📦
content-creator
88Create Brand-Consistent Marketing Content
Marketing teams need content that stays consistent across channels. This skill helps plan, write, analyze, and optimize content for brand voice and SEO.
Writing & Content View
Skills data-engineer
📦
data-engineer
Content revision r2 Safe
Design Reliable Data Platforms
Data teams need reliable architectures that meet scale, latency, governance, and cost requirements. This skill turns those requirements into pipeline designs, implementation plans, quality controls, and operating guidance.
Supports: Claude Codex Code(CC)
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-engineer" from https://skillstore.io/skills/sickn33-data-engineer.md and its manifest at https://skillstore.io/api/skills/sickn33-data-engineer/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.
Test it
Using "data-engineer". Design a daily pipeline from PostgreSQL to BigQuery for finance reporting.
Expected outcome:
- Architecture: incremental extraction, object storage staging, warehouse loading, dbt transformations, and scheduled orchestration.
- Controls: source reconciliation, schema validation, freshness checks, duplicate detection, and protected finance access.
- Operations: retries, backfills, lineage, cost monitoring, alert ownership, and recovery procedures.
Using "data-engineer". Plan a Kafka pipeline for late and out-of-order delivery.
Expected outcome:
- Processing plan: event-time windows, watermarks, durable checkpoints, idempotent sinks, and a dead-letter path.
- Reliability plan: schema compatibility, replay testing, lag alerts, duplicate metrics, and documented recovery objectives.
Using "data-engineer". Compare Snowflake and BigQuery for a multi-team analytics platform.
Expected outcome:
A structured comparison covering workload fit, scaling, governance, ecosystem integration, administration, migration effort, and cost assumptions, followed by a justified recommendation.
Security Audit
Safev5 • 7/23/2026 Open versioned report
The sole static finding is a false positive caused by a list of real-time analytics products in SKILL.md. No system reconnaissance instructions, prompt injection, or other malicious intent were found.
1
Files scanned
228
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 →
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.
Copy report link
https://skillstore.io/skills/sickn33-data-engineer/audits/5?utm_source=security_passport&utm_medium=share&utm_campaign=versioned_reportMarkdown badge
[](https://skillstore.io/skills/sickn33-data-engineer?utm_source=security_passport_badge)HTML badge
<a href="https://skillstore.io/skills/sickn33-data-engineer?utm_source=security_passport_badge"><img src="https://skillstore.io/badges/skills/sickn33-data-engineer/security.svg" alt="Skillstore security assessment" loading="lazy"></a>Embed card
<iframe src="https://skillstore.io/embed/skills/sickn33-data-engineer.html" title="Skillstore Security Assessment" sandbox="allow-popups allow-popups-to-escape-sandbox" loading="lazy" referrerpolicy="no-referrer" width="420" height="180"></iframe>Academic citations (APA · BibTeX · CFF)
APA citation
sickn33. (2026). data-engineer security audit report (audit version 5) [Author version unspecified]. Skillstore. https://skillstore.io/skills/sickn33-data-engineer/audits/5BibTeX citation
@techreport{sickn33-sickn33-data-engineer-2026,
author = {sickn33},
title = {data-engineer security audit report (audit version 5)},
institution = {Skillstore},
year = {2026},
number = {5},
url = {https://skillstore.io/skills/sickn33-data-engineer/audits/5},
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-engineer security audit report (audit version 5)"
version: "unspecified"
type: report
authors:
- name: "sickn33"
date-released: "2026-07-23"
url: "https://skillstore.io/skills/sickn33-data-engineer/audits/5"
identifiers:
- type: other
value: "skillstore:sickn33-data-engineer:audit:5"
description: "Skillstore immutable audit report identifier"
Compare variants
2 installable variantsEach author remains a separate installable skill. The recommended variant is ranked by Skillstore evidence.
Why this variant is first
Highest Skillstore Score
Author / source Skillstore Score Evidence Confidence Skillstore usage Updated
sickn33-data-engineer
Skillstore Score 79
Evidence Confidence High
Skillstore usage 10
Updated
2026-08-21
zl2023github-data-engineer
Skillstore Score 69
Evidence Confidence Medium
Skillstore usage 5
Updated
2026-08-21
Skillstore Score
Why this score Evidence Confidence: High 55
Architecture
85
Maintainability
87
Content
68
Community
91
Spec Compliance
What You Can Build
Build an Analytics Warehouse
Plan ingestion, dbt transformations, dimensional models, tests, lineage, and orchestration for a governed analytics warehouse.
Design a Streaming Platform
Select messaging, processing, storage, schema evolution, monitoring, and recovery patterns for low-latency event workloads.
Modernize Data Governance
Define quality controls, catalogs, access policies, privacy safeguards, retention rules, and audit processes across data products.
Try These Prompts
Plan a Basic Batch Pipeline
Recommend a batch pipeline from [source] to [warehouse]. Use [daily volume], [refresh target], and [cloud]. Explain components, checks, and operations.
Design a Warehouse Model
Design a dimensional model for [business process]. Include grain, facts, dimensions, history handling, incremental loads, tests, lineage, and key assumptions.
Architect a Streaming Pipeline
Architect a streaming pipeline for [event source] at [throughput] and [latency]. Address schemas, ordering, duplicates, late data, recovery, monitoring, and cost.
Evaluate an Enterprise Data Platform
Compare [options] for [workload portfolio]. Score scalability, reliability, governance, security, migration effort, operating complexity, and cost. Recommend a phased architecture.
Best Practices
- Provide data volumes, latency targets, retention, consistency, recovery objectives, and expected growth.
- Specify current tools, cloud constraints, source schemas, sink requirements, team skills, and budget limits.
- Request quality, security, governance, observability, cost, and failure recovery requirements in every production design.
Avoid
- Do not request a tool choice without describing workloads, service levels, constraints, and ownership.
- Do not deploy generated configurations before testing permissions, data correctness, failure behavior, and rollback procedures.
- Do not optimize only for throughput while ignoring quality, privacy, operational complexity, and total cost.
Frequently Asked Questions
Does this skill deploy data infrastructure?
No. It produces architecture and implementation guidance that teams must review, test, and deploy in their own environments.
Which data tools can it cover?
It covers common tools for Spark, dbt, Airflow, Kafka, Flink, warehouses, lakehouses, catalogs, quality systems, and cloud data services.
Can it design for AWS, Azure, and Google Cloud?
Yes. It can compare and design with data services across all three platforms when requirements and constraints are supplied.
What information improves the recommendations?
Provide source types, schemas, volumes, growth, latency, retention, users, compliance needs, reliability targets, existing tools, and budget.
Does it address security and governance?
Yes. It can include encryption, least privilege, masking, lineage, catalogs, retention, audit logging, and compliance considerations.
Is it suitable for exploratory data analysis?
Not as the primary task. Use it when analysis depends on designing, operating, or improving data pipelines and platforms.
Developer Details
Author
sickn33License
MIT
Skillstore revision
r2
Version notice
The author did not declare a version.
Ref
f9e2c34b4f19c7f3e6b0a1e93227b5f77cc12526
Maintenance freshness
7/26/2026
Usage
8 downloads · 98 views
File structure
📄 SKILL.md
More from sickn33
View all📦
prompt-engineering-patterns
85Improve LLM Prompts With Proven Patterns
Inconsistent prompts waste time and make AI outputs hard to trust. This skill guides prompt design with reusable patterns, examples, evaluation steps, and optimization workflows.
Productivity View
📦
senior-fullstack
82Build Fullstack Apps with Senior Patterns
Teams need consistent setup, architecture, and review guidance for modern web applications. This skill provides scaffolders, workflow references, and quality prompts for React, Next.js, Node.js, GraphQL, and PostgreSQL projects.
Coding & Development View
📦
senior-architect
81Design Scalable Software Architectures
Architecture decisions are hard to compare across web, mobile, backend, and cloud systems. This skill provides structured guides and local scaffold scripts for reports, dependency review, and trade-off documentation.
Coding & Development View
📦
prompt-engineer
81Optimize AI Prompts With Prompt Engineer
Writing clear prompts is hard when goals are vague or complex. This skill turns rough requests into structured prompts using proven prompt frameworks.
Productivity View
📦
product-manager-toolkit
80Prioritize Product Work With Research Insights
Product teams need faster ways to rank features, synthesize interviews, and document decisions. This skill provides RICE scoring, interview analysis, and PRD templates for structured planning.
Productivity View
Related Skills
View all📦
senior-data-engineer
81Build Production Data Pipelines
by alirezarezvani
Production data teams need reliable patterns for pipelines, quality checks, and orchestration. This skill guides architecture, scaffolding, and DataOps practices for modern data stacks.
Data & Analytics View
📦
parquet-optimization
80Optimize Parquet Performance in Rust
by EmilLindfors
Parquet pipelines can waste storage and memory when defaults are used. This skill reviews Rust Parquet reads and writes, then suggests compression, sizing, projection, and filtering improvements.
Data & Analytics View
📦
datafusion-query-advisor
80Optimize DataFusion Queries
by EmilLindfors
Slow DataFusion queries can waste compute and hide simple plan issues. This skill reviews SQL and DataFrame patterns, then suggests concrete performance improvements.
Data & Analytics View
📦
spark-optimization
79Optimize Apache Spark Jobs Faster
by wshobson
Slow Spark jobs waste cluster budget and delay data delivery. This skill guides Claude, Codex, and Claude Code through partitioning, shuffle, memory, join, caching, and monitoring improvements.
Data & Analytics View
📦
data-orchestrator
79Coordinate Data Pipelines and Quality Checks
by Brownbull
Data pipeline work can become inconsistent when ingestion, features, quality, and reporting are planned separately. This skill organizes data tasks around pipeline context, quality gates, lineage, and handoffs.
Data & Analytics View
📦
airflow-dag-patterns
77Build Production Airflow DAGs
by wshobson
Airflow DAGs can fail when dependencies, retries, and sensors are designed poorly. This skill gives practical patterns for reliable pipeline orchestration.
Data & Analytics View