Business teams often lack consistent metrics and repeatable analysis workflows. This skill guides SQL, Python, dashboards, experiments, and automated reporting from requirements to delivery.
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Agent request
Review the Skillstore skill "bi-analyst" from https://skillstore.io/skills/zl2023github-bi-analyst.md and its manifest at https://skillstore.io/api/skills/zl2023github-bi-analyst/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.
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Agent-readable resources
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Using "bi-analyst". Assess whether a checkout experiment should launch.
Expected outcome:
The treatment improved conversion, but the confidence interval includes effects too small to justify rollout. Continue the test until the planned sample size.
All 42 static alerts are false positives caused by Markdown fences, SQL identifiers, and multilingual documentation entropy. The skill contains legitimate BI examples and no malicious intent or prompt injection.
Share the versioned assessment report, neutral badge, embed card, and citations. Skillstore reports evidence without deciding whether this Skill is safe.
zl2023github. (2026). bi-analyst security audit report (audit version 4) [Author version unspecified]. Skillstore. https://skillstore.io/skills/zl2023github-bi-analyst/audits/4
BibTeX citation
@techreport{zl2023github-zl2023github-bi-analyst-2026,
author = {zl2023github},
title = {bi-analyst security audit report (audit version 4)},
institution = {Skillstore},
year = {2026},
number = {4},
url = {https://skillstore.io/skills/zl2023github-bi-analyst/audits/4},
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: "bi-analyst security audit report (audit version 4)"
version: "unspecified"
type: report
authors:
- name: "zl2023github"
date-released: "2026-07-24"
url: "https://skillstore.io/skills/zl2023github-bi-analyst/audits/4"
identifiers:
- type: other
value: "skillstore:zl2023github-bi-analyst:audit:4"
description: "Skillstore immutable audit report identifier"
Skillstore Score
Why this scoreEvidence Confidence: Medium
55
Architecture
85
Maintainability
87
Content
65
Community
83
Spec Compliance
What You Can Build
Prepare an Executive KPI Review
Define consistent metrics, compare periods, identify changes, and structure a concise decision-ready report.
Design a Product Analytics Dashboard
Plan funnel, retention, and cohort views with suitable dimensions, refresh schedules, and alert thresholds.
Build a Reproducible Reporting Pipeline
Organize extraction, cleaning, validation, calculation, visualization, and scheduled delivery steps.
Try These Prompts
Summarize a Dataset
Review this dataset description: [description]. Identify useful metrics, required cleaning checks, and three questions the data can answer.
Design a KPI Dashboard
Design a dashboard for [business goal]. Define the primary metric, supporting metrics, dimensions, filters, refresh frequency, and validation checks.
Evaluate an A/B Test
Analyze this experiment: [groups, sample sizes, outcomes, dates]. Check assumptions, choose suitable tests, estimate effect size, and state decision risks.
Plan an Automated BI Pipeline
Plan a scheduled pipeline from [source] to [report destination]. Include transformations, quality gates, metric definitions, retries, alerts, and reproducibility controls.
Best Practices
Confirm metric definitions, time ranges, dimensions, and audiences before analysis.
Validate important results against a second query, source, or BI view.
Document assumptions, data quality issues, and reproducible calculation steps.
Avoid
Do not infer business causes from correlation without supporting evidence.
Do not load full large tables into Python when database aggregation is available.
Do not publish dashboards with undefined metrics or untested refresh logic.
Frequently Asked Questions
Can this skill connect to my database?
It can guide connection and query design, but it does not provide credentials or a built-in connector.
Which data formats does it cover?
The guidance covers relational databases, CSV files, Excel workbooks, APIs, and logs at a workflow level.
Can it create SQL and Python analysis?
Yes. It provides adaptable patterns for querying, cleaning, metrics, statistics, visualization, and reporting.
Does it support A/B test analysis?
Yes. It covers hypothesis tests, conversion comparisons, effect size, sample considerations, and decision framing.
Can it design dashboards?
Yes. It helps define metrics, layouts, filters, drilldowns, refresh schedules, and alert thresholds.
How should sensitive data be handled?
Keep credentials outside scripts, limit access, remove unnecessary personal data, and follow organizational governance requirements.