Build Datadata DQL Data Scripts
Writing Datadata DQL scripts requires platform-specific Starlark syntax and data APIs. This skill guides Claude, Codex, and Claude Code to create query, fetch, DataFrame, and validation workflows.
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Review the Skillstore skill "datadata-dql" from https://skillstore.io/skills/datadata-team-datadata-dql.md and its manifest at https://skillstore.io/api/skills/datadata-team-datadata-dql/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.Your Agent should still show its plan and request any confirmation required by the security policy.
Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.
Using "datadata-dql". Create a DQL script to compare current month revenue with previous month revenue.
Expected outcome:
The result includes a script plan that queries both periods, calculates revenue change, handles zero previous revenue, and returns comparison fields.
Using "datadata-dql". Clean a customer table before an RFM analysis.
Expected outcome:
The result describes a DQL workflow that checks required columns, removes duplicates, validates missing values, and returns a prepared customer dataset.
Using "datadata-dql". Inspect an external API response before transforming it.
Expected outcome:
The result returns the response type and sample body first, so the user can confirm structure before final transformation logic is written.
No evidence of prompt injection, malware, or install-time execution was found. The static findings are false positives from Markdown code formatting, type-stub docstrings, SQL examples, and documented DQL fetch examples that use placeholder domains. The skill does describe network-capable DQL scripts, so users should approve real endpoints and data sharing before running generated scripts.
Share the versioned assessment report, neutral badge, embed card, and citations. Skillstore reports evidence without deciding whether this Skill is safe.
https://skillstore.io/skills/datadata-team-datadata-dql/audits/4?utm_source=security_passport&utm_medium=share&utm_campaign=versioned_report[](https://skillstore.io/skills/datadata-team-datadata-dql?utm_source=security_passport_badge)<a href="https://skillstore.io/skills/datadata-team-datadata-dql?utm_source=security_passport_badge"><img src="https://skillstore.io/badges/skills/datadata-team-datadata-dql/security.svg" alt="Skillstore security assessment" loading="lazy"></a><iframe src="https://skillstore.io/embed/skills/datadata-team-datadata-dql.html" title="Skillstore Security Assessment" sandbox="allow-popups allow-popups-to-escape-sandbox" loading="lazy" referrerpolicy="no-referrer" width="420" height="180"></iframe>datadata-team. (2026). datadata-dql security audit report (audit version 4) [Author version unspecified]. Skillstore. https://skillstore.io/skills/datadata-team-datadata-dql/audits/4@techreport{datadata-team-datadata-team-datadata-dql-2026,
author = {datadata-team},
title = {datadata-dql security audit report (audit version 4)},
institution = {Skillstore},
year = {2026},
number = {4},
url = {https://skillstore.io/skills/datadata-team-datadata-dql/audits/4},
note = {Author version unspecified}
}cff-version: 1.2.0
message: "If you use this Skill, cite its author and this versioned security audit report."
title: "datadata-dql security audit report (audit version 4)"
version: "unspecified"
type: report
authors:
- name: "datadata-team"
date-released: "2026-07-05"
url: "https://skillstore.io/skills/datadata-team-datadata-dql/audits/4"
identifiers:
- type: other
value: "skillstore:datadata-team-datadata-dql:audit:4"
description: "Skillstore immutable audit report identifier"
Create DQL scripts that query business tables, aggregate metrics, and return validated results for dashboards.
Generate transformations that remove duplicates, fill missing values, and standardize fields before downstream use.
Combine SQL query results with approved external API responses and return a structured dataset for review.
Write a Datadata DQL script that queries [table], limits results to [row count], and returns the DataFrame for inspection.
Create a DQL script that reads [table], removes duplicate rows by [key], handles missing [field], and returns a small validation summary.
Write Datadata DQL to calculate [metric] by [dimension] for [date range], using SQL filtering first and DataFrame operations only when needed.
Create a safe DQL workflow that queries [internal data], fetches approved data from [API endpoint], validates both structures, and returns merged results.
Author
datadata-teamLicense
MIT
Skillstore revision
r1
Version notice
The author did not declare a version.
Ref
7e9e368dc1a370f2040369ebcd0db06d90cb48e5
Maintenance freshness
7/18/2026
Usage
1 downloads · 37 views
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