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data-orchestration

v0.1.2

Data-orchestration team — agents (orchestration-architect, pipeline-orchestration-engineer) for the layer that RUNS and SCHEDULES data pipelines: orchestrator selection (Airflow, Dagster, Prefect, Mage, Temporal-for-data; cloud-native MWAA/Step Functions, Azure Data Factory, Cloud Composer/Workflows), DAG and software-defined-asset design, scheduling/triggers (cron, sensor, data-aware), backfills and catchup, idempotency and retries with exponential backoff, partitioning, freshness SLAs and alerting, and lineage. skills (choose-orchestrator, design-dag-and-dependencies, handle-backfills-and-retries), a knowledge bank (a Mermaid selection tree + a 2026 patterns reference), and templates. Distinct from data-platform (ELT/warehouse/BI), analytics-engineering (dbt), and data-streaming-engineering (real-time) — this is the scheduling/run layer they plug into. Requires ravenclaude-core@>=0.7.0.

Claude Code3 Skills

By Matt CorbettLicense: MIT7 GitHub starsUpdated last week

Directory evidence

Runtimes
Claude Code
Parsed components
3 skill or MCP entries
Source updated
Sep 15, 2026
Manifest status
Canonical path parsed

The directory validates manifest shape and source location. It does not execute the plugin or provide a security endorsement. Review the indexing methodology →

Install data-orchestration for Claude Code

Installs for the current user
claude plugin marketplace add IchenDEV/agent-plugin-mkt
claude plugin marketplace update agent-plugin-marketplace
claude plugin install data-orchestration@agent-plugin-marketplace

Paste and run these commands in a terminal with Claude Code. They add and refresh the PluginsMP catalog, then install this plugin.

The installer fetches third-party code from the source repository shown on this page. This directory validates manifest structure and source location, but does not perform a security audit; review the manifest, components, and source before installing.

Get the source manually
git clone https://github.com/mcorbett51090/RavenClaude

Clone the source repository, then follow its setup instructions to add the plugin to a compatible client. The plugin root is plugins/data-orchestration/.

Plugin files

plugins/data-orchestration/
├── .claude-plugin/plugin.json
├── skills/choose-orchestrator/SKILL.md
├── skills/design-dag-and-dependencies/SKILL.md
└── skills/handle-backfills-and-retries/SKILL.md

Included Skills3

choose-orchestratorskills/choose-orchestrator/SKILL.md

Pick the right data-pipeline orchestrator for a described workload by traversing the orchestrator-selection decision tree (workload shape → latency → asset-centric vs task-centric → team/ops capacity → cloud lock-in → engine), then return the recommended engine, its scheduling model, its executor/runtime, the trade-offs, and the conditions that would flip the choice. Reach for this when the user asks "Airflow vs Dagster vs Prefect?", "self-host or managed (MWAA/Composer/ADF)?", or "what should run our pipelines?". Used by `orchestration-architect` (primary).

design-dag-and-dependenciesskills/design-dag-and-dependencies/SKILL.md

Design a correct, minimal DAG or software-defined-asset graph for a pipeline — derive the real upstream→downstream edges, pick the partition grain, choose the scheduling/triggering model (cron / sensor-deferrable / data-aware-asset), and decide catchup behavior — then capture it in the DAG design doc. Reach for this when the user says "design the DAG/assets for <pipeline>", "what should depend on what?", or "how should these jobs trigger?". Used by `pipeline-orchestration-engineer` (primary).

handle-backfills-and-retriesskills/handle-backfills-and-retries/SKILL.md

Make pipeline tasks safe to re-run and plan backfills that don't corrupt state — prove idempotency (deterministic partition keys, overwrite-by-partition), then add bounded retries with exponential backoff + jitter, and run a controlled backfill (partition strategy, catchup config, concurrency caps, monitoring, rollback). Reach for this when the user says "our pipeline isn't safe to re-run", "add retries", or "we need to backfill <range>". Used by `pipeline-orchestration-engineer` (primary).

Plugin manifests1

plugins/data-orchestration/.claude-plugin/plugin.json
{
  "name": "data-orchestration",
  "version": "0.1.2",
  "description": "Data-orchestration team — agents (orchestration-architect, pipeline-orchestration-engineer) for the layer that RUNS and SCHEDULES data pipelines: orchestrator selection (Airflow, Dagster, Prefect, Mage, Temporal-for-data; cloud-native MWAA/Step Functions, Azure Data Factory, Cloud Composer/Workflows), DAG and software-defined-asset design, scheduling/triggers (cron, sensor, data-aware), backfills and catchup, idempotency and retries with exponential backoff, partitioning, freshness SLAs and alerting, and lineage. skills (choose-orchestrator, design-dag-and-dependencies, handle-backfills-and-retries), a knowledge bank (a Mermaid selection tree + a 2026 patterns reference), and templates. Distinct from data-platform (ELT/warehouse/BI), analytics-engineering (dbt), and data-streaming-engineering (real-time) — this is the scheduling/run layer they plug into. Requires ravenclaude-core@>=0.7.0.",
  "author": {
    "name": "Matt Corbett"
  },
  "homepage": "https://github.com/mcorbett51090/RavenClaude",
  "license": "MIT",
  "keywords": [
    "airflow",
    "dagster",
    "prefect",
    "dag",
    "orchestration",
    "scheduling",
    "backfill",
    "idempotency",
    "data-pipeline",
    "lineage",
    "retries",
    "partitioning"
  ],
  "requires": {
    "plugins": [
      "ravenclaude-core@>=0.7.0"
    ]
  }
}

For maintainers

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[data-orchestration on Agent Plugins Marketplace](https://pluginsmp.com/plugins/data-orchestration)