully indexed and ready for querying.
- Establish an analysis session to manage history and context for the AI agent.
- Submit natural-language questions as jobs to the Powerdrill API to receive text, code, or visual data blocks.
- Process the resulting response blocks and perform final cleanup of datasets and sessions using the Openclaw Skills framework.
Powerdrill Data Analysis Setup
Install the necessary dependencies and configure your environment variables to begin using this skill:
pip install requests
export POWERDRILL_USER_ID="your_user_id"
export POWERDRILL_PROJECT_API_KEY="your_project_api_key"
To use the client in your scripts, adjust the path and import the module:
import sys
sys.path.insert(0, "/path/to/scripts")
from powerdrill_client import *
Powerdrill Data Analysis Data Schema & Taxonomy
The skill organizes data into a hierarchy of datasets and sources, managed via the following structure:
| Entity | Attributes | Description |
|---|---|---|
| Dataset | ID, Name, Description | The primary container for managing related data sources. |
| Data Source | ID, Name, Status, URL | Individual files (PDF, CSV, XLSX, etc.) attached to a dataset. |
| Session | ID, Name, Context | Manages the conversation state and history for iterative analysis. |
| Job Result | MESSAGE, TABLE, IMAGE | The structured output types generated by the analysis engine. |
Powerdrill Data Analysis Advanced Features
- Comprehensive multi-format support including .md, .docx, .pptx, and .json for diverse data ingestion.
- Polling-based synchronization management with wait_for_dataset_sync for reliable automation workflows.
- Contextual session history allowing for complex follow-up questions within the same thread in Openclaw Skills.
- Automated resource lifecycle management via the cleanup function to prevent storage bloat and ensure data privacy.
- Direct CLI integration for headless data analysis and integration into existing CI/CD pipelines.
SKILL.md
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