# Check a pipeline before running it

**Use case:** 

A dry run of a three step chain. Nothing is started and no step is charged; the output shows each step in order with the exact input it would be given, including which field receives the previous step's dataset. This is the safe way to check a new chain.

## Input

```json
{
  "steps": [
    {
      "actor": "nerolabs/dataset-filter-transform",
      "label": "Filter",
      "input": {
        "data": [
          {
            "email": "ana@example.com",
            "plan": "Pro",
            "mrr": 49,
            "active": true
          },
          {
            "email": "ben@example.com",
            "plan": "Starter",
            "mrr": 19,
            "active": true
          },
          {
            "email": "cara@example.com",
            "plan": "Pro",
            "mrr": 99,
            "active": false
          },
          {
            "email": "dan@example.com",
            "plan": "Pro",
            "mrr": 149,
            "active": true
          }
        ],
        "filters": [
          {
            "field": "active",
            "operator": "isTrue"
          }
        ]
      }
    },
    {
      "actor": "nerolabs/dataset-cleaner-exporter",
      "label": "Dedupe and export",
      "input": {
        "dedupMode": "normalized",
        "dedupFields": [
          "email"
        ],
        "exportFormats": [
          "csv"
        ]
      }
    },
    {
      "actor": "nerolabs/dataset-aggregate-pivot",
      "label": "Summarise",
      "input": {
        "groupByFields": [
          "plan"
        ],
        "aggregations": [
          {
            "field": "mrr",
            "function": "sum",
            "as": "totalMrr"
          }
        ]
      }
    }
  ],
  "stopOnFailure": true,
  "defaultWaitSecs": 3600,
  "dryRun": true
}
```

## Output

```json
{
  "stepIndex": {
    "label": "#",
    "format": "number"
  },
  "label": {
    "label": "Step",
    "format": "text"
  },
  "actor": {
    "label": "Actor",
    "format": "text"
  },
  "status": {
    "label": "Status",
    "format": "text"
  },
  "itemCount": {
    "label": "Rows out",
    "format": "number"
  },
  "datasetId": {
    "label": "Dataset",
    "format": "text"
  },
  "injectedField": {
    "label": "Fed in via",
    "format": "text"
  },
  "durationMs": {
    "label": "ms",
    "format": "number"
  },
  "runUrl": {
    "label": "Run",
    "format": "link"
  },
  "error": {
    "label": "Error",
    "format": "text"
  }
}
```

## About this Actor

This example demonstrates how to use [Actor Pipeline Runner (Chain Actors in One Run)](https://apify.com/nerolabs/actor-pipeline-runner.md) with a specific input configuration. Visit the [Actor detail page](https://apify.com/nerolabs/actor-pipeline-runner.md) to learn more, explore other use cases, and run it yourself.


## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
This Task's input is already configured above — use it as-is rather than inventing a new one.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — the [REST API](https://docs.apify.com/api/v2.md).

For full API examples (JavaScript, Python, CLI, MCP, OpenAPI), see this Task's Actor page: https://apify.com/nerolabs/actor-pipeline-runner.md

For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).