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.
Installer avec mon Agent
Copiez cette demande dans votre Agent. Elle contient la page Skill de référence et le manifeste.
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.Votre Agent doit toujours présenter son plan et demander toute confirmation exigée par la politique de sécurité.
Ressources lisibles par les agents
Utilisez ces liens lorsqu’un Agent IA, un crawler ou un script a besoin d’un contexte propre au lieu de lire toute la page.
Tester
Utilisation de « webask-data-quality ». Illustrative request: Review 100 responses and estimate the effect of excluding suspicious submissions.
Résultat attendu:
- 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.
Utilisation de « webask-data-quality ». Illustrative request: Review contradictory answers and harsh comments before publishing survey results.
Résultat attendu:
- 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.
Audit de sécurité
SûrAll 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.
Facteurs de risque
📁 Accès au système de fichiers (1)
⚙️ Commandes externes (2)
Partager et citer ce rapport
Partagez le rapport d’évaluation versionné, le badge neutre, la carte intégrable et les citations. Skillstore présente les preuves sans décider si ce Skill est sûr.
Copier le lien du rapport
https://skillstore.io/skills/webaskio-webask-data-quality/audits/1?utm_source=security_passport&utm_medium=share&utm_campaign=versioned_reportBadge Markdown
[](https://skillstore.io/skills/webaskio-webask-data-quality?utm_source=security_passport_badge)Badge HTML
<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>Carte intégrable
<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>Citations académiques (APA · BibTeX · CFF)
Citation APA
webaskio. (2026). webask-data-quality security audit report (audit version 1) [Author version unspecified]. Skillstore. https://skillstore.io/skills/webaskio-webask-data-quality/audits/1Citation BibTeX
@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"
Score Skillstore
Pourquoi ce score Niveau de confiance des preuves: MoyenCe que vous pouvez créer
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.
Essayez ces 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.
Bonnes pratiques
- 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.
Éviter
- 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.
Foire aux 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?
Détails du développeur
Auteur
webaskioLicence
MIT
Révision Skillstore
r1
Avis de version
L’auteur n’a déclaré aucune version.
Réf
bad9dafc37d1638cd29cb9bab06d5f8dbcd0f6c2
Récence de maintenance
30/09/2026
Utilisation
0 téléchargements · 0 vues
Structure de fichiers