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← Data Engineering CopilotCONTENT HISTORY

Update to Data Engineering Copilot

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

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Before

Sep 30, 2026 · 23:18 UTC

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After

Nov 3, 2026 · 00:12 UTC

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Package file

Before

Sep 30, 2026 · 23:18 UTC

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After

Nov 3, 2026 · 00:12 UTC

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Package file

Before

Sep 30, 2026 · 23:18 UTC

2a008086fd4646e97fe61742f05b39e721679ddc6d230399917b9d100fe589b7

After

Nov 3, 2026 · 00:12 UTC

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Package file

Before

Sep 30, 2026 · 23:18 UTC

2535

After

Nov 3, 2026 · 00:12 UTC

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Changed files

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.codex-plugin/assets/composer-icon.svg →

.codex-plugin/assets/logo.svg →

.codex-plugin/plugin.json →

CHANGELOG.md →

PLUGIN_CREATOR_PROMPT.md →

README.md →

SUBMISSION.md →

UPGRADE_AUDIT.md →

assets/composer-icon.svg →

assets/icon.svg →

benchmarks/fixtures/reconciliation.json →

benchmarks/review-cases.md →

benchmarks/scenarios.json →

docs/RESEARCH_NOTES.md →

docs/original-v0.1.0-skill.md →

plugin.json →

references/agent-orchestration.md →

references/architecture-patterns.md →

references/cdc-and-merge.md →

references/core-invariants.md →

references/data-quality-reconciliation.md →

references/evidence-contract.md →

references/fabric-onelake.md →

references/incident-and-recovery.md →

references/migration-playbook.md →

references/observability-and-cost.md →

references/official-sources.md →

references/orchestration-and-retries.md →

references/original/data_engineering_implementation_templates.md →

references/original/data_quality_observability_runbook.md →

references/original/official_source_registry.md →

references/original/production_architecture_patterns.md →

references/original/production_debug_playbooks.md →

references/original/spark_databricks_tuning_guide.md →

references/original/streaming_cdc_recovery_patterns.md →

references/schema-contracts.md →

references/security-governance.md →

references/spark-performance.md →

references/streaming-state.md →

references/testing-standards.md →

references/warehouse-dbt.md →

scripts/reconcile_fixture.py →

scripts/validate_package.py →

skills/build-data-pipeline/SKILL.md →

skills/data-engineering/SKILL.md →

skills/data-engineering/agents/openai.yaml →

skills/data-engineering/references/data_engineering_implementation_templates.md →

skills/data-engineering/references/data_quality_observability_runbook.md →

skills/data-engineering/references/official_source_registry.md →

skills/data-engineering/references/production_architecture_patterns.md →

skills/data-engineering/references/production_debug_playbooks.md →

skills/data-engineering/references/spark_databricks_tuning_guide.md →

skills/data-engineering/references/streaming_cdc_recovery_patterns.md →

skills/debug-data-pipeline/SKILL.md →

skills/design-cdc-pipeline/SKILL.md →

skills/design-data-quality/SKILL.md →

skills/design-fabric-lakehouse/SKILL.md →

skills/design-orchestration/SKILL.md →

skills/design-streaming/SKILL.md →

skills/design-warehouse-dbt/SKILL.md →

skills/modernize-data-platform/SKILL.md →

skills/observe-data-platform/SKILL.md →

skills/orchestrate-data-agents/SKILL.md →

skills/plan-backfill-replay/SKILL.md →

skills/plan-data-platform/SKILL.md →

skills/respond-data-incident/SKILL.md →

skills/review-data-model/SKILL.md →

skills/secure-data-platform/SKILL.md →

skills/tune-spark-databricks/SKILL.md →

submission/listing-and-prompts.md →

submission/public-submission-checklist.md →

submission/release-notes.md →

submission/test-cases.md →

templates/agent-handoff.md →

templates/architecture-brief.md →

templates/backfill-plan.md →

templates/incident-brief.md →

templates/model-contract.md →

templates/quality-test-matrix.csv →

templates/release-evidence.md →

tests/test_reconcile_fixture.py →

plugin.json

− removed · + added

--- before
+++ after
@@ -1,50 +1,67 @@
 {
   "$schema": "https://agent-plugins.org/schemas/1.0.0/plugin.schema.json",
+  "name": "production-data-engineering-copilot",
+  "version": "0.2.0",
+  "description": "Production data-engineering design, implementation, debugging, recovery, reliability and safe data operations.",
   "author": {
-    "name": "Krishna Sathvik"
-  },
-  "description": "Production data engineering architecture, debugging, recovery, and reliability workflows.",
-  "extensions": {
-    "com.openai": {
-      "interface": {
-        "capabilities": [
-          "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"
-        ],
-        "category": "Data & Analytics",
-        "composerIcon": "./.codex-plugin/assets/composer-icon.svg",
-        "defaultPrompt": [
-          "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."
-        ],
-        "developerName": "Krishna Sathvik",
-        "displayName": "Data Engineering Copilot",
-        "logo": "./.codex-plugin/assets/logo.svg",
-        "longDescription": "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.",
-        "shortDescription": "Debug production data systems"
-      }
-    }
+    "name": "Tekris Labs",
+    "email": "support@tekrislabs.com",
+    "url": "https://tekrislabs.com/"
   },
+  "homepage": "https://tekrislabs.com/",
   "keywords": [
     "data-engineering",
     "spark",
     "databricks",
     "delta-lake",
+    "fabric",
+    "onelake",
+    "airflow",
+    "dbt",
     "cdc",
     "streaming",
-    "airflow",
     "data-quality"
   ],
-  "name": "production-data-engineering-copilot",
   "skills": "./skills/",
-  "version": "0.1.0"
+  "extensions": {
+    "com.openai": {
+      "interface": {
+        "displayName": "Data Engineering Copilot",
+        "shortDescription": "Build reliable data pipelines",
+        "longDescription": "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.",
+        "developerName": "Tekris Labs",
+        "category": "Data & Analytics",
+        "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"
+        ],
+        "defaultPrompt": [
+          "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."
+        ],
+        "brandColor": "#183044",
+        "logo": "./assets/logo.svg",
+        "composerIcon": "./assets/composer-icon.svg",
+        "websiteURL": "https://tekrislabs.com/",
+        "supportURL": "https://tekrislabs.com/contact",
+        "privacyPolicyURL": "https://tekrislabs.com/privacy",
+        "termsOfServiceURL": "https://tekrislabs.com/terms"
+      },
+      "onboardingSkill": "./skills/data-engineering/SKILL.md",
+      "publication": {
+        "release_notes": "0.2.0: Reorganized the original production data engineering skill into focused build, incident, CDC, streaming, backfill, modeling, Fabric, dbt/warehouse, observability, governance, testing and agent orchestration workflows. Preserved original reference material and icons; added templates, 24 prompt-level benchmark cases, and offline data-quality checks. Updated publisher and Tekris Labs policy URLs. Skills-only."
+      }
+    }
+  }
 }
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