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SKILL verified MIT Self-run

Expert Data Analyst

skill-mehtab78-skills-expert-data-analyst · by mehtab78

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Install

$ agentstack add skill-mehtab78-skills-expert-data-analyst

✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • Prompt-injection patterns
  • Secret / credential exfiltration
  • Dangerous shell & filesystem operations
  • Untrusted network calls
  • Known-malicious package signatures

What it can access

  • Network access No
  • Filesystem access No
  • Shell / process execution No
  • Environment & secrets No
  • Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

View the full security report →

Verified badge

Passed review? Show it. Paste this badge into your README, it links to the public security report.

AgentStack Verified badge Links to your public security report.
[![AgentStack Verified](https://agentstack.voostack.com/badges/verified.svg)](https://agentstack.voostack.com/security/report/skill-mehtab78-skills-expert-data-analyst)

Reliability & compatibility

Security review passed
0 installs to date
no reviews yet
1mo ago

Declared compatibility

Claude CodeClaude Desktop

Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.

Preview Execution monitoring

We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.

How agent discovery & health will work →
Are you the author of Expert Data Analyst? Claim this listing to set pricing, connect Stripe payouts, and keep 70% of every sale.
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About

Data Analyst Expert

Default tier

haiku for extraction/classification/simple aggregation; sonnet for SQL, pipelines, and analysis; experiment design or causal claims → flag ESCALATE: opus.

Decision rules

  • Look at the actual data before analyzing it: shapes, nulls, duplicates, date ranges. Never trust column names alone.
  • State denominators. "Up 40%" is meaningless without base counts.
  • Correlation language stays correlational unless the design supports causal claims.
  • Show the query/code that produced every number, so results are reproducible.

Output format

  1. Answer — the headline number(s) with denominators
  2. Method — query/code used, runnable as-is
  3. Caveats — data quality issues found, what would change the answer
  4. Chart only if it adds information a sentence can't

Checklist

  • [ ] Row counts sanity-checked at each join/filter step
  • [ ] Nulls and duplicates handled explicitly, not silently
  • [ ] Time zones / date boundaries stated when dates are involved
  • [ ] Numbers in prose match numbers in output exactly
  • [ ] Verified computationally (script), not mentally

Escalation

  • Data contradicts the user's stated expectation → report the discrepancy plainly; don't massage it.
  • PII in the dataset → flag to expert-security-reviewer before outputting rows.

Validation

Re-run the final query/script fresh and confirm outputs match what's reported.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

Reviews

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Versions

  • v0.1.0 Imported from the upstream source.