This skill should be used when the user wants to analyze, explore, visualize, or query data using Powerdrill. Covers listing, creating, and deleting datasets; uploading local files as data sources; creating analysis sessions; running natural-language data analysis queries; and retrieving charts, tables, and insights. Triggers on requests like "analyze my data", "query my dataset", "upload this file for analysis", "list my datasets", "create a dataset", "visualize sales trends", "continue my previous analysis", "delete this dataset", or any data exploration task mentioning Powerdrill.
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name
powerdrill-data-analysis
description
This skill should be used when the user wants to analyze, explore, visualize, or query data using Powerdrill. Covers listing, creating, and deleting datasets; uploading local files as data sources; creating analysis sessions; running natural-language data analysis queries; and retrieving charts, tables, and insights. Triggers on requests like "analyze my data", "query my dataset", "upload this file for analysis", "list my datasets", "create a dataset", "visualize sales trends", "continue my previous analysis", "delete this dataset", or any data exploration task mentioning Powerdrill.
license
MIT
compatibility
claude
metadata
{"audience":"human","workflow":"powerdrill"}
Powerdrill Data Analysis Skill
Analyze data using the Powerdrill API via the Python client at scripts/powerdrill_client.py. All operations use the Powerdrill REST API v2 (https://ai.data.cloud/api).
Prerequisites & Setup
Before using any Powerdrill functions, the user must have:
Create a data source from a public URL or an uploaded file key. Provide exactly one of url or file_object_key.
# From public URL
ds = create_data_source(dataset_id, "report.pdf", url="https://example.com/report.pdf")
# From uploaded file (see upload_local_file)
ds = create_data_source(dataset_id, "data.csv", file_object_key=key)
upload_local_file(file_path) -> str
Upload a local file via multipart upload. Returns file_object_key for use with create_data_source().
Run a natural-language analysis query. This is the core analysis function.
Non-streaming (default): returns full response with all blocks.
result = create_job(session_id, "What are the top 5 products by revenue?", dataset_id=dataset_id)
for block in result["data"]["blocks"]:
if block["type"] == "MESSAGE":
print(block["content"])
elif block["type"] == "TABLE":
print(f"Table: {block['content']['url']}")
elif block["type"] == "IMAGE":
print(f"Chart: {block['content']['url']}")
Streaming: returns parsed result with accumulated text and separate blocks.
result = create_job(session_id, "Summarize trends", dataset_id=dataset_id, stream=True)
print(result["text"]) # accumulated MESSAGE textfor b in result["blocks"]: # TABLE, IMAGE, etc.print(b["type"], b["content"])
Response block types:
MESSAGE - Analytical text
CODE - Code snippets (Markdown)
TABLE - {name, url, expires_at} - download before expiration
IMAGE - {name, url, expires_at} - download before expiration
SOURCES - Citation references
QUESTIONS - Suggested follow-up questions
CHART_INFO - Chart configuration and data
Cleanup
cleanup(session_id=None, dataset_id=None) -> None
Delete session and/or dataset after analysis. Always call this when done.
Delete individual resources. Errors are logged but not raised.
Recommended Workflows
Full analysis workflow (upload, analyze, cleanup)
from powerdrill_client import *
# 1. Create dataset and upload data
ds = create_dataset("My Analysis")
dataset_id = ds["data"]["id"]
upload_and_create_data_source(dataset_id, "/path/to/data.csv")
wait_for_dataset_sync(dataset_id)
# 2. Create session and run analysis
session = create_session("Analysis Session")
session_id = session["data"]["id"]
result = create_job(session_id, "What are the key trends?", dataset_id=dataset_id)
for block in result["data"]["blocks"]:
if block["type"] == "MESSAGE":
print(block["content"])
# 3. Ask follow-up questions (same session for context)
result = create_job(session_id, "Break this down by region", dataset_id=dataset_id)
# 4. Cleanup when done
cleanup(session_id=session_id, dataset_id=dataset_id)
Analyze existing dataset
from powerdrill_client import *
# 1. Find the dataset
datasets = list_datasets(search="sales")
dataset_id = datasets["data"]["records"][0]["id"]
# 2. Explore it
overview = get_dataset_overview(dataset_id)
print(overview["data"]["summary"])
# 3. Create session and analyze
session = create_session("Quick Analysis")
session_id = session["data"]["id"]
result = create_job(session_id, overview["data"]["exploration_questions"][0], dataset_id=dataset_id)
# 4. Cleanup session when done (keep dataset)
cleanup_session(session_id)
CLI usage
# List datasets
python scripts/powerdrill_client.py list-datasets --search "sales"# Create dataset + upload file
python scripts/powerdrill_client.py create-dataset "Test Data"
python scripts/powerdrill_client.py upload-file dset-xxx /path/to/file.csv
python scripts/powerdrill_client.py wait-sync dset-xxx
# Create session and run a job
python scripts/powerdrill_client.py create-session "My Session"
python scripts/powerdrill_client.py create-job SESSION_ID "Summarize the data" --dataset-id dset-xxx
# Cleanup
python scripts/powerdrill_client.py cleanup --session-id SESSION_ID --dataset-id dset-xxx
Error Handling
Authentication errors: Verify POWERDRILL_USER_ID and POWERDRILL_PROJECT_API_KEY. Direct the user to the setup videos above.
Dataset not found: Re-run list_datasets() to verify the ID. The dataset may have been deleted.
Job execution failure: Ensure the dataset has at least one synced data source (wait_for_dataset_sync()). Retry with a rephrased question.
Upload timeout:wait_for_dataset_sync() polls up to 30 attempts (90s). Use get_dataset_status() to check manually.
Invalid data sources: Check file format is supported. Re-upload with correct file type.
Rate limiting: Wait before retrying. Space out rapid sequential API calls.
Important Notes
Always create a session before running analysis jobs
Always call cleanup() to delete sessions and datasets after analysis is complete
Sessions maintain conversational context - reuse the same session for related follow-up questions
TABLE and IMAGE URLs in job responses expire - download or present results promptly
Call wait_for_dataset_sync() after uploading files, before running analysis
Dataset and session names are limited to 128 characters
