# How to use a Righthand with Apify

Use Righthand to prepare an Apify dataset-quality review from a specified Actor run.

By Righthand Team · 2026-10-12

Source: https://www.righthand.ai/blog/how-to-use-righthand-with-apify

## Quick answer

Use Righthand to prepare an Apify dataset-quality review from a specified Actor run. Dataset rows need source provenance and field validation before they become business facts.

Start with a reviewable deliverable, keeping Apify as the accepted home for its records. This guide uses an explicitly approved snapshot or browser read; it does not assume that every provider API action is exposed by your Righthand connection.

## Evidence and access

Apify documents datasets, export formats, and append-only dataset storage. See the [official Apify reference](https://docs.apify.com/storage/dataset). Provider capabilities and Righthand's available tools are separate checks.

Use the supplied Actor run and dataset export with run ID, Actor version, inputs, retrieval times, and owner-approved use scope. Verify the required tools and owner-approved scope through [Righthand integrations](/integrations); use the supplied packet when a native read is unavailable.

## Define the objects and authority

Include these fields in the working input: Actor run ID, dataset ID, row key, source URL, required fields, collected time. These are the requested review columns, not a promise that every provider endpoint returns them under those exact names.

A completed crawl does not establish complete site coverage or accurate extraction. New Actor runs and data changes need separate approval.

| Responsibility | Owner |
| --- | --- |
| Prepare source scope and access | Account owner |
| Organize evidence and proposed next steps | Righthand |
| Approve consequential changes | the data-collection owner |

## Illustrative first-run brief

> Use the owner-provided Apify snapshot or approved read-only view. Review the supplied dataset for twenty approved source rows. Flag missing required fields and duplicate source URLs. Prepare a reconciliation sheet without starting Actors. Run once for the agreed sample, using America/Los_Angeles for the review deadline and preserving source timestamps separately. Return the draft in this conversation by Friday at 3 PM Pacific for the data-collection owner to review. Include source references and missing fields. Do not write records, spend money, change permissions, or send external messages. Stop and report an unavailable source rather than substituting another account.

## Expected result and verification

A row with no source URL remains unverified. Duplicate URLs are listed for review rather than deleted from an append-only source.

Compare a sampled row with its source and exact Actor run, then reconcile exported counts. Record the source cutoff, applied filters, and items excluded from the sample. The reviewer should be able to follow each consequential finding back to an original object. A well-written summary is not evidence that a proposed action happened. If a later write is authorized, verify its specific result separately before changing the task's status.


### Illustrative input and draft output

These synthetic entries show the requested review, not live account results or a provider field schema.

| Source item | Supplied observation | Draft conclusion |
| --- | --- | --- |
| Run A | Dataset and inputs supplied | Review scoped output |
| Run B | Succeeded, dataset empty | Business result unknown |
| Run C | Input filter changed | Separate comparison |

## Common failure and recovery

The wrong run or unnamed expired dataset can make evidence unavailable. Ask for the retained approved export and preserve its run identity.

If a run succeeded but its dataset is empty, report both facts and request the expected input/output context before declaring that no matching items exist.

## FAQ

### What should the reviewer verify first?

Verify run ID, Actor version, input filters, dataset identity, and completeness evidence. Successful execution does not guarantee the dataset answers the business question.

### How should a repeat run be scoped?

Preserve run and dataset references across reviews. Recheck changed Actor versions and input filters; avoid treating outputs from different configurations as equivalent counts.

For connection and permission details, use [Righthand Docs](https://docs.righthand.ai). Review [Pricing](/pricing) alongside any separate provider charges before expanding the workflow.