Skills data-engineering-data-driven-feature
📦

data-engineering-data-driven-feature

Content revision r1 Safe ⚙️ External commands

Build Data-Driven Features

Feature teams often ship changes without clear evidence or measurable outcomes. This skill guides analysis, A/B testing, instrumentation, rollout, and post-launch review.

Supports: Claude Codex Code(CC)
🥉 78 Bronze

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-engineering-data-driven-feature" from https://skillstore.io/skills/sickn33-data-engineering-data-driven-feature.md and its manifest at https://skillstore.io/api/skills/sickn33-data-engineering-data-driven-feature/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/sickn33-data-engineering-data-driven-feature.md Signed manifest GET /api/skills/sickn33-data-engineering-data-driven-feature/manifest Signed lockfile GET /api/skills/sickn33-data-engineering-data-driven-feature/lockfile

Test it

Using "data-engineering-data-driven-feature". Evaluate a new onboarding checklist for paid account activation.

Expected outcome:

A plan with baseline activation metrics, target segments, experiment hypotheses, tracking events, rollout steps, and decision criteria.

Using "data-engineering-data-driven-feature". Prepare analytics for a personalized recommendation feature.

Expected outcome:

An instrumentation plan covering exposure events, interaction events, conversion funnels, cohort analysis, and monitoring requirements.

Using "data-engineering-data-driven-feature". Review A/B test results for a pricing page change.

Expected outcome:

A decision memo comparing conversion lift, confidence intervals, revenue impact, guardrail metrics, and recommended rollout action.

Security Audit

Safe
v4 • 7/6/2026 Open versioned report

The static external-command findings are false positives caused by Markdown inline code and a YAML code fence in SKILL.md. I found no prompt injection, data exfiltration intent, or executable script behavior in the reviewed file.

1
Files scanned
183
Lines analyzed
0
Review items
0
False positives ignored

Risk Factors

⚙️ External commands (2)
SKILL.md:27 SKILL.md:138-161
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 →
Share & cite this report

Share the versioned assessment report, neutral badge, embed card, and citations. Skillstore reports evidence without deciding whether this Skill is safe.

Open versioned report
Security Assessment

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https://skillstore.io/skills/sickn33-data-engineering-data-driven-feature/audits/4?utm_source=security_passport&utm_medium=share&utm_campaign=versioned_report

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Academic citations (APA · BibTeX · CFF)

APA citation

sickn33. (2026). data-engineering-data-driven-feature security audit report (audit version 4) [Author version unspecified]. Skillstore. https://skillstore.io/skills/sickn33-data-engineering-data-driven-feature/audits/4

BibTeX citation

@techreport{sickn33-sickn33-data-engineering-data-driven-feature-2026, author = {sickn33}, title = {data-engineering-data-driven-feature security audit report (audit version 4)}, institution = {Skillstore}, year = {2026}, number = {4}, url = {https://skillstore.io/skills/sickn33-data-engineering-data-driven-feature/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: "data-engineering-data-driven-feature security audit report (audit version 4)" version: "unspecified" type: report authors: - name: "sickn33" date-released: "2026-07-06" url: "https://skillstore.io/skills/sickn33-data-engineering-data-driven-feature/audits/4" identifiers: - type: other value: "skillstore:sickn33-data-engineering-data-driven-feature:audit:4" description: "Skillstore immutable audit report identifier"

Skillstore Score

Why this score Evidence Confidence: High
55
Architecture
85
Maintainability
87
Content
68
Community
83
Spec Compliance

What You Can Build

Plan a Measured Product Feature

Turn a feature idea into hypotheses, metrics, sample-size planning, and a controlled rollout path.

Design Analytics Instrumentation

Create event schemas, funnel tracking, and data pipeline requirements before engineers begin implementation.

Assess Experiment Results

Review A/B test outcomes, segment effects, guardrail metrics, and business impact for a rollout decision.

Try These Prompts

Start Feature Discovery 
Use this skill to evaluate a proposed feature. Identify available data, baseline metrics, user segments, and missing inputs.
Design an Experiment 
Use this skill to design an A/B test for a feature. Include hypotheses, success metrics, guardrails, sample size needs, and rollout stages.
Plan Instrumented Delivery 
Use this skill to plan implementation for a data-driven feature. Cover feature flags, analytics events, pipelines, monitoring, and validation.
Analyze Launch Outcomes 
Use this skill to assess experiment results. Compare statistical impact, business value, segment behavior, guardrail metrics, and next actions.

Best Practices

  • Define primary metrics and guardrail metrics before implementation starts.
  • Validate event quality in staging before sending production traffic.
  • Use gradual rollout and rollback rules for every experiment.

Avoid

  • Do not choose metrics after reviewing experiment results.
  • Do not launch variants without validating randomization and sample size.
  • Do not treat analytics tracking as a post-launch task.

Frequently Asked Questions

What does this skill help create?
It helps create a data-backed feature plan from discovery through experiment analysis.
Does it require existing analytics data?
Existing data is strongly recommended because the workflow starts with baseline behavior and metrics.
Can it support A/B testing?
Yes. It includes experiment design, feature flags, rollout planning, monitoring, and statistical analysis.
Does it implement code directly?
It provides implementation guidance and agent prompts, but it does not include executable source code.
Which teams benefit from it?
Product, data, engineering, analytics, and growth teams can use it for measured feature delivery.
Can it replace statistical review?
No. It structures the review process, but final conclusions need valid data and qualified analysis.

Developer Details

Author

sickn33

License

MIT

Skillstore revision

r1

Version notice

The author did not declare a version.

Repository

https://github.com/sickn33/antigravity-awesome_skills/tree/main/skills/data-engineering-data-driven-feature

Ref

01171b582d636c013315c5e0d969c64f8d9cdff2

Maintenance freshness

7/18/2026

Usage

6 downloads · 91 views

File structure

📄 SKILL.md

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