A working Actor is only the runtime
An Apify Actor can run successfully and still be a poor product. It may accept an unbounded input, emit records whose shape changes between runs, require undocumented secrets, expose internal diagnostics as dataset rows, or have no safe default for a marketplace reviewer. None of those problems necessarily stop a developer from running it manually. All of them make the Actor difficult to buy, evaluate, automate, and support.
The product begins when the runtime is surrounded by a stable contract. Buyers need to understand what they can ask for, what they will receive, what a run can cost, how an empty result behaves, and which credentials remain under their control. Marketplace operators need deterministic artifacts, quality evidence, publication state, and a way to repair the product without replacing its identity.
That turns “deploy this script” into a lifecycle: source, input policy, output contract, sample, package, pricing, quality run, public listing, integrations, and ongoing reconciliation.
Choose a narrow product promise
The Actor title should describe a buyer outcome, not a generic scraping capability. A narrow promise makes every later decision easier: source selection, input fields, schema, sample data, documentation, pricing event, and search language.
Define the product family explicitly. A recall monitor, tender finder, job-posting normalizer, supplier lookup, price-and-stock monitor, and legal filing extractor should not fall back to one generic “data” schema. Each family needs its own required fields and terminology. Cross-domain defaults create packages that technically validate while describing the wrong product.
The promise also sets the source boundary. A buyer-ready Actor needs a stable adapter and a source whose rights, access pattern, and expected output are compatible with the intended commercial use. If that cannot be established, publication should stop before the package is generated.
Bound every public input
Public inputs are part of the product contract. They should be typed, explained, validated, and capped. URLs need allowed protocols and host policy. Numeric limits need minimums and maximums. Modes need explicit enums. Optional credentials need precise storage and transmission rules.
A safe default run deserves special attention because reviewers and first-time buyers will use it. Zentra-generated Actors use an explicit one-item, uncharged sample mode for unattended quality and marketplace checks. Live collection is available only when the caller selects the live source mode and buyer-ready output.
That default has several benefits. It proves that the Actor starts. It demonstrates the field contract. It prevents an automated review from launching an expensive crawl. It produces deterministic evidence even when the live source is temporarily unavailable. Most importantly, it does not pretend that a synthetic sample is a live buyer result.
Design the Product Output Contract first
The dataset schema should not be an afterthought generated from whichever row appeared during testing. Define the buyer-facing record family before publication: stable identifier, relevant timestamps, required domain fields, optional values, enums, nested structures, null behavior, and provenance fields that are safe to expose.
From that contract, generate a coordinated set of artifacts:
- the Actor dataset schema;
- one deterministic, schema-complete sample row;
- validation for live buyer-ready rows;
- smoke fixtures for package checks;
- public field documentation;
- integration output definitions for Zapier, Sheets, or GitHub workflows.
Keep runtime and pricing metadata out of the dataset unless it belongs to the buyer record. Charge events, internal run identifiers, source diagnostics, and quality evidence belong in manifests, logs, or operational records. When strict dataset validation sees the same field family in sample and live modes, marketplace review becomes more predictable.
Make empty and partial results honest
An Actor can run correctly and find nothing. Empty results should remain empty, not be padded with demo records so an integration appears active. The package and docs should explain how zero results are represented and which filters commonly narrow the dataset.
Partial source failures also need an explicit policy. Decide whether the run fails, returns validated partial records with a warning, or waits for retry. Whatever the policy, invalid rows must not be charged as successful buyer output. In Zentra's generated runtime model, pricing events attach only to contract-valid live rows, while sample, quality, internal, owner, and invalid-contract activity stays outside actual buyer revenue.
Connect pricing to validated output
Pricing must match the work and the deliverable. A per-result model needs a stable event and a clear definition of a chargeable record. A fixed package needs a deterministic archive and provider-native price. A usage cap needs to stop the run before it creates uncontrolled cost.
For an Actor, document the default sample behavior, live mode, maximum items, charge event, and stop condition together. The runtime should suppress charges for the default sample and quality paths. It should validate the output contract before attaching any pay-per-event signal.
Forecasts and internal runs should never be presented as actual revenue. Revenue truth begins with eligible buyer activity and the marketplace's payout evidence, not with a successful QA dataset.
Package for installation and inspection
A buyer-ready Actor package should be understandable without a call. At minimum it needs a complete Actor manifest, input schema, dataset schema, stable sample, README, examples, limits, credential guidance, changelog, version, license or usage terms, troubleshooting, and a clear description of the public/private state.
The archive or deployment payload should be deterministic. Stable samples and sorted artifacts keep checksums meaningful. Secrets, local paths, raw provider identifiers, private datasets, and owner diagnostics must stay out of the package.
Documentation should distinguish buyer-owned credentials from publisher credentials. Integrations such as Zapier or Google Sheets can invoke a public Actor with the buyer's Apify token without requiring a separate paid Zentra account. The token should be stored by the integration's approved secret mechanism and sent only to the exact Apify host.
Prove the marketplace default run
Local unit tests are necessary, but the marketplace path has its own contract. Run the generated package with the exact default input a reviewer will see. Confirm that it starts asynchronously, produces the expected one-item sample, writes to the declared default dataset, matches the schema, stays uncharged, and exposes enough evidence to diagnose failure without leaking private data.
Quality checks should cover manifest completeness, bounded inputs, schema compatibility, sample mode, charge-limit stopping, documentation, source policy, pricing alignment, task coverage, and remote visibility. Repeated meaningful failures can pause or privatize a listing while preserving its remote identity and history. Provider rate limits or maintenance should wait and retry; they should not be misclassified as a bad product.
Publish by reconciliation, not replacement
Publication state is more than public or private. The control plane should know whether the package is generated, validated, prepared, submitted, under review, published, paused, rejected, or archived. It should also preserve the external Actor identifier, Store URL when available, published timestamp, and task coverage.
Before creating a remote Actor or task, reconciliation should look for an existing identity. Regeneration should update the existing product in place and preserve public status. A temporary provider response should not demote a published listing. A quality pause should keep history so the same product can recover rather than reappear as a duplicate.
External credentials and marketplace approval remain operational dependencies. The application can prepare, validate, queue, sync, and repair the product, but it cannot invent an approval or claim buyer activity that has not happened.
A buyer-ready Actor checklist
An Actor is ready for product review when each answer is specific:
- Is the product promise narrow and searchable?
- Is the source permitted and supported by a stable adapter?
- Are public inputs typed, documented, and bounded?
- Does the default run produce one safe, deterministic, uncharged sample?
- Do sample and live rows satisfy the same Product Output Contract?
- Are empty results honest and invalid rows excluded from charges?
- Is pricing tied to a defined, validated output event?
- Are manifests, schemas, docs, limits, and troubleshooting complete?
- Has the exact marketplace default run produced dataset evidence?
- Can publication reconcile and update an existing remote identity?
- Are buyer credentials separated from publisher credentials?
- Are quality, provider, and owner-action failures classified differently?
When those gates pass together, the Actor is no longer merely runnable. It is inspectable, packageable, publishable, and maintainable as a data product.
