data-orchestrator
Coordinate Data Pipelines and Quality Checks
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.
Install with my Agent
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Review the Skillstore skill "data-orchestrator" from https://skillstore.io/skills/brownbull-data-orchestrator.md and its manifest at https://skillstore.io/api/skills/brownbull-data-orchestrator/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-orchestrator". Plan a daily aggregation pipeline for transactions and customer records.
Expected outcome:
The plan identifies source dependencies, validation rules, recovery checkpoints, data quality gates, lineage updates, and documentation changes.
Using "data-orchestrator". Review a churn risk feature before release.
Expected outcome:
- Confirm source dependencies and business logic ownership.
- Add completeness, accuracy, consistency, timeliness, uniqueness, and validity checks.
- Define monitoring for feature drift, freshness, and downstream model impact.
Using "data-orchestrator". Coordinate a pipeline completion event with analytics and backend teams.
Expected outcome:
The output explains expected handoffs, reporting updates, API contract checks, cache considerations, and quality score review.
Security Audit
SafeAll 20 static findings are false positives from Markdown code fences, inline skill names, and ordinary data-quality wording in SKILL.md. No prompt injection, exfiltration intent, credential access, or executable command guidance was found, so no semantic findings were added.
Risk Factors
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https://skillstore.io/skills/brownbull-data-orchestrator/audits/9?utm_source=security_passport&utm_medium=share&utm_campaign=versioned_reportMarkdown badge
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<a href="https://skillstore.io/skills/brownbull-data-orchestrator?utm_source=security_passport_badge"><img src="https://skillstore.io/badges/skills/brownbull-data-orchestrator/security.svg" alt="Skillstore security assessment" loading="lazy"></a>Embed card
<iframe src="https://skillstore.io/embed/skills/brownbull-data-orchestrator.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
Brownbull. (2026). data-orchestrator security audit report (audit version 9) [Author version unspecified]. Skillstore. https://skillstore.io/skills/brownbull-data-orchestrator/audits/9BibTeX citation
@techreport{brownbull-brownbull-data-orchestrator-2026,
author = {Brownbull},
title = {data-orchestrator security audit report (audit version 9)},
institution = {Skillstore},
year = {2026},
number = {9},
url = {https://skillstore.io/skills/brownbull-data-orchestrator/audits/9},
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-orchestrator security audit report (audit version 9)"
version: "unspecified"
type: report
authors:
- name: "Brownbull"
date-released: "2026-07-06"
url: "https://skillstore.io/skills/brownbull-data-orchestrator/audits/9"
identifiers:
- type: other
value: "skillstore:brownbull-data-orchestrator:audit:9"
description: "Skillstore immutable audit report identifier"
Skillstore Score
Why this score Evidence Confidence: HighWhat You Can Build
Plan Reliable ETL Work
Define sources, dependencies, validation, recovery, and monitoring before building a data pipeline.
Coordinate Reporting Changes
Align metrics, feature definitions, dashboard freshness, and documentation before releasing analytics updates.
Prepare Feature Workflows
Plan dependencies, versioning, edge cases, monitoring, and quality checks for model inputs.
Try These Prompts
Plan an ETL task for [source] to [destination]. Include sources, dependencies, validation checks, quality gates, and documentation updates.
Review this proposed feature workflow: [details]. Identify dependencies, lineage needs, quality tests, monitoring, and team handoffs.
Create an orchestration plan for a batch and stream workflow with [SLA]. Include context, assignments, tests, and success metrics.
Audit our data program using this guidance. Compare pipelines, features, governance, quality checks, events, and anti-patterns. Return prioritized fixes.
Best Practices
- Validate inputs, transformations, and outputs before marking a data task complete.
- Track lineage, ownership, and documentation for every pipeline and feature change.
- Set measurable quality gates before downstream teams consume data.
Avoid
- Skipping validation because a pipeline completed successfully.
- Creating feature logic without documenting dependencies and business rules.
- Treating monitoring and recovery as optional follow-up work.