Dataops

MicrosoftOfficialPublished by Microsoft
Skill
Guidelines
Software development
CC-BY-4.0
Low risk
Has scripts
From the author

DataOps and DS/MLOps testing reference for data tiering, Bronze-to-Silver validation placement, pipeline invariants, pytest categories, and validation-versus-drift. Use when designing, reviewing, or generating data pipelines, transformation code, data validation, or data-science test suites.
— the description by Microsoft
AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author
The dataops skill provides a structured reference for designing and reviewing data pipelines and machine learning workflows. It synthesizes engineering best practices regarding data quality tiers (Bronze, Silver, Gold), the strategic placement of validation logic, and the importance of maintaining faithful source copies for pipeline replayability. The skill helps teams distinguish between data validation and drift detection, ensuring that data-heavy systems are robust, testable, and maintainable.
What it does

Defines data tier semantics for Bronze, Silver, and Gold storage layers.
Provides rules for placing validation at the Bronze-to-Silver boundary.
Explains the rationale for maintaining immutable, faithful source copies in the Bronze tier.
Offers guidance on distinguishing between data validation and drift detection.
Provides a framework for testing data science and MLOps code.
Includes a contract for synthetic data operations to ensure safe preflight validation.
When to use it

When designing the architecture of a new data pipeline or lakehouse.
When reviewing data transformation code or pipeline test suites.
When deciding where to implement data validation checks.
When troubleshooting pipeline failures that require data replay or recovery.
When establishing testing strategies for machine learning models and data loading processes.
Advantages

Ensures consistent application of data quality and pipeline invariants.
Promotes robust recovery strategies through faithful data ingestion.
Clarifies the distinction between validation and drift for better observability.
Provides clear, attributed guidance based on established engineering playbooks.
Prevents common pitfalls like premature validation before data landing.
Usage examples

“Where should I place validation logic in my ETL pipeline?”: The skill advises placing validation at the Bronze-to-Silver boundary, explaining that Bronze should remain a faithful, immutable copy of the source to support replayability and recovery.
“What is the difference between data validation and drift detection?”: The skill provides a breakdown distinguishing data validation (schema and invariant checks) from drift detection (monitoring changes in data or model signals over time).
Install

Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/microsoft/dataops/download
Unzip it into the cross-client skills folder of your project:
```
mkdir -p .agents/skills && unzip dataops.zip -d .agents/skills
```
Documentation by Microsoft

DataOps Reference Pack

Goal

Ground pipeline and test generation in the Microsoft CSE engineering playbook so that data tier semantics, validation placement, recovery invariants, and DS/MLOps test technique are applied consistently and attributed accurately.
Inputs

The pipeline, transformation, validation, or test work under discussion
The tier of each dataset involved, when the consuming workflow records one
Existing test layout, package structure, and data-access boundaries
A data classification produced elsewhere, when sensitivity matters
Reference index

Read only the reference that matches the active concern.
Reference	Read this when
data-tiers-and-pipeline-invariants.md	Assigning tier meaning, placing validation, routing malformed records, or asserting replay, idempotency, testability, source-control, and configuration invariants
testing-data-science-and-mlops.md	Writing or reviewing tests for data loading, transformation, model load or predict, data validation, or model robustness
validation-drift-and-observability.md	Distinguishing data validation from drift detection, choosing remediation, or deciding which data and model signals matter
synthetic-data-operation-contract.md	Validating synthetic-data preflight authority, field lineage, conditional subgroup evidence, result linkage, or guarded local replacement
provenance.md	Confirming what is upstream guidance, what is HVE Core derivation, and where upstream is silent
Success criteria

Tier language distinguishes the three upstream quality tiers from the additional storage areas.
Validation is placed at the Bronze-to-Silver boundary, and the faithful-copy rationale with both replay purposes travels with that placement whether or not the request challenges it.
Test guidance names the operation category, its technique, and where mocking stops.
Validation and drift keep their distinct definitions and their distinct remediation paths.
Every claim traces to an attributed upstream source or is labelled as HVE Core guidance.
Synthetic-data generation stops before source access or writes unless its applicable SYNTHETIC_DATA_OPERATION_V1 preflight passes deterministic validation.
Constraints

This skill generates code, assertions, and review guidance. It does not execute pipelines, transformation engines, or telemetry backends.
Reproduce only the minimum text necessary for a specific technical point, and paraphrase everything else. Attribute every reference and describe accurately what each reference reproduces.
Label HVE Core derivations as such. Do not present a derived consequence or a repository convention as upstream guidance.
Where upstream is silent, say so rather than inventing an upstream-sounding rule.
Ownership boundaries

This skill decides which data and model signals matter. It does not own the vocabulary, the classification, or the surrounding workflow.
Concern	Owner
Metric names, instrument types, units, cardinality discipline, and the PII emission denylist	`telemetry-foundations`
Data sensitivity classification and DPIA thresholds	`privacy-standards`
Entity semantics, relationships, and which tier a dataset is recorded as	The calling workflow
Feasibility assessment and go/no-go recommendation	The calling workflow
No repository artifact currently owns the last two rows. When the caller supplies neither, state the gap rather than deciding tier assignment or feasibility here.
This skill never decides what is sensitive. It reads a classification produced elsewhere.
For synthetic-data operations, read synthetic-data-operation-contract.md and validate records against synthetic-data-operation-v1.schema.json. The contract records qualified decisions by immutable reference; it does not store source values or replace the authority of data, privacy, Responsible AI, fairness, or domain owners.
Stop rules

Stop and route metric naming, units, and cardinality to telemetry-foundations, the OpenTelemetry-aligned vocabulary and instrumentation skill. Route data sensitivity to privacy-standards, the privacy classification and DPIA-threshold reference.
Stop and state the gap when the request depends on guidance the playbook does not provide, such as a drift threshold or an alerting policy.
Stop and offer the correct placement when asked to validate before Bronze landing, rather than complying or refusing without an alternative.
Attribution

This pack declares CC-BY-4.0 because both bodies of content it holds carry that license.
Source pages are Microsoft CSE Code With Engineering Playbook documentation licensed CC BY 4.0. The upstream project applies MIT through a separate LICENSE-CODE file to code samples only, which this pack does not reproduce. The references derive from those documentation pages and have been changed: upstream guidance is paraphrased, and only identifiers and structural names are carried across as facts. THIRD-PARTY-NOTICES carries the attribution CC BY 4.0 requires and states that the content has been changed. Each reference cites its own upstream URL and states what it reproduces.
Content labelled as HVE Core derivation is repository-original material under CC BY 4.0. See provenance.md for the consolidated source map and derivation labels.
Files

ATTRIBUTION.mdAdded by AutoGineer965 B
LICENSEAdded by AutoGineer16.6 KiB
SKILL.md7.6 KiB
assets/synthetic-data-operation-v1.schema.json9.2 KiB
pyproject.toml681 B
references/data-tiers-and-pipeline-invariants.md10.2 KiB
references/provenance.md10.8 KiB
references/synthetic-data-operation-contract.md6.4 KiB
references/testing-data-science-and-mlops.md8.8 KiB
references/validation-drift-and-observability.md7.2 KiB
scripts/synthetic_data_operation.pyScript21.2 KiB
tests/corpus/0_empty0 B
tests/corpus/1_empty_object2 B
tests/corpus/2_non_object_json2 B
tests/corpus/3_minimal_preflight27 B
tests/fuzz_harness.pyScript1.2 KiB
tests/test_synthetic_data_operation.pyScript23.9 KiB
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Requirements and compatibility

Written for: GitHub Copilot, Claude Code
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Claude Code frontmatter, Needs Python
Security

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Attribution and licence

Published by
Microsoft
Publisher
Microsoft (Official)
Source
microsoft/hve-core · .github/skills/data-science-engineering/dataops · commit f425e94
Licence
Creative Commons Attribution 4.0 International (redistribution allowed)
LICENSE (written by AutoGineer)
ATTRIBUTION.md (written by AutoGineer)
Synced
2026-11-08
AutoGineer is not affiliated with or endorsed by Microsoft.
Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download dataops.zip