← Data Engineering CopilotCONTENT HISTORY

Update to Data Engineering Copilot

Snapshot Nov 3, 2026 · 00:12 UTC · version 0.2.0

WHAT CHANGED

Declared capabilities changed

Declared skills — Now says: [{"description":"Implement or improve a SQL, Python, PySpark or orchestration pipeline with version-aware safe writes and test...

Observed in instructions or declared skills. Runtime behavior has not been tested.

Save Data Engineering Copilot and check future changes in Following.

Up to 10 plugins free · no card required.

All fields and original values.

Declared skills

Before

Oct 27, 2026 · 00:42 UTC

[{"description":"Production-focused workflow for designing, debugging, recovering, tuning, and operating data pipelines across Spark, Databricks, Delta Lake, SQL, CDC, Structured Streaming, Airflow, backfills, quality, observability, and...

After

Nov 3, 2026 · 00:12 UTC

[{"description":"Implement or improve a SQL, Python, PySpark or orchestration pipeline with version-aware safe writes and test evidence.","interface":{"brand_color":null,"default_prompt":null,"display_name":"build-data-pipeline","icon_la...

Short description

Before

Oct 27, 2026 · 00:42 UTC

Debug production data systems

After

Nov 3, 2026 · 00:12 UTC

Build reliable data pipelines

Long description

Before

Oct 27, 2026 · 00:42 UTC

Data Engineering Copilot helps you design, debug, recover, and improve production data systems across Spark, Databricks, Delta Lake, SQL, CDC, Structured Streaming, Airflow, backfills, data quality, observability, and lakehouse architect...

After

Nov 3, 2026 · 00:12 UTC

Design, build, debug, recover and optimize dependable data pipelines and platforms. Work across Apache Spark, Databricks, Delta Lake, structured streaming, CDC, orchestration, dbt, SQL warehouses, Microsoft Fabric/OneLake, data quality, ...

Full description

Before

Oct 27, 2026 · 00:42 UTC

Production data engineering architecture, debugging, recovery, and reliability workflows.

After

Nov 3, 2026 · 00:12 UTC

Production data-engineering design, implementation, debugging, recovery, reliability and safe data operations.

Compare saved observations

Download comparison JSON
Full technical diff · 18 changed fields

changed /release/description

BEFORE
"Production data engineering architecture, debugging, recovery, and reliability workflows."
AFTER
"Production data-engineering design, implementation, debugging, recovery, reliability and safe data operations."

changed /release/id

BEFORE
"pluginrel_d255d856c2248191bf1362c3046b509c"
AFTER
"pluginrel_a2def7072ab08191b0c6fcbe977e9c18"

changed /release/interface/brand_color

BEFORE
null
AFTER
"#183044"

changed /release/interface/capabilities

BEFORE
[
  "Design reliable batch, CDC, streaming, and lakehouse architectures",
  "Debug failed, slow, duplicated, stale, or backlogged production pipelines",
  "Plan safe replay, backfill, recovery, and checkpoint strategies",
  "Review Delta MERGE logic for keys, ordering, duplicates, updates, and deletes",
  "Diagnose Spark and Databricks performance using plans, shuffle, spill, skew, and file evidence",
  "Design Structured Streaming state, watermark, checkpoint, and sink behavior",
  "Create production-oriented SQL, PySpark, Databricks, and Airflow patterns",
  "Build data-quality gates, reconciliation, freshness checks, and operational runbooks",
  "Evaluate retry safety, idempotency, schema evolution, and downstream contracts",
  "Use current official docs for Spark, Databricks, Delta Lake, Airflow, and runtime-specific behavior"
]
AFTER
[
  "Architect batch, CDC, streaming, lakehouse and warehouse data systems",
  "Debug duplicates, data loss, latency, failures and stale pipelines from evidence",
  "Plan safe backfills, replay, checkpoint recovery and rollback strategies",
  "Review CDC merge ordering, delete propagation and idempotent writes",
  "Diagnose Spark and Databricks query plans, shuffle, skew, spill and file layout",
  "Design Microsoft Fabric Lakehouse, OneLake, shortcuts and deployment flows",
  "Design dbt/SQL transformations, model grain, incremental updates and contracts",
  "Review Airflow orchestration, retries, scheduling and backfill safety",
  "Build data quality, freshness, reconciliation and observability checks",
  "Assess access control, governance, PII handling and data lineage",
  "Coordinate supported specialist agents and evidence-based verification",
  "Prepare version-aware implementation plans and release/incident evidence"
]

changed /release/interface/composer_icon_url

BEFORE
"https://files.openai.com/content?id=file_0000000010dc81fb9a972b5977355f9a"
AFTER
"https://files.openai.com/content?id=file_00000000f5fc82068a4f9b67b62c8953"

changed /release/interface/default_prompt

BEFORE
"Debug why this production pipeline is failing or producing wrong data."
AFTER
"Diagnose this failing or incorrect pipeline using logs, code and data-quality evidence."

changed /release/interface/default_prompts

BEFORE
[
  "Debug why this production pipeline is failing or producing wrong data.",
  "Design a reliable CDC or streaming pipeline for this use case.",
  "Plan a safe backfill or replay without duplicating or losing data."
]
AFTER
[
  "Diagnose this failing or incorrect pipeline using logs, code and data-quality evidence.",
  "Design a reliable CDC or lakehouse pipeline with replay, recovery and data contracts.",
  "Plan a safe backfill and validation strategy while live ingestion continues."
]

changed /release/interface/developer_name

BEFORE
"Krishna Sathvik"
AFTER
"Tekris Labs"

changed /release/interface/logo_url

BEFORE
"https://files.openai.com/content?id=file_00000000524481f890727a5fa01bd762"
AFTER
"https://files.openai.com/content?id=file_000000005c0c81f8917f92358f648ccb"

changed /release/interface/long_description

BEFORE
"Data Engineering Copilot helps you design, debug, recover, and improve production data systems across Spark, Databricks, Delta Lake, SQL, CDC, Structured Streaming, Airflow, backfills, data quality, observability, and lakehouse architectures. It can diagnose failed or slow pipelines, reason about duplicate and late data, plan safe replay and backfill strategies, review checkpoint and streaming-state risks, tune Spark from execution evidence, and create production-oriented SQL, PySpark, and orchestration patterns. It prioritizes correctness, recoverability, SLA, security, and cost before scaling or redesigning."
AFTER
"Design, build, debug, recover and optimize dependable data pipelines and platforms. Work across Apache Spark, Databricks, Delta Lake, structured streaming, CDC, orchestration, dbt, SQL warehouses, Microsoft Fabric/OneLake, data quality, lineage and incident response. Inspect actual code, logs, query plans and data contracts where the user supplies them or authorized tools are available; identify root causes, plan safe replay and backfills, check schema/row grain, and validate fixes. Includes multi-agent coordination guidance when genuine subagent tools exist. Skills-only: no automatic access to clusters, credentials, datasets or production systems; executions and changes require enabled tools and permissions."

changed /release/interface/privacy_policy_url

BEFORE
null
AFTER
"https://tekrislabs.com/privacy"

changed /release/interface/short_description

BEFORE
"Debug production data systems"
AFTER
"Build reliable data pipelines"

changed /release/interface/terms_of_service_url

BEFORE
null
AFTER
"https://tekrislabs.com/terms"

changed /release/interface/website_url

BEFORE
null
AFTER
"https://tekrislabs.com/"

changed /release/keywords

BEFORE
[
  "data-engineering",
  "spark",
  "databricks",
  "delta-lake",
  "cdc",
  "streaming",
  "airflow",
  "data-quality"
]
AFTER
[
  "data-engineering",
  "spark",
  "databricks",
  "delta-lake",
  "fabric",
  "onelake",
  "airflow",
  "dbt",
  "cdc",
  "streaming",
  "data-quality"
]

changed /release/onboarding_skill_name

BEFORE
null
AFTER
"data-engineering"

changed /release/skills

BEFORE
[
  {
    "description": "Production-focused workflow for designing, debugging, recovering, tuning, and operating data pipelines across Spark, Databricks, Delta Lake, SQL, CDC, Structured Streaming, Airflow, backfills, quality, observability, and lakehouse systems.",
    "interface": {
      "brand_color": null,
      "default_prompt": "Debug why this production pipeline is failing or producing wrong data.",
      "display_name": "Data Engineering Copilot",
      "icon_large_url": null,
      "icon_small_url": "https://files.openai.com/content?id=file_0000000000c081f598f2dfb37f7328b4",
      "iconography": "chart",
      "short_description": "Debug production data systems"
    },
    "name": "data-engineering",
    "plugin_release_skill_id": "pluginrsk_6ab424afaf608191b4c67ed67116dcfc"
  }
]
AFTER
[
  {
    "description": "Implement or improve a SQL, Python, PySpark or orchestration pipeline with version-aware safe writes and test evidence.",
    "interface": {
      "brand_color": null,
      "default_prompt": null,
      "display_name": "build-data-pipeline",
      "icon_large_url": null,
      "icon_small_url": null,
      "iconography": "code",
      "short_description": "Implement or improve a SQL, Python, PySpark or orchestration pipeline with version-aware safe writes and test evidence."
    },
    "name": "build-data-pipeline",
    "plugin_release_skill_id": "pluginrsk_6ae8d7488fac81918b2847f2df9e31e8"
  },
  {
    "description": "Route data engineering design, debugging, implementation, recovery, observability, optimization, and security requests to focused skills.",
    "interface": {
      "brand_color": null,
      "default_prompt": "Help me diagnose a production data pipeline from evidence.",
      "display_name": "Data Engineering Copilot",
      "icon_large_url": null,
      "icon_small_url": "https://files.openai.com/content?id=file_000000008ec081f4bc22cd3f8359fccb",
      "iconography": "code",
      "short_description": "Build reliable data pipelines"
    },
    "name": "data-engineering",
    "plugin_release_skill_id": "pluginrsk_6ae8d74dfe9081918d1377948761453c"
  },
  {
    "description": "Diagnose failing, duplicated, stale, late or incorrect data pipelines using job logs, sample data and execution evidence.",
    "interface": {
      "brand_color": null,
      "default_prompt": null,
      "display_name": "debug-data-pipeline",
      "icon_large_url": null,
      "icon_small_url": null,
      "iconography": "chart",
      "short_description": "Diagnose failing, duplicated, stale, late or incorrect data pipelines using job logs, sample data and execution evidence."
    },
    "name": "debug-data-pipeline",
    "plugin_release_skill_id": "pluginrsk_6ae8d74b44948191be465b16187a10f2"
  },
  {
    "description": "Design source-to-target change data capture with authoritative ordering, deletes, schema evolution and idempotent MERGE.",
    "interface": {
      "brand_color": null,
      "default_prompt": null,
      "display_name": "design-cdc-pipeline",
      "icon_large_url": null,
      "icon_small_url": null,
      "iconography": "code",
      "short_description": "Design source-to-target change data capture with authoritative ordering, deletes, schema evolution and idempotent MERGE."
    },
    "name": "design-cdc-pipeline",
    "plugin_release_skill_id": "pluginrsk_6ae8d74b5c3c819183b5f11221150971"
  },
  {
    "description": "Design source/target data contracts, reconciliation, completeness, freshness and anomaly tests with actions.",
    "interface": {
      "brand_color": null,
      "default_prompt": null,
      "display_name": "design-data-quality",
      "icon_large_url": null,
      "icon_small_url": null,
      "iconography": "chart",
      "short_description": "Design source/target data contracts, reconciliation, completeness, freshness and anomaly tests with actions."
    },
    "name": "design-data-quality",
    "plugin_release_skill_id": "pluginrsk_6ae8d74da1a88191aa92eb97ea354812"
  },
  {
    "description": "Design or diagnose Microsoft Fabric Lakehouse, OneLake, data pipelines, shortcuts and deployment workflows.",
    "interface": {
      "brand_color": null,
      "default_prompt": null,
      "display_name": "design-fabric-lakehouse",
      "icon_large_url": null,
      "icon_small_url": null,
      "iconography": "hierarchy",
      "short_description": "Design or diagnose Microsoft Fabric Lakehouse, OneLake, data pipelines, shortcuts and deployment workflows."
    },
    "name": "design-fabric-lakehouse",
    "plugin_release_skill_id": "pluginrsk_6ae8d74c8f408191a2a273d3ba68de16"
  },
  {
    "description": "Design Airflow or other job orchestration with idempotent retries, scheduling, dependencies and safe backfills.",
    "interface": {
      "brand_color": null,
      "default_prompt": null,
      "display_name": "design-orchestration",
      "icon_large_url": null,
      "icon_small_url": null,
      "iconography": "hierarchy",
      "short_description": "Design Airflow or other job orchestration with idempotent retries, scheduling, dependencies and safe backfills."
    },
    "name": "design-orchestration",
    "plugin_release_skill_id": "pluginrsk_6ae8d749d5548191a6384f5a72d7ef39"
  },
  {
    "description": "Design and troubleshoot Structured Streaming sources, state, watermarking, checkpoints and sink semantics.",
    "interface": {
      "brand_color": null,
      "default_prompt": null,
      "display_name": "design-streaming",
      "icon_large_url": null,
      "icon_small_url": null,
      "iconography": "code",
      "short_description": "Design and troubleshoot Structured Streaming sources, state, watermarking, checkpoints and sink semantics."
    },
    "name": "design-streaming",
    "plugin_release_skill_id": "pluginrsk_6ae8d7482e8481919e6b6290374b309d"
  },
  {
    "description": "Design dbt and SQL warehouse incremental models, tests, snapshots, lineage and release gates.",
    "interface": {
      "brand_color": null,
      "default_prompt": null,
      "display_name": "design-warehouse-dbt",
      "icon_large_url": null,
      "icon_small_url": null,
      "iconography": "code",
      "short_description": "Design dbt and SQL warehouse incremental models, tests, snapshots, lineage and release gates."
    },
    "name": "design-warehouse-dbt",
    "plugin_release_skill_id": "pluginrsk_6ae8d747ee088191a2c35e9c378aeeb1"
  },
  {
    "description": "Migrate legacy ETL, Spark, schedulers, cloud warehouses or lakehouse code while preserving correctness and contracts.",
    "interface": {
      "brand_color": null,
      "default_prompt": null,
      "display_name": "modernize-data-platform",
      "icon_large_url": null,
      "icon_small_url": null,
      "iconography": "code",
      "short_description": "Migrate legacy ETL, Spark, schedulers, cloud warehouses or lakehouse code while preserving correctness and contracts."
    },
    "name": "modernize-data-platform",
    "plugin_release_skill_id": "pluginrsk_6ae8d748bd4881918b369b8e677de903"
  },
  {
    "description": "Define data-platform operational telemetry, SLOs, alerting and cost drivers tied to recovery decisions.",
    "interface": {
      "brand_color": null,
      "default_prompt": null,
      "display_name": "observe-data-platform",
      "icon_large_url": null,
      "icon_small_url": null,
      "iconography": "chart",
      "short_description": "Define data-platform operational telemetry, SLOs, alerting and cost drivers tied to recovery decisions."
    },
    "name": "observe-data-platform",
    "plugin_release_skill_id": "pluginrsk_6ae8d74a739c819187d299d4845426b4"
  },
  {
    "description": "Coordinate actual specialist agents for complex data engineering tasks only when supported execution tools exist.",
    "interface": {
      "brand_color": null,
      "default_prompt": null,
      "display_name": "orchestrate-data-agents",
      "icon_large_url": null,
      "icon_small_url": null,
      "iconography": "hierarchy",
      "short_description": "Coordinate actual specialist agents for complex data engineering tasks only when supported execution tools exist."
    },
    "name": "orchestrate-data-agents",
    "plugin_release_skill_id": "pluginrsk_6ae8d749e0e88191ad72dc9f8f50df2c"
  },
  {
    "description": "Plan safe historical backfills, CDC replays and checkpoint recovery without duplicates or lost data.",
    "interface": {
      "brand_color": null,
      "default_prompt": null,
      "display_name": "plan-backfill-replay",
      "icon_large_url": null,
      "icon_small_url": null,
      "iconography": "hierarchy",
      "short_description": "Plan safe historical backfills, CDC replays and checkpoint recovery without duplicates or lost data."
    },
    "name": "plan-backfill-replay",
    "plugin_release_skill_id": "pluginrsk_6ae8d74cf69481919d4f89472cc93826"
  },
  {
    "description": "Architect batch, CDC, streaming, lakehouse or warehouse data systems with correctness, access, SLA and recovery trade-offs.",
    "interface": {
      "brand_color": null,
      "default_prompt": null,
      "display_name": "plan-data-platform",
      "icon_large_url": null,
      "icon_small_url": null,
      "iconography": "hierarchy",
      "short_description": "Architect batch, CDC, streaming, lakehouse or warehouse data systems with correctness, access, SLA and recovery trade-offs."
    },
    "name": "plan-data-platform",
    "plugin_release_skill_id": "pluginrsk_6ae8d74d161081918336f795a89347d5"
  },
  {
    "description": "Triage an active production data incident and plan containment, evidence capture, safe recovery, and stakeholder updates.",
    "interface": {
      "brand_color": null,
      "default_prompt": null,
      "display_name": "respond-data-incident",
      "icon_large_url": null,
      "icon_small_url": null,
      "iconography": "radar",
      "short_description": "Triage an active production data incident and plan containment, evidence capture, safe recovery, and stakeholder updates."
    },
    "name": "respond-data-incident",
    "plugin_release_skill_id": "pluginrsk_6ae8d74bb94081919f71bada0382565a"
  },
  {
    "description": "Review SQL data-model grain, key uniqueness, fact/dimension joins, slowly changing dimensions and metric definitions.",
    "interface": {
      "brand_color": null,
      "default_prompt": null,
      "display_name": "review-data-model",
      "icon_large_url": null,
      "icon_small_url": null,
      "iconography": "chart",
      "short_description": "Review SQL data-model grain, key uniqueness, fact/dimension joins, slowly changing dimensions and metric definitions."
    },
    "name": "review-data-model",
    "plugin_release_skill_id": "pluginrsk_6ae8d74c7f548191b6a0d8ae2e1c05aa"
  },
  {
    "description": "Review data-pipeline permissions, secrets, PII, tenant isolation, lineage, retention and audit boundaries.",
    "interface": {
      "brand_color": null,
      "default_prompt": null,
      "display_name": "secure-data-platform",
      "icon_large_url": null,
      "icon_small_url": null,
      "iconography": "radar",
      "short_description": "Review data-pipeline permissions, secrets, PII, tenant isolation, lineage, retention and audit boundaries."
    },
    "name": "secure-data-platform",
    "plugin_release_skill_id": "pluginrsk_6ae8d74bb3e081918599979c54f8da70"
  },
  {
    "description": "Diagnose Spark and Databricks performance using actual plans, stage metrics, skew, spill and storage layout evidence.",
    "interface": {
      "brand_color": null,
      "default_prompt": null,
      "display_name": "tune-spark-databricks",
      "icon_large_url": null,
      "icon_small_url": null,
      "iconography": "chart",
      "short_description": "Diagnose Spark and Databricks performance using actual plans, stage metrics, skew, spill and storage layout evidence."
    },
    "name": "tune-spark-databricks",
    "plugin_release_skill_id": "pluginrsk_6ae8d749f2988191ba34df2bfa15c21a"
  }
]

changed /release/version

BEFORE
"0.1.0"
AFTER
"0.2.0"
Full snapshot data
{
  "canonical_app_id": null,
  "connector_id": null,
  "created_at": "2026-09-23T19:17:18.763916Z",
  "discoverability": "LISTED",
  "id": "plugins_6ab424ad5b448191b8fede2ebf569333",
  "is_template": false,
  "is_template_instance": false,
  "is_v2_template": false,
  "name": "production-data-engineering-copilot",
  "release": {
    "app_ids": [],
    "app_manifest": null,
    "app_templates": [],
    "description": "Production data-engineering design, implementation, debugging, recovery, reliability and safe data operations.",
    "display_name": "Data Engineering Copilot",
    "id": "pluginrel_a2def7072ab08191b0c6fcbe977e9c18",
    "interface": {
      "brand_color": "#183044",
      "capabilities": [
        "Architect batch, CDC, streaming, lakehouse and warehouse data systems",
        "Debug duplicates, data loss, latency, failures and stale pipelines from evidence",
        "Plan safe backfills, replay, checkpoint recovery and rollback strategies",
        "Review CDC merge ordering, delete propagation and idempotent writes",
        "Diagnose Spark and Databricks query plans, shuffle, skew, spill and file layout",
        "Design Microsoft Fabric Lakehouse, OneLake, shortcuts and deployment flows",
        "Design dbt/SQL transformations, model grain, incremental updates and contracts",
        "Review Airflow orchestration, retries, scheduling and backfill safety",
        "Build data quality, freshness, reconciliation and observability checks",
        "Assess access control, governance, PII handling and data lineage",
        "Coordinate supported specialist agents and evidence-based verification",
        "Prepare version-aware implementation plans and release/incident evidence"
      ],
      "category": "Data & Analytics",
      "composer_icon_dark_url": null,
      "composer_icon_url": "https://files.openai.com/content?id=file_00000000f5fc82068a4f9b67b62c8953",
      "default_prompt": "Diagnose this failing or incorrect pipeline using logs, code and data-quality evidence.",
      "default_prompts": [
        "Diagnose this failing or incorrect pipeline using logs, code and data-quality evidence.",
        "Design a reliable CDC or lakehouse pipeline with replay, recovery and data contracts.",
        "Plan a safe backfill and validation strategy while live ingestion continues."
      ],
      "developer_name": "Tekris Labs",
      "logo_url": "https://files.openai.com/content?id=file_000000005c0c81f8917f92358f648ccb",
      "logo_url_dark": null,
      "long_description": "Design, build, debug, recover and optimize dependable data pipelines and platforms. Work across Apache Spark, Databricks, Delta Lake, structured streaming, CDC, orchestration, dbt, SQL warehouses, Microsoft Fabric/OneLake, data quality, lineage and incident response. Inspect actual code, logs, query plans and data contracts where the user supplies them or authorized tools are available; identify root causes, plan safe replay and backfills, check schema/row grain, and validate fixes. Includes multi-agent coordination guidance when genuine subagent tools exist. Skills-only: no automatic access to clusters, credentials, datasets or production systems; executions and changes require enabled tools and permissions.",
      "plugin_category_id": "data & analytics",
      "privacy_policy_url": "https://tekrislabs.com/privacy",
      "screenshot_urls": [],
      "short_description": "Build reliable data pipelines",
      "terms_of_service_url": "https://tekrislabs.com/terms",
      "website_url": "https://tekrislabs.com/"
    },
    "keywords": [
      "data-engineering",
      "spark",
      "databricks",
      "delta-lake",
      "fabric",
      "onelake",
      "airflow",
      "dbt",
      "cdc",
      "streaming",
      "data-quality"
    ],
    "mcp_servers": [],
    "onboarding_skill_name": "data-engineering",
    "requires_local_executor": false,
    "skills": [
      {
        "description": "Implement or improve a SQL, Python, PySpark or orchestration pipeline with version-aware safe writes and test evidence.",
        "interface": {
          "brand_color": null,
          "default_prompt": null,
          "display_name": "build-data-pipeline",
          "icon_large_url": null,
          "icon_small_url": null,
          "iconography": "code",
          "short_description": "Implement or improve a SQL, Python, PySpark or orchestration pipeline with version-aware safe writes and test evidence."
        },
        "name": "build-data-pipeline",
        "plugin_release_skill_id": "pluginrsk_6ae8d7488fac81918b2847f2df9e31e8"
      },
      {
        "description": "Route data engineering design, debugging, implementation, recovery, observability, optimization, and security requests to focused skills.",
        "interface": {
          "brand_color": null,
          "default_prompt": "Help me diagnose a production data pipeline from evidence.",
          "display_name": "Data Engineering Copilot",
          "icon_large_url": null,
          "icon_small_url": "https://files.openai.com/content?id=file_000000008ec081f4bc22cd3f8359fccb",
          "iconography": "code",
          "short_description": "Build reliable data pipelines"
        },
        "name": "data-engineering",
        "plugin_release_skill_id": "pluginrsk_6ae8d74dfe9081918d1377948761453c"
      },
      {
        "description": "Diagnose failing, duplicated, stale, late or incorrect data pipelines using job logs, sample data and execution evidence.",
        "interface": {
          "brand_color": null,
          "default_prompt": null,
          "display_name": "debug-data-pipeline",
          "icon_large_url": null,
          "icon_small_url": null,
          "iconography": "chart",
          "short_description": "Diagnose failing, duplicated, stale, late or incorrect data pipelines using job logs, sample data and execution evidence."
        },
        "name": "debug-data-pipeline",
        "plugin_release_skill_id": "pluginrsk_6ae8d74b44948191be465b16187a10f2"
      },
      {
        "description": "Design source-to-target change data capture with authoritative ordering, deletes, schema evolution and idempotent MERGE.",
        "interface": {
          "brand_color": null,
          "default_prompt": null,
          "display_name": "design-cdc-pipeline",
          "icon_large_url": null,
          "icon_small_url": null,
          "iconography": "code",
          "short_description": "Design source-to-target change data capture with authoritative ordering, deletes, schema evolution and idempotent MERGE."
        },
        "name": "design-cdc-pipeline",
        "plugin_release_skill_id": "pluginrsk_6ae8d74b5c3c819183b5f11221150971"
      },
      {
        "description": "Design source/target data contracts, reconciliation, completeness, freshness and anomaly tests with actions.",
        "interface": {
          "brand_color": null,
          "default_prompt": null,
          "display_name": "design-data-quality",
          "icon_large_url": null,
          "icon_small_url": null,
          "iconography": "chart",
          "short_description": "Design source/target data contracts, reconciliation, completeness, freshness and anomaly tests with actions."
        },
        "name": "design-data-quality",
        "plugin_release_skill_id": "pluginrsk_6ae8d74da1a88191aa92eb97ea354812"
      },
      {
        "description": "Design or diagnose Microsoft Fabric Lakehouse, OneLake, data pipelines, shortcuts and deployment workflows.",
        "interface": {
          "brand_color": null,
          "default_prompt": null,
          "display_name": "design-fabric-lakehouse",
          "icon_large_url": null,
          "icon_small_url": null,
          "iconography": "hierarchy",
          "short_description": "Design or diagnose Microsoft Fabric Lakehouse, OneLake, data pipelines, shortcuts and deployment workflows."
        },
        "name": "design-fabric-lakehouse",
        "plugin_release_skill_id": "pluginrsk_6ae8d74c8f408191a2a273d3ba68de16"
      },
      {
        "description": "Design Airflow or other job orchestration with idempotent retries, scheduling, dependencies and safe backfills.",
        "interface": {
          "brand_color": null,
          "default_prompt": null,
          "display_name": "design-orchestration",
          "icon_large_url": null,
          "icon_small_url": null,
          "iconography": "hierarchy",
          "short_description": "Design Airflow or other job orchestration with idempotent retries, scheduling, dependencies and safe backfills."
        },
        "name": "design-orchestration",
        "plugin_release_skill_id": "pluginrsk_6ae8d749d5548191a6384f5a72d7ef39"
      },
      {
        "description": "Design and troubleshoot Structured Streaming sources, state, watermarking, checkpoints and sink semantics.",
        "interface": {
          "brand_color": null,
          "default_prompt": null,
          "display_name": "design-streaming",
          "icon_large_url": null,
          "icon_small_url": null,
          "iconography": "code",
          "short_description": "Design and troubleshoot Structured Streaming sources, state, watermarking, checkpoints and sink semantics."
        },
        "name": "design-streaming",
        "plugin_release_skill_id": "pluginrsk_6ae8d7482e8481919e6b6290374b309d"
      },
      {
        "description": "Design dbt and SQL warehouse incremental models, tests, snapshots, lineage and release gates.",
        "interface": {
          "brand_color": null,
          "default_prompt": null,
          "display_name": "design-warehouse-dbt",
          "icon_large_url": null,
          "icon_small_url": null,
          "iconography": "code",
          "short_description": "Design dbt and SQL warehouse incremental models, tests, snapshots, lineage and release gates."
        },
        "name": "design-warehouse-dbt",
        "plugin_release_skill_id": "pluginrsk_6ae8d747ee088191a2c35e9c378aeeb1"
      },
      {
        "description": "Migrate legacy ETL, Spark, schedulers, cloud warehouses or lakehouse code while preserving correctness and contracts.",
        "interface": {
          "brand_color": null,
          "default_prompt": null,
          "display_name": "modernize-data-platform",
          "icon_large_url": null,
          "icon_small_url": null,
          "iconography": "code",
          "short_description": "Migrate legacy ETL, Spark, schedulers, cloud warehouses or lakehouse code while preserving correctness and contracts."
        },
        "name": "modernize-data-platform",
        "plugin_release_skill_id": "pluginrsk_6ae8d748bd4881918b369b8e677de903"
      },
      {
        "description": "Define data-platform operational telemetry, SLOs, alerting and cost drivers tied to recovery decisions.",
        "interface": {
          "brand_color": null,
          "default_prompt": null,
          "display_name": "observe-data-platform",
          "icon_large_url": null,
          "icon_small_url": null,
          "iconography": "chart",
          "short_description": "Define data-platform operational telemetry, SLOs, alerting and cost drivers tied to recovery decisions."
        },
        "name": "observe-data-platform",
        "plugin_release_skill_id": "pluginrsk_6ae8d74a739c819187d299d4845426b4"
      },
      {
        "description": "Coordinate actual specialist agents for complex data engineering tasks only when supported execution tools exist.",
        "interface": {
          "brand_color": null,
          "default_prompt": null,
          "display_name": "orchestrate-data-agents",
          "icon_large_url": null,
          "icon_small_url": null,
          "iconography": "hierarchy",
          "short_description": "Coordinate actual specialist agents for complex data engineering tasks only when supported execution tools exist."
        },
        "name": "orchestrate-data-agents",
        "plugin_release_skill_id": "pluginrsk_6ae8d749e0e88191ad72dc9f8f50df2c"
      },
      {
        "description": "Plan safe historical backfills, CDC replays and checkpoint recovery without duplicates or lost data.",
        "interface": {
          "brand_color": null,
          "default_prompt": null,
          "display_name": "plan-backfill-replay",
          "icon_large_url": null,
          "icon_small_url": null,
          "iconography": "hierarchy",
          "short_description": "Plan safe historical backfills, CDC replays and checkpoint recovery without duplicates or lost data."
        },
        "name": "plan-backfill-replay",
        "plugin_release_skill_id": "pluginrsk_6ae8d74cf69481919d4f89472cc93826"
      },
      {
        "description": "Architect batch, CDC, streaming, lakehouse or warehouse data systems with correctness, access, SLA and recovery trade-offs.",
        "interface": {
          "brand_color": null,
          "default_prompt": null,
          "display_name": "plan-data-platform",
          "icon_large_url": null,
          "icon_small_url": null,
          "iconography": "hierarchy",
          "short_description": "Architect batch, CDC, streaming, lakehouse or warehouse data systems with correctness, access, SLA and recovery trade-offs."
        },
        "name": "plan-data-platform",
        "plugin_release_skill_id": "pluginrsk_6ae8d74d161081918336f795a89347d5"
      },
      {
        "description": "Triage an active production data incident and plan containment, evidence capture, safe recovery, and stakeholder updates.",
        "interface": {
          "brand_color": null,
          "default_prompt": null,
          "display_name": "respond-data-incident",
          "icon_large_url": null,
          "icon_small_url": null,
          "iconography": "radar",
          "short_description": "Triage an active production data incident and plan containment, evidence capture, safe recovery, and stakeholder updates."
        },
        "name": "respond-data-incident",
        "plugin_release_skill_id": "pluginrsk_6ae8d74bb94081919f71bada0382565a"
      },
      {
        "description": "Review SQL data-model grain, key uniqueness, fact/dimension joins, slowly changing dimensions and metric definitions.",
        "interface": {
          "brand_color": null,
          "default_prompt": null,
          "display_name": "review-data-model",
          "icon_large_url": null,
          "icon_small_url": null,
          "iconography": "chart",
          "short_description": "Review SQL data-model grain, key uniqueness, fact/dimension joins, slowly changing dimensions and metric definitions."
        },
        "name": "review-data-model",
        "plugin_release_skill_id": "pluginrsk_6ae8d74c7f548191b6a0d8ae2e1c05aa"
      },
      {
        "description": "Review data-pipeline permissions, secrets, PII, tenant isolation, lineage, retention and audit boundaries.",
        "interface": {
          "brand_color": null,
          "default_prompt": null,
          "display_name": "secure-data-platform",
          "icon_large_url": null,
          "icon_small_url": null,
          "iconography": "radar",
          "short_description": "Review data-pipeline permissions, secrets, PII, tenant isolation, lineage, retention and audit boundaries."
        },
        "name": "secure-data-platform",
        "plugin_release_skill_id": "pluginrsk_6ae8d74bb3e081918599979c54f8da70"
      },
      {
        "description": "Diagnose Spark and Databricks performance using actual plans, stage metrics, skew, spill and storage layout evidence.",
        "interface": {
          "brand_color": null,
          "default_prompt": null,
          "display_name": "tune-spark-databricks",
          "icon_large_url": null,
          "icon_small_url": null,
          "iconography": "chart",
          "short_description": "Diagnose Spark and Databricks performance using actual plans, stage metrics, skew, spill and storage layout evidence."
        },
        "name": "tune-spark-databricks",
        "plugin_release_skill_id": "pluginrsk_6ae8d749f2988191ba34df2bfa15c21a"
      }
    ],
    "version": "0.2.0"
  },
  "scope": "GLOBAL",
  "share_url": "https://chatgpt.com/plugins/plugins_6ab424ad5b448191b8fede2ebf569333?open_in_app",
  "status": "ENABLED"
}

SHA-256 of public snapshot: f0301d3a1cac7a8b6a057e234fe72fe0c8e0d574d47223571e0b3d34427c35fa