Actor Dataset Schema Generator

Store Positioning

Store title: Actor Dataset Schema Generator

Short description: Audit actor dataset schema generator inputs and return structured findings, evidence rows, and a buyer-ready report for SEO, developer, and operations teams.

SEO title: Actor Dataset Schema Generator — API, schema, and developer QA

SEO description: Audit actor dataset schema generator inputs and return structured findings, evidence rows, and a buyer-ready report for SEO, developer, and operations teams. Use it to catch contract drift, schema mistakes, unsafe endpoint assumptions, and developer-tool quality issues before release.

Categories: SEO_TOOLS, DEVELOPER_TOOLS

Keywords: actor, dataset, schema, generator, apify actor, data qa, dataset qa, api testing, api/developer qa

Fixed-Inclusive PPE Pricing

This actor uses pay-per-event pricing. Event prices include Apify platform usage; users are not expected to pay a separate platform-usage pass-through charge for the configured pricing model.

• Tier: A1 — API/developer QA
• Primary event: schema-validated at $0.00650 base
• Default max charge: $10.00
• Store discounts: FREE/BRONZE base, SILVER discounted, GOLD deepest approved discount

Event set:

• actor-start: base $0.00500, GOLD $0.00400. Actor Dataset Schema Generator: charged when actor start is completed. The price includes Apify platform usage; no separate usage pass-through is intended.
• schema-validated: base $0.00650, GOLD $0.00520. Actor Dataset Schema Generator: charged when schema validated is completed. The price includes Apify platform usage; no separate usage pass-through is intended.
• contract-rule-checked: base $0.00390, GOLD $0.00312. Actor Dataset Schema Generator: charged when contract rule checked is completed. The price includes Apify platform usage; no separate usage pass-through is intended.
• report-generated: base $0.05000, GOLD $0.04000. Actor Dataset Schema Generator: charged when report generated is completed. The price includes Apify platform usage; no separate usage pass-through is intended.

Public Task Concepts

• Audit Actor Dataset Schema controls on a capped public sample
• Find high-priority Actor Dataset Schema issues before release
• Validate Actor Dataset Schema evidence from supplied pages
• Prioritize Actor Dataset Schema fixes with severity and proof
• Export Actor Dataset Schema QA rows for client review

Generate or improve Apify dataset schemas/views from sample actor output rows.

What This Actor Does

Actor Dataset Schema Generator turns explicit watchlists or supplied records into structured Apify dataset rows and a concise report. The capped built-in records make the default run deterministic and avoid network, browser, proxy, LLM, or third-party API costs.

The actor checks each supplied record for source identity, matched watch terms, actor-specific metadata, issue signals, severity, recommendations, and pricing metadata. It writes one dataset row per processed record and, when enabled, key-value-store JSON and Markdown summaries for operational workflows.

What This Actor Does Not Do

• It does not provide legal, medical, financial, investment, safety, or compliance advice.
• It does not guarantee completeness of any public source or third-party dataset.
• It does not collect sensitive personal data by default.
• It does not rely on unsupported private APIs.

Best Use Cases

• schema drafting from dataset rows
• Apify output view preparation
• dataset contract cleanup

Input Fields Explained

Field	Type	Default	Notes
records	array	two built-in records	Records or snapshots to check. Each item can include title, description, body, entity, category, keywords, and metadata.
urls	array	[]	Reserved for optional source URLs. Defaults do not fetch URLs.
fetchUrls	boolean	false	Enables HTTP fetching for records with sourceUrl and no body. Keep false when records already contain the text to analyze.
watchTerms	array	actor defaults	Terms used to mark relevant records and alerts.
includeReport	boolean	true	Writes JSON and Markdown report artifacts to the key-value store.
maxItems	integer	2	Caps processed records. Maximum is 50.
maxTextBytes	integer	500000	Caps analyzed text per record.
debug	boolean	false	Enables debug logging.

Example Input

{
  "records": [
    {
      "sourceId": "schema-sample",
      "sourceUrl": "dataset://sample",
      "title": "Dataset sample rows",
      "description": "Two sample actor output rows for schema inference.",
      "entityName": "sample-actor",
      "category": "schema",
      "keywords": [
        "schema"
      ],
      "metadata": {
        "sampleRows": [
          {
            "url": "https://example.com/a",
            "status": "pass",
            "score": 98,
            "tags": [
              "seo"
            ]
          },
          {
            "url": "https://example.com/b",
            "status": "warn",
            "score": 62,
            "tags": [
              "missing-alt"
            ]
          }
        ]
      }
    },
    {
      "sourceId": "schema-empty",
      "sourceUrl": "dataset://empty",
      "title": "Missing sample rows",
      "description": "Schema request without sample rows.",
      "entityName": "sample-actor",
      "category": "schema",
      "keywords": [],
      "metadata": {}
    }
  ],
  "includeReport": true,
  "maxItems": 2
}

Output Fields Explained

Field	Meaning
auditId	Stable hash for the checked record.
sourceId, sourceUrl	Source identifiers for traceability.
status, severity, score	Normalized outcome and 0-100 quality/risk score.
matchedKeywords	Watch terms found in the record text or metadata.
issues, issueCodes	Actor-specific findings with severity and messages.
recommendations	Next actions for the operator or content/data owner.
metadata	Original metadata plus derived helper fields when applicable.
pricingTemplate, pricingEventName	Pricing source and primary PPE event used by the actor.

Example Output

{
  "auditId": "actorda_2445f7caa4a09be9",
  "actorSlug": "actor-dataset-schema-generator",
  "actorName": "Actor Dataset Schema Generator",
  "sourceType": "fixture",
  "sourceId": "schema-sample",
  "sourceUrl": "dataset://sample",
  "title": "Dataset sample rows",
  "status": "pass",
  "severity": "low",
  "score": 100,
  "matchedKeywords": [
    "dataset",
    "sample rows",
    "schema"
  ],
  "primaryEntity": "sample-actor",
  "category": "schema",
  "publishedAt": null,
  "summary": "Two sample actor output rows for schema inference.",
  "issueCount": 1,
  "issues": [
    {
      "code": "schema-generated",
      "severity": "info",
      "message": "Sample rows are available for schema generation.",
      "recommendation": "Review the generated schema descriptions before publishing."
    }
  ],
  "issueCodes": [
    "schema-generated"
  ],
  "recommendations": [
    "Review the generated schema descriptions before publishing."
  ],
  "metadata": {
    "sampleRows": [
      {
        "url": "https://example.com/a",
        "status": "pass",
        "score": 98,
        "tags": [
          "seo"
        ]
      },
      {
        "url": "https://example.com/b",
        "status": "warn",
        "score": 62,
        "tags": [
          "missing-alt"
        ]
      }
    ],
    "generatedDatasetSchema": {
      "actorDatasetSchemaVersion": 1,
      "title": "Generated Dataset Schema",
      "description": "Inferred from supplied sample rows. Review descriptions before publishing.",
      "type": "object",
      "properties": {
        "score": {
          "type": "number",
          "description": "Inferred field \"score\" from sample dataset rows."
        },
        "status": {
          "type": "string",
          "description": "Inferred field \"status\" from sample dataset rows."
        },
        "tags": {
          "type": "array",
          "description": "Inferred field \"tags\" from sample dataset rows.",
          "items": {
            "type": "string"
          }
        },
        "url": {
          "type": "string",
          "description": "Inferred field \"url\" from sample dataset rows."
        }
      }
    },
    "sourceSpecific": {
      "structuredRows": [
        {
          "url": "https://example.com/a",
          "status": "pass",
          "score": 98,
          "tags": [
            "seo"
          ]
        },
        {
          "url": "https://example.com/b",
          "status": "warn",
          "score": 62,
          "tags": [
            "missing-alt"
          ]
        }
      ]
    },
    "normalizedTextPreview": "schema-sample dataset://sample Dataset sample rows Two sample actor output rows for schema inference.  sample-actor schema schema {\"sampleRows\":[{\"url\":\"https://example.com/a\",\"status\":\"pass\",\"score\":98,\"tags\":[\"seo\"]},{\"url\":\"https://examp"
  },
  "textBytes": 302,
  "checkedAt": "2026-07-02T00:00:00.000Z",
  "pricingTemplate": "A1 — API/developer QA",
  "pricingEventName": "schema-validated"
}

Cost-Control Tips

• Keep maxItems small for default and scheduled checks.
• Use supplied records or snapshots for deterministic QA runs.
• Leave fetchUrls off unless live source fetching is explicitly needed.
• Review warning rows before increasing caps.
• Schedule small frequent runs instead of broad one-off sweeps.

Scheduling Examples

• Daily watchlist check with two to five high-value records.
• Weekly QA run before publishing a site, data, or actor update.
• Post-migration or post-release validation using a saved record set.

Public Task Examples

• tiny-default-check - Run a two-record actor dataset schema generator check with report artifacts enabled.
• watchlist-digest - Process a small supplied watchlist and summarize matched terms.
• risk-triage-report - Return only records with warning or fail status for operator follow-up.
• scheduled-monitor - Run Actor Dataset Schema Generator on a daily or weekly schedule with tight caps.
• schema-and-billing-preflight - Validate output shape and PPE event names before live setup.

FAQ

Can I schedule recurring checks?

Yes. Save a capped input and use Apify schedules to run it daily or weekly.

Can I use this with live URLs?

Yes. Set fetchUrls to true and provide urls or records with sourceUrl. Failed fetches produce explicit diagnostic rows.

Why does it charge before pushing output?

Pay-per-event actors must charge before paid output is written. If the charge limit is reached, processing stops before another paid row is emitted.

What happens on charge limits?

The actor stops gracefully and writes a summary instead of pushing unpaid result rows.

Is this legal, financial, safety, or compliance advice?

No. The actor produces operational signals and structured data for review. Human owners remain responsible for decisions.

Troubleshooting

• If the dataset is empty, check that maxItems is greater than zero and that records are supplied or defaults are enabled.
• If a record is flagged unexpectedly, inspect issueCodes, matchedKeywords, and the normalized metadata preview.
• If live fetching fails, rerun with supplied record bodies or snapshots and keep fetchUrls false.

Limitations

This actor only sees the records, snapshots, or URLs supplied to it. It does not guarantee complete public-source coverage, private-source access, or all edge-case parsing. Browser and proxy flows are not used by default.

Source And Safety Notes

The built-in records are labeled through sourceType: "fixture" and are intended to show the output contract. When connecting public sources, use official or permissioned endpoints where possible and avoid sensitive personal data. Do not use the output as a substitute for professional advice.

Changelog

• 2026-07-02: Initial actor package with schemas, capped defaults, pricing controls, and runtime validation.