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

# Prioritize accounts with reviewable rules

> Ask your agent to rank accounts with reasons your team can inspect.

[Agent guide: CLI and SDK instructions →](/docs/guides/account-prioritization)

## 1. Have your context ready

Have target account IDs, a prioritization policy, required evidence, and examples of high and low priorities.

## 2. Ask your agent

If the evidence or criteria are unclear, start with source research:

```deepline-prompt theme={null}
/deepline-research Identify evidence sources for the scoring or prioritization decision I describe. Separate observed facts, candidate proxies, and missing information. Check whether the evidence can be available at the decision date. Recommend a source plan and explain its coverage before collecting additional data. Do not invent scoring weights or treat source availability as proof of predictive value.
```

Once the criteria and evidence plan are clear, ask for scoring. Skip the research step when you already have reviewed sources and approved rules:

```deepline-prompt theme={null}
/deepline-scoring Prepare account priorities with Deepline using the rules I provide. Separate observed facts from inferred importance. Keep missing evidence and conflicting signals visible. Return each account’s priority, contributing evidence, rule version, and suggested next review action. Compare the result with a small labeled sample before expanding. Do not automatically assign owners or launch outreach.
```

Complete any requested sign-in yourself. Give the agent the missing information before approving execution. Provider-backed work can incur usage.

## 3. Review the result

The team can explain why an account outranks another under the stated policy. Unknown information does not become invented evidence.

Ask your agent:

```deepline-prompt theme={null}
/deepline-scoring Compare the result with the acceptance rule we agreed. Show the evidence for accepted and rejected outcomes, unresolved questions, and recorded Deepline usage where work ran. If work is still running or a response is uncertain, inspect the original saved execution before proposing another request. Explain the next decision I need to make.
```

Approve a larger job, recurring work, or external changes only after reviewing that decision.

## Implement the reviewed policy

After reviewing the scoring rules and evaluation limits, hand the agreed policy to GTM execution:

```deepline-prompt theme={null}
/deepline-gtm Implement the reviewed scoring or prioritization policy from this conversation. Preserve the agreed model or rule version, input definitions, evidence requirements, missing-data behavior, and output contract. Reuse collected evidence where it fits. Check the Play and propose a representative pilot before expanding the job or changing CRM records. Do not tune the scoring policy silently during implementation.
```

## Check your understanding

Should a missing decisive fit field always mean the lowest priority?

> **Show the answer**
>
> No. Apply the stated uncertainty policy. Missing evidence and verified poor
> fit are different states.

## Related guides

* [Write Plays](/docs/human/sdk-v2/plays)
* [Batch CSV enrichment](/docs/human/sdk-v2/batch-csv)
* [Score accounts against labeled examples](/docs/human/guides/account-scoring-workflows)
* [Competitive Advertising Intelligence: Build a Cited Brief](/docs/human/guides/competitive-advertising-intelligence)


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