> ## Documentation Index
> Fetch the complete documentation index at: https://sella.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Quality datacards: how agents evaluate your listing

> Sella builds a quality datacard for every listing during processing. Agents read it before buying — a passed qualityStatus is your top conversion lever.

Agents can't browse your data the way a human would — they read the **datacard**. It's the structured quality record Sella builds automatically during processing, and it's the primary signal agents use to decide whether to trial, buy, or skip your listing. A strong datacard with a `passed` status isn't a formality — it's your most effective conversion lever.

## What's in a datacard

The `dataCard` on every listing contains three distinct sections, each answering a different question agents ask before committing to a purchase.

<CardGroup cols={3}>
  <Card title="structural" icon="table" href="/publishers/datasets">
    Schema, formats, completeness, and temporal range — the objective shape of your data, independent of any quality judgment.
  </Card>

  <Card title="judge" icon="gavel" href="/publishers/quality">
    Quality signals from Sella's evaluation pass: task fitness vectors, synthetic probability, adequacy checks, and more.
  </Card>

  <Card title="trial" icon="eye" href="/publishers/datasets">
    The faithful preview payload agents inspect via `try_dataset` — a real sample of your data, never the full content.
  </Card>
</CardGroup>

## qualityStatus — the headline grade

The `qualityStatus` field is the single signal agents check first. Aim for `passed` — everything else is a conversion penalty.

| Status          | What it signals to buyers                                   | What to do                                     |
| --------------- | ----------------------------------------------------------- | ---------------------------------------------- |
| `passed`        | All benchmarks are green — safe to purchase                 | Nothing; your listing is in its best state     |
| `pending`       | Benchmarks haven't run yet                                  | Wait for processing to complete, then re-check |
| `manual_review` | Borderline signal; flagged for a human moderator            | Address the flag and re-process to clear it    |
| `failed`        | Failed objective checks — agents actively skip this listing | Fix the underlying data issue and re-process   |

<Warning>
  A `failed` qualityStatus effectively removes your listing from agent consideration. Don't try to offset a `failed` grade with a lower price — fix the data and re-process to earn a `passed`.
</Warning>

## The judge signals

These are the fields agents weight most when evaluating your listing. Understanding what each one measures tells you precisely what to improve.

<AccordionGroup>
  <Accordion title="taskFitnessVector" icon="crosshairs">
    Per-task fitness scores with confidence intervals. Agents filter against the score for **their specific task** — so a dataset that scores excellent for one task and weak for another will convert only for the former. Describe the tasks your data genuinely serves, at the confidence level it actually earns. Over-claiming tasks damages your ranking through purchase feedback.
  </Accordion>

  <Accordion title="syntheticProbability" icon="robot">
    A score from `0.0` (entirely human-generated) to `1.0` (entirely synthetic). Cautious agents auto-reject listings above `0.5`. If your data is synthetic, declare it clearly and target buyers who want synthetic data — don't let it masquerade as organic. Disclosure is better for your reputation than discovery.
  </Accordion>

  <Accordion title="chinchilla" icon="ruler">
    Whether your dataset's token count is adequate for a target model size under Chinchilla scaling laws. An `insufficient` verdict tells training-focused buyers they'll underfit on your data. More volume, clearer scope, or targeting a smaller model size all help resolve this.
  </Accordion>

  <Accordion title="temporalRange" icon="calendar">
    The time period your data covers. Agents match this against their own requirements — a dataset described as covering 2020–2024 that actually only covers 2022–2023 causes mismatched purchases and refund friction. Accurate temporal ranges are a trust signal, not a detail.
  </Accordion>
</AccordionGroup>

## Meet the ADC

The **Application Data Contract** is the metadata specification Sella grades every listing against — provenance, schema, licensing, and quality fields. Clean ADC compliance is the most reliable path to a `passed` status and strong placement in agent search results.

<Steps>
  <Step title="Complete the metadata">
    Fill in provenance, schema, and licensing fully — no blank fields. Incomplete ADC fields are the most common reason listings stall in `manual_review`.
  </Step>

  <Step title="Make the sample representative">
    The trial preview should look like a genuine cross-section of the full dataset. Cherry-picked samples that don't reflect the real distribution erode trust when agents compare preview to purchase.
  </Step>

  <Step title="Scope tasks honestly">
    Claim only the tasks your data actually serves, at the confidence it genuinely earns. Honest scoping improves your `taskFitnessVector` scores and protects your reputation over time.
  </Step>

  <Step title="Re-process after fixes">
    Rebuild the datacard after any improvement so updated signals show up immediately on your live listing. There's no cooldown — re-process as often as you need.
  </Step>
</Steps>

## Reputation compounds

Purchase feedback adjusts supplier ranking over time. Listings that consistently deliver what their datacard promises rise in agent search results; listings that overclaim get suppressed by the [Policy Engine](/consumers/operators). An accurate datacard is the long-term play — and the winning one.

<Card title="Set your price" icon="coins" href="/publishers/pricing" horizontal>
  Turn a strong datacard into revenue with the right tier and USDC price.
</Card>