oc-data-ops
Data-pipeline harness with a Designer/Builder/Contract-Verifier loop. Owns the data estate: ingestion patterns (batch, stream, CDC), transformation layering (staging → intermediate → marts), dbt project design, and observable data contracts with freshness/volume/schema SLAs. Use for /oc-data-ops, "data pipeline", "ingestion", "ELT", "dbt", "data warehouse modeling", "data contract", "warehouse schema drift", "stale data", "data quality checks", "volume anomaly", "sync SaaS data into the warehouse", "medallion architecture", "backfill strategy". Invoked by oc-app-architect Phase 2 when discovery surfaces a data-heavy backend. Warehouse/queue choice comes from oc-stack-forge. NOT single-metric analytics (oc-signal-forge), NOT dashboards (oc-dash-forge), NOT live-pipeline engine swaps (oc-migration-ops), NOT API contract / schema-to-code drift (oc-api-dev), NOT the SaaS connector mechanics (OAuth/webhooks — oc-integrations-engineer builds the connector; this skill owns the pipeline + cont
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原文涵盖的主题
- Data Ops
- Command Reference
- Phase 1: Designer (/oc-data-ops design)
- Phase 1b: Contracts (/oc-data-ops contracts)
- Phase 2: Builder (/oc-data-ops build)
- Phase 3: Contract-Verifier (/oc-data-ops verify)
- Phase 4: Observe (/oc-data-ops observe)
- Checkpoint Integration
On first invocation, read references/orchestrator.md and follow its welcome protocol. Data Ops owns the estate level of data: how data enters the system, how it is layered and transformed, and — the part most pipeline work skips — the contracts that make each dataset's shape, freshness, and volume observable and enforceable. Where oc-signal-forge proves one metric answers one question, Data Ops builds and guards the pipelines whole families of metrics ride on. Tri-agent loop: Designer → Builder → Contract-Verifier. All three roles run in the same session, so the Verifier's separation is a discipline, not a mechanism: it grades from the contracts and the built pipeline alone — never the Builder's reasoning. Produce the pipeline architecture before any transform is written: 1. Source inventory. Every producer: app DBs, event streams, third-party exports, files. For each: change pattern (append-only, mutable, CDC-able), volume, and arrival cadence. 2. Ingestion pattern per source. Batch pull, streaming, CDC, or file-drop — chosen from the source's change pattern, not fashion. State the reason in one line each. 3. Layer map. Staging (source-shaped, immutable) → intermediate (conformed,
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- 来源仓库
- ainatx/opchain
- 源文件
- plugins/opchain/skills/oc-data-ops/SKILL.md
- 固定版本
- d4443b0fb4ef658f7b351587b2c60d228ad55620
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原作者:ainatx/opchain · 原作者仓库元数据。文档核查:未核查。最近来源检查:2026-11-10T20:01:07.841Z。
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