{"id":"a6b3987f-6494-48d7-b306-12edf5ebd033","slug":"clawhub-archlab-space-data-pipeline-design-review","name":"Data Pipeline Design Review","description":"Use when a data engineer needs a structured design review of a proposed data pipeline, ETL/ELT flow, or dbt/SQL model before it ships. Produces severity-rate...","canonicalUrl":"https://www.xpersona.co/agent/clawhub-archlab-space-data-pipeline-design-review","sourceUrl":"https://clawhub.ai/archlab-space/data-pipeline-design-review","homepage":"https://clawhub.ai/archlab-space/skills/data-pipeline-design-review","source":"CLAWHUB","vendor":{"slug":"clawhub","label":"Clawhub","url":"https://clawhub.ai/archlab-space/skills/data-pipeline-design-review"},"protocols":["OPENCLEW"],"capabilities":[],"trustScore":null,"trustConfidence":"unknown","artifactCount":0,"benchmarkCount":0,"lastRelease":"0.1.3","freshnessAt":"2026-10-11T21:31:51.987Z","freshnessLabel":"Oct 11, 2026","securityReviewed":true,"openapiReady":false,"stats":[{"label":"Trust score","value":"Unknown"},{"label":"Compatibility","value":"OpenClaw"},{"label":"Freshness","value":"Oct 11, 2026"},{"label":"Vendor","value":"Clawhub"},{"label":"Artifacts","value":"0"},{"label":"Benchmarks","value":"0"},{"label":"Last release","value":"0.1.3"}],"factsPreview":[{"factKey":"vendor","category":"vendor","label":"Vendor","value":"Clawhub","href":"https://clawhub.ai/archlab-space/skills/data-pipeline-design-review","sourceUrl":"https://clawhub.ai/archlab-space/skills/data-pipeline-design-review","sourceType":"profile","confidence":"medium","observedAt":"2026-10-11T21:31:52.059Z","isPublic":true},{"factKey":"protocols","category":"compatibility","label":"Protocol compatibility","value":"OpenClaw","href":"https://www.xpersona.co/api/v1/agents/clawhub-archlab-space-data-pipeline-design-review/contract","sourceUrl":"https://www.xpersona.co/api/v1/agents/clawhub-archlab-space-data-pipeline-design-review/contract","sourceType":"contract","confidence":"medium","observedAt":"2026-10-11T21:31:52.059Z","isPublic":true},{"factKey":"traction","category":"adoption","label":"Adoption signal","value":"990 downloads","href":"https://clawhub.ai/archlab-space/data-pipeline-design-review","sourceUrl":"https://clawhub.ai/archlab-space/data-pipeline-design-review","sourceType":"profile","confidence":"medium","observedAt":"2026-10-11T21:31:52.059Z","isPublic":true},{"factKey":"latest_release","category":"release","label":"Latest release","value":"0.1.3","href":"https://clawhub.ai/archlab-space/data-pipeline-design-review","sourceUrl":"https://clawhub.ai/archlab-space/data-pipeline-design-review","sourceType":"release","confidence":"medium","observedAt":"2026-05-28T09:47:08.275Z","isPublic":true},{"factKey":"handshake_status","category":"security","label":"Handshake status","value":"UNKNOWN","href":"https://www.xpersona.co/api/v1/agents/clawhub-archlab-space-data-pipeline-design-review/trust","sourceUrl":"https://www.xpersona.co/api/v1/agents/clawhub-archlab-space-data-pipeline-design-review/trust","sourceType":"trust","confidence":"medium","observedAt":null,"isPublic":true}],"highlights":["990 downloads","Trust evidence available"],"agentCard":{"name":"Data Pipeline Design Review","description":"Use when a data engineer needs a structured design review of a proposed data pipeline, ETL/ELT flow, or dbt/SQL model before it ships. Produces severity-rate...","source":"CLAWHUB","sourceId":"clawhub:s176qz6rwtpzj9gk93r7b3jm6984ty2d:data-pipeline-design-review","homepage":"https://clawhub.ai/archlab-space/skills/data-pipeline-design-review","repository":"https://clawhub.ai/archlab-space/data-pipeline-design-review","documentation":"https://www.xpersona.co/agent/clawhub-archlab-space-data-pipeline-design-review","protocols":["OPENCLEW"],"examples":[{"kind":"example","language":"text","snippet":"DATA PIPELINE DESIGN REVIEW\nArtifact: <architecture | dbt/SQL model | streaming flow>\nScope reviewed: <one line>\n\nASSUMPTIONS\n- <missing context treated as assumed>\n\nFINDINGS\n[CRITICAL] <title>\n  Dimension: <one of the six>\n  Failure scenario: <concrete way this breaks in production>\n  Recommendation: <minimal fix>\n[HIGH] ...\n[MEDIUM] ...\n[LOW] ...\n\nDIMENSION COVERAGE\n- Correctness & grain: <assessed / not assessable — why>\n- Idempotency & recovery: <...>\n- Data quality: <...>\n- Schema evolution: <...>\n- Observability: <...>\n- Cost & performance: <...>\n\nREMEDIATION CHECKLIST (ordered by severity)\n1. [ ] <action>\n2. [ ] <action>\n\nRECOMMENDATION: GO | GO WITH CONDITIONS | NO-GO\nRationale: <2–3 sentences>"},{"kind":"example","language":"text","snippet":"DATA PIPELINE DESIGN REVIEW\nArtifact: <architecture | dbt/SQL model | streaming flow>\nScope reviewed: <one line>\n\nASSUMPTIONS\n- <missing context treated as assumed>\n\nFINDINGS\n[CRITICAL] <title>\n  Dimension: <one of the six>\n  Failure scenario: <concrete way this breaks in production>\n  Recommendation: <minimal fix>\n[HIGH] ...\n[MEDIUM] ...\n[LOW] ...\n\nDIMENSION COVERAGE\n- Correctness & grain: <assessed / not assessable — why>\n- Idempotency & recovery: <...>\n- Data quality: <...>\n- Schema evolution: <...>\n- Observability: <...>\n- Cost & performance: <...>\n\nREMEDIATION CHECKLIST (ordered by severity)\n1. [ ] <action>\n2. [ ] <action>\n\nRECOMMENDATION: GO | GO WITH CONDITIONS | NO-GO\nRationale: <2–3 sentences>"}]}}