Skills data-analyst
📦

data-analyst

v1.0.0 Content revision r1 Safe

Analyze Data with SQL and pandas

Data work often needs clear queries, repeatable transformations, and careful interpretation. This skill guides SQL, pandas, statistics, and data cleaning workflows.

Supports: Claude Codex Code(CC)
🥈 80 Silver

Install with my Agent

Copy this request to your Agent. It includes the canonical Skill page and manifest.

Agent request
Review the Skillstore skill "data-analyst" from https://skillstore.io/skills/shubhamsaboo-data-analyst.md and its manifest at https://skillstore.io/api/skills/shubhamsaboo-data-analyst/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.

Markdown detail GET /skills/shubhamsaboo-data-analyst.md Signed manifest GET /api/skills/shubhamsaboo-data-analyst/manifest Signed lockfile GET /api/skills/shubhamsaboo-data-analyst/lockfile

Test it

Using "data-analyst". A sales table has order dates, regions, revenue, and customer identifiers. Find monthly revenue trends.

Expected outcome:

  • Suggested monthly aggregation by order month and region.
  • Recommended checks for missing dates, duplicated orders, and uneven regional coverage.
  • Interpretation notes comparing growth rates and seasonal changes.

Using "data-analyst". A user has pandas data with missing values and product categories. Prepare it for analysis.

Expected outcome:

  • Profile missingness by column before removing records.
  • Standardize category labels and document any recoding decisions.
  • Use grouped summaries to confirm the cleaned data matches expectations.

Using "data-analyst". A team wants to compare conversion rates before and after a product change.

Expected outcome:

  • Suggested a hypothesis test for two proportions.
  • Listed assumptions, sample size checks, and possible confounding factors.
  • Explained how to report statistical and practical significance.

Security Audit

Safe
v2 • 7/7/2026 Open versioned report

Static analysis found no suspicious patterns in the single prompt-only skill file. I found no semantic evidence of prompt injection, data exfiltration intent, executable code, network access, or filesystem abuse.

1
Files scanned
58
Lines analyzed
0
Review items
0
False positives ignored
No confirmed security findings were detected by the latest completed static and semantic audit. This does not prove the skill has no side effects.
Audited by: codex View Audit History →
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Open versioned report
Security Assessment

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Academic citations (APA · BibTeX · CFF)

APA citation

shubhamsaboo. (2026). data-analyst security audit report (audit version 2) [Author version 1.0.0]. Skillstore. https://skillstore.io/skills/shubhamsaboo-data-analyst/audits/2

BibTeX citation

@techreport{shubhamsaboo-shubhamsaboo-data-analyst-2026, author = {shubhamsaboo}, title = {data-analyst security audit report (audit version 2)}, institution = {Skillstore}, year = {2026}, number = {2}, url = {https://skillstore.io/skills/shubhamsaboo-data-analyst/audits/2}, note = {Author version 1.0.0} }

CITATION.cff

cff-version: 1.2.0 message: "If you use this Skill, cite its author and this versioned security audit report." title: "data-analyst security audit report (audit version 2)" version: "1.0.0" type: report authors: - name: "shubhamsaboo" date-released: "2026-07-07" url: "https://skillstore.io/skills/shubhamsaboo-data-analyst/audits/2" identifiers: - type: other value: "skillstore:shubhamsaboo-data-analyst:audit:2" description: "Skillstore immutable audit report identifier"

Skillstore Score

Why this score Evidence Confidence: Medium
55
Architecture
100
Maintainability
87
Content
65
Community
83
Spec Compliance

What You Can Build

Build Reporting Queries

Create SQL queries for metrics, joins, aggregations, and window functions across business tables.

Clean Research Data

Plan pandas steps for missing values, reshaping, time series preparation, and exploratory summaries.

Review Product Metrics

Interpret trends, correlations, and test results before sharing decisions with stakeholders.

Try These Prompts

Explore a Dataset 
Review this dataset description and suggest SQL or pandas steps for profiling columns, missing values, and useful summaries: [dataset details].
Write a Query 
Write a SQL query for this schema and question. Include joins, filters, aggregations, and comments: [schema and business question].
Design an Analysis Plan 
Create an analysis plan for this hypothesis. Include cleaning steps, statistical tests, assumptions to check, and interpretation guidance: [hypothesis and data].
Optimize a Workflow 
Review this SQL or pandas workflow for correctness, performance risks, and clearer outputs. Recommend improvements with reasoning: [workflow details].

Best Practices

  • Share schemas, column meanings, and sample rows before requesting queries or transformations.
  • Ask for assumptions and interpretation, not only query or pandas syntax.
  • Validate results with row counts, missing value checks, and known business totals.

Avoid

  • Do not request production-ready conclusions from undocumented or incomplete data.
  • Do not skip validation checks after cleaning, joining, or reshaping data.
  • Do not treat correlation, model output, or test results as causation without support.

Frequently Asked Questions

Can this skill write SQL queries?
Yes. It can draft queries using joins, CTEs, window functions, aggregations, filters, and optimization notes.
Can it help with pandas?
Yes. It can guide grouping, filtering, pivoting, time series analysis, and missing data handling.
Does it execute database queries?
No. It provides guidance and code suggestions, but execution depends on the connected environment.
What information should I provide?
Provide schemas, column meanings, sample rows, data size, desired metrics, and the database or pandas context.
Can it perform statistical analysis?
Yes. It supports descriptive statistics, hypothesis testing, correlation analysis, and basic predictive modeling guidance.
Is it safe to publish?
The reviewed skill is prompt-only, with no static or semantic security findings.

Developer Details

License

MIT

Author version

v1.0.0

Skillstore revision

r1

Repository

https://github.com/shubhamsaboo/awesome-llm-apps/tree/main/awesome_agent_skills/data-analyst

Ref

412e23e39ccd9729546286287b96bb41707397cf

Maintenance freshness

7/18/2026

Usage

3 downloads · 0 views

File structure

📄 SKILL.md