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Automate survey deployment, data collection, and pipeline management
Passed 20 of 25 test cases with the skill, and 15 of 25 without it.
and 6 more in the full report
6 fixed14 passed either way4 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 6 test cases the skill fixed. Same model, same prompt, with and without the skill.
What specific summary keys must be returned by the survey data validation check function when compiling issues?
| Checked | Without skill | With skill |
|---|---|---|
| Includes keys n_records, n_issues, issues, and timestamp in the returned dict. | Missed | Passed |
The instructions your model reads when the skill loads.
A skill for automating research data collection, survey deployment, and data pipeline management. Covers survey platform APIs, automated data retrieval, quality checks, ETL pipelines, and scheduling for longitudinal studies.
import os
import json
import urllib.request
import time
def export_qualtrics_responses(survey_id: str,
file_format: str = "csv") -> str:
"""
Export survey responses from Qualtrics via API.
Args:
survey_id: The Qualtrics survey ID (SV_...)
file_format: Export format (csv, json, spss)
"""
api_token = os.environ["QUALTRICS_API_TOKEN"]
data_center = os.environ["QUALTRICS_DATACENTER"]
base_url = f"https://{data_center}.qualtrics.com/API/v3"
headers = {
"X-API-TOKEN": api_token,
"Content-Type": "application/json"
}
# Step 1: Start export
export_data = json.dumps({
"format": file_format,
"compress": False
}).encode("utf-8")
req = urllib.request.Request(
test cases passed, vs 15 / 25 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.