webask-data-quality
Check WebAsk Survey Data Quality
Suspicious survey responses can distort results and weaken decisions. This skill guides checks of WebAsk responses and compares key metrics before recommending exclusions.
Install with my Agent
Copy this request to your Agent. It includes the canonical Skill page and manifest.
Review the Skillstore skill "webask-data-quality" from https://skillstore.io/skills/webaskio-webask-data-quality.md and its manifest at https://skillstore.io/api/skills/webaskio-webask-data-quality/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.
Agent-readable resources
Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.
Test it
Using "webask-data-quality". Illustrative request: Review 100 responses and estimate the effect of excluding suspicious submissions.
Expected outcome:
- Fast completions: 12 responses, or 12%. Repetitive choices: 8 responses, or 8%.
- Four responses match both indicators. Unique flagged responses: 16, or 16%.
- Excluding those responses would leave 84. The average rating would change from 4.0 to 4.1.
- Recommend reviewing and tagging flagged responses before deciding on exclusions. No responses were changed.
Using "webask-data-quality". Illustrative request: Review contradictory answers and harsh comments before publishing survey results.
Expected outcome:
- Three responses deny using the service but also rate its quality. Review the survey display logic.
- Five comments contain specific complaints. Negative sentiment alone is not a reason to exclude them.
- Recommend reviewing contradictions separately from critical feedback. No responses were changed.
Security Audit
SafeAll three static findings are false positives: relative documentation links and Markdown formatting around tool names. No evidence found of prompt injection, unauthorized execution, or data exfiltration in the reviewed files. The workflow uses survey retrieval tools and prohibits autonomous deletion.
Risk Factors
📁 Filesystem access (1)
⚙️ External commands (2)
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https://skillstore.io/skills/webaskio-webask-data-quality/audits/1?utm_source=security_passport&utm_medium=share&utm_campaign=versioned_reportMarkdown badge
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<a href="https://skillstore.io/skills/webaskio-webask-data-quality?utm_source=security_passport_badge"><img src="https://skillstore.io/badges/skills/webaskio-webask-data-quality/security.svg" alt="Skillstore security assessment" loading="lazy"></a>Embed card
<iframe src="https://skillstore.io/embed/skills/webaskio-webask-data-quality.html" title="Skillstore Security Assessment" sandbox="allow-popups allow-popups-to-escape-sandbox" loading="lazy" referrerpolicy="no-referrer" width="420" height="180"></iframe>Academic citations (APA · BibTeX · CFF)
APA citation
webaskio. (2026). webask-data-quality security audit report (audit version 1) [Author version unspecified]. Skillstore. https://skillstore.io/skills/webaskio-webask-data-quality/audits/1BibTeX citation
@techreport{webaskio-webaskio-webask-data-quality-2026,
author = {webaskio},
title = {webask-data-quality security audit report (audit version 1)},
institution = {Skillstore},
year = {2026},
number = {1},
url = {https://skillstore.io/skills/webaskio-webask-data-quality/audits/1},
note = {Author version unspecified}
}CITATION.cff
cff-version: 1.2.0
message: "If you use this Skill, cite its author and this versioned security audit report."
title: "webask-data-quality security audit report (audit version 1)"
version: "unspecified"
type: report
authors:
- name: "webaskio"
date-released: "2026-09-29"
url: "https://skillstore.io/skills/webaskio-webask-data-quality/audits/1"
identifiers:
- type: other
value: "skillstore:webaskio-webask-data-quality:audit:1"
description: "Skillstore immutable audit report identifier"
Skillstore Score
Why this score Evidence Confidence: MediumWhat You Can Build
Review an incentivized survey
Inspect fast completions, repeated contacts, and matching response bursts before interpreting results from a rewarded survey.
Prepare a survey report
Compare key metrics before and after proposed exclusions to assess whether quality concerns affect the report.
Investigate conflicting feedback
Review contradictory responses and possible survey display logic errors without treating negative customer feedback as invalid.
Try These Prompts
Check the response quality for WebAsk survey [survey]. Summarize the main concerns without changing any responses.
Review [survey] for fast completions, repetitive answers, duplicate contacts, and meaningless text. Report counts and percentages for each indicator.
For [survey], compare [key metrics] before and after proposed exclusions. Count overlapping flags only once when calculating remaining responses. Do not modify data.
Investigate matching response bursts, duplicate contacts, and contradictory answers in [survey]. Separate possible display logic errors from suspicious behavior. Compare metric changes and recommend reversible actions.
Best Practices
- Review suspicious indicators in context before treating any response as invalid.
- Report indicator percentages and count overlapping flags only once when estimating remaining responses.
- Compare key metrics before exclusions and prefer reversible tagging.
Avoid
- Removing low ratings or harsh comments solely because they are negative.
- Deleting flagged responses without an explicit request and confirmation.
- Changing the sample to produce a preferred conclusion.
Frequently Asked Questions
Which AI tools are supported?
What WebAsk tools does it require?
Does a suspicious response prove fraud?
Will it automatically delete responses?
Does it exclude negative feedback?
What happens when a service limit is reached?
Developer Details
Author
webaskioLicense
MIT
Skillstore revision
r1
Version notice
The author did not declare a version.
Ref
bad9dafc37d1638cd29cb9bab06d5f8dbcd0f6c2
Maintenance freshness
9/30/2026
Usage
0 downloads · 0 views
File structure