[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M69AM947EG9W5G8DAM00DBSC":3,"skill-same:01M69AM947EG9W5G8DAM00DBSC":166},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":23,"publishedAt":46,"linkOnly":47,"markdownContent":48,"markdownBody":49,"frontMatter":50,"refName":56,"commitSha":57,"skillDirectory":58,"forksCount":59,"repositoryHtmlUrl":60,"repositoryHomepageUrl":6,"ownerHtmlUrl":61,"ownerBlogUrl":62,"ownerDescription":63,"updatedAt":64,"install":65,"resources":78},"01M69AM947EG9W5G8DAM00DBSC","dataops",null,"DataOps and DS\u002FMLOps 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.","microsoft\u002Fhve-core","microsoft","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6154722?v=4","01M698VYDXZ6JW5WH3CRV650R8","Microsoft","01M69AK4J04JBRYTAKCMZ8G14R",1613,"CC-BY-4.0","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fhve-core\u002Fblob\u002Fmain\u002F.github\u002Fskills\u002Fdata-science-engineering\u002Fdataops\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量","SCENARIO","使用场景",[5,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,9,43,44,45],"projects","2026","when","tiering","invariants","categories","data-science","io","false","drift","patterns","model","testing","playbook","pipeline","data","data pipelines","code","ml","test","github","design","2026-11-10 10:12:30",false,"---\nname: dataops\ndescription: \"DataOps and DS\u002FMLOps 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.\"\nlicense: CC-BY-4.0\nuser-invocable: false\nmetadata:\n  authors: \"Microsoft (Code With Engineering Playbook); Microsoft (planning synthesis)\"\n  spec_version: \"1.0\"\n  last_updated: \"2026-09-09\"\n  content_based_on: \"https:\u002F\u002Fmicrosoft.github.io\u002Fcode-with-engineering-playbook\u002Fdesign\u002Fdesign-patterns\u002Fdata-heavy-design-guidance\u002F; https:\u002F\u002Fmicrosoft.github.io\u002Fcode-with-engineering-playbook\u002Fml-and-ai-projects\u002Ftesting-data-science-and-mlops-code\u002F; https:\u002F\u002Fmicrosoft.github.io\u002Fcode-with-engineering-playbook\u002Fml-and-ai-projects\u002Fml-model-checklist\u002F; https:\u002F\u002Fmicrosoft.github.io\u002Fcode-with-engineering-playbook\u002Fobservability\u002Fml-observability\u002F\"\n---\n\n# DataOps Reference Pack\n\n## Goal\n\nGround pipeline and test generation in the Microsoft CSE engineering playbook so that data tier semantics, validation placement, recovery invariants, and DS\u002FMLOps test technique are applied consistently and attributed accurately.\n\n## Inputs\n\n* The pipeline, transformation, validation, or test work under discussion\n* The tier of each dataset involved, when the consuming workflow records one\n* Existing test layout, package structure, and data-access boundaries\n* A data classification produced elsewhere, when sensitivity matters\n\n## Reference index\n\nRead only the reference that matches the active concern.\n\n| Reference                                                                                 | Read this when                                                                                                                                                     |\n|-------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| [data-tiers-and-pipeline-invariants.md](references\u002Fdata-tiers-and-pipeline-invariants.md) | Assigning tier meaning, placing validation, routing malformed records, or asserting replay, idempotency, testability, source-control, and configuration invariants |\n| [testing-data-science-and-mlops.md](references\u002Ftesting-data-science-and-mlops.md)         | Writing or reviewing tests for data loading, transformation, model load or predict, data validation, or model robustness                                           |\n| [validation-drift-and-observability.md](references\u002Fvalidation-drift-and-observability.md) | Distinguishing data validation from drift detection, choosing remediation, or deciding which data and model signals matter                                         |\n| [synthetic-data-operation-contract.md](references\u002Fsynthetic-data-operation-contract.md)   | Validating synthetic-data preflight authority, field lineage, conditional subgroup evidence, result linkage, or guarded local replacement                          |\n| [provenance.md](references\u002Fprovenance.md)                                                 | Confirming what is upstream guidance, what is HVE Core derivation, and where upstream is silent                                                                    |\n\n## Success criteria\n\n* Tier language distinguishes the three upstream quality tiers from the additional storage areas.\n* 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.\n* Test guidance names the operation category, its technique, and where mocking stops.\n* Validation and drift keep their distinct definitions and their distinct remediation paths.\n* Every claim traces to an attributed upstream source or is labelled as HVE Core guidance.\n* Synthetic-data generation stops before source access or writes unless its applicable\n  `SYNTHETIC_DATA_OPERATION_V1` preflight passes deterministic validation.\n\n## Constraints\n\n* This skill generates code, assertions, and review guidance. It does not execute pipelines, transformation engines, or telemetry backends.\n* 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.\n* Label HVE Core derivations as such. Do not present a derived consequence or a repository convention as upstream guidance.\n* Where upstream is silent, say so rather than inventing an upstream-sounding rule.\n\n## Ownership boundaries\n\nThis skill decides *which* data and model signals matter. It does not own the vocabulary, the classification, or the surrounding workflow.\n\n| Concern                                                                                      | Owner                   |\n|----------------------------------------------------------------------------------------------|-------------------------|\n| Metric names, instrument types, units, cardinality discipline, and the PII emission denylist | `telemetry-foundations` |\n| Data sensitivity classification and DPIA thresholds                                          | `privacy-standards`     |\n| Entity semantics, relationships, and which tier a dataset is recorded as                     | The calling workflow    |\n| Feasibility assessment and go\u002Fno-go recommendation                                           | The calling workflow    |\n\nNo repository artifact currently owns the last two rows. When the caller supplies neither, state the gap rather than deciding tier assignment or feasibility here.\n\nThis skill never decides what is sensitive. It reads a classification produced elsewhere.\n\nFor synthetic-data operations, read\n[synthetic-data-operation-contract.md](references\u002Fsynthetic-data-operation-contract.md)\nand validate records against\n[synthetic-data-operation-v1.schema.json](assets\u002Fsynthetic-data-operation-v1.schema.json).\nThe contract records qualified decisions by immutable reference; it does not store source\nvalues or replace the authority of data, privacy, Responsible AI, fairness, or domain owners.\n\n## Stop rules\n\n* Stop and route metric naming, units, and cardinality to\n  `telemetry-foundations`, the OpenTelemetry-aligned vocabulary and\n  instrumentation skill. Route data sensitivity to `privacy-standards`, the\n  privacy classification and DPIA-threshold reference.\n* 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.\n* Stop and offer the correct placement when asked to validate before Bronze landing, rather than complying or refusing without an alternative.\n\n## Attribution\n\nThis pack declares `CC-BY-4.0` because both bodies of content it holds carry that license.\n\nSource 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.\n\nContent labelled as HVE Core derivation is repository-original material under CC BY 4.0. See [provenance.md](references\u002Fprovenance.md) for the consolidated source map and derivation labels.\n","# DataOps Reference Pack\n\n## Goal\n\nGround pipeline and test generation in the Microsoft CSE engineering playbook so that data tier semantics, validation placement, recovery invariants, and DS\u002FMLOps test technique are applied consistently and attributed accurately.\n\n## Inputs\n\n* The pipeline, transformation, validation, or test work under discussion\n* The tier of each dataset involved, when the consuming workflow records one\n* Existing test layout, package structure, and data-access boundaries\n* A data classification produced elsewhere, when sensitivity matters\n\n## Reference index\n\nRead only the reference that matches the active concern.\n\n| Reference                                                                                 | Read this when                                                                                                                                                     |\n|-------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| [data-tiers-and-pipeline-invariants.md](references\u002Fdata-tiers-and-pipeline-invariants.md) | Assigning tier meaning, placing validation, routing malformed records, or asserting replay, idempotency, testability, source-control, and configuration invariants |\n| [testing-data-science-and-mlops.md](references\u002Ftesting-data-science-and-mlops.md)         | Writing or reviewing tests for data loading, transformation, model load or predict, data validation, or model robustness                                           |\n| [validation-drift-and-observability.md](references\u002Fvalidation-drift-and-observability.md) | Distinguishing data validation from drift detection, choosing remediation, or deciding which data and model signals matter                                         |\n| [synthetic-data-operation-contract.md](references\u002Fsynthetic-data-operation-contract.md)   | Validating synthetic-data preflight authority, field lineage, conditional subgroup evidence, result linkage, or guarded local replacement                          |\n| [provenance.md](references\u002Fprovenance.md)                                                 | Confirming what is upstream guidance, what is HVE Core derivation, and where upstream is silent                                                                    |\n\n## Success criteria\n\n* Tier language distinguishes the three upstream quality tiers from the additional storage areas.\n* 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.\n* Test guidance names the operation category, its technique, and where mocking stops.\n* Validation and drift keep their distinct definitions and their distinct remediation paths.\n* Every claim traces to an attributed upstream source or is labelled as HVE Core guidance.\n* Synthetic-data generation stops before source access or writes unless its applicable\n  `SYNTHETIC_DATA_OPERATION_V1` preflight passes deterministic validation.\n\n## Constraints\n\n* This skill generates code, assertions, and review guidance. It does not execute pipelines, transformation engines, or telemetry backends.\n* 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.\n* Label HVE Core derivations as such. Do not present a derived consequence or a repository convention as upstream guidance.\n* Where upstream is silent, say so rather than inventing an upstream-sounding rule.\n\n## Ownership boundaries\n\nThis skill decides *which* data and model signals matter. It does not own the vocabulary, the classification, or the surrounding workflow.\n\n| Concern                                                                                      | Owner                   |\n|----------------------------------------------------------------------------------------------|-------------------------|\n| Metric names, instrument types, units, cardinality discipline, and the PII emission denylist | `telemetry-foundations` |\n| Data sensitivity classification and DPIA thresholds                                          | `privacy-standards`     |\n| Entity semantics, relationships, and which tier a dataset is recorded as                     | The calling workflow    |\n| Feasibility assessment and go\u002Fno-go recommendation                                           | The calling workflow    |\n\nNo repository artifact currently owns the last two rows. When the caller supplies neither, state the gap rather than deciding tier assignment or feasibility here.\n\nThis skill never decides what is sensitive. It reads a classification produced elsewhere.\n\nFor synthetic-data operations, read\n[synthetic-data-operation-contract.md](references\u002Fsynthetic-data-operation-contract.md)\nand validate records against\n[synthetic-data-operation-v1.schema.json](assets\u002Fsynthetic-data-operation-v1.schema.json).\nThe contract records qualified decisions by immutable reference; it does not store source\nvalues or replace the authority of data, privacy, Responsible AI, fairness, or domain owners.\n\n## Stop rules\n\n* Stop and route metric naming, units, and cardinality to\n  `telemetry-foundations`, the OpenTelemetry-aligned vocabulary and\n  instrumentation skill. Route data sensitivity to `privacy-standards`, the\n  privacy classification and DPIA-threshold reference.\n* 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.\n* Stop and offer the correct placement when asked to validate before Bronze landing, rather than complying or refusing without an alternative.\n\n## Attribution\n\nThis pack declares `CC-BY-4.0` because both bodies of content it holds carry that license.\n\nSource 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. 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