Which AI agents and CRM platforms is this skill compatible with?

This skill is compatible with any AI agent that supports file uploads and data analysis, and it is optimized for processing CSV and XLSX exports from major real estate platforms like Salesforce, KVCore, and Follow Up Boss.

How does this engine handle data safety compared to just using a standard AI prompt?

Unlike manual prompting, this engine generates a comprehensive row-level change log and a rollback plan, ensuring you can audit every single modification and revert changes if needed to maintain data integrity.

What is required to set up and start using the cleanup engine?

No complex installation is required; simply upload your redacted CRM export and field definitions to your AI agent to begin the profiling and cleanup process.

Does the skill make live changes to my CRM database automatically?

To ensure privacy and security, this skill requires you to provide redacted data, and it generates a cleaned file for you to upload rather than performing live, unauthorized mutations directly within your CRM.

What exactly is included with the purchase of this skill?

Buyers receive the full skill logic for data profiling, duplicate detection, and normalization, along with templates for generating audit trails and import-ready datasets.

Are there any limitations on the volume of data the skill can process at once?

While it excels at complex pattern matching, very large datasets should be processed in chunks to stay within AI context limits and ensure the highest accuracy for duplicate detection.

crm data audit and cleanup engine

Example session with this skill installed

Audit this CSV export of 50 real estate leads. Identify duplicates based on email and phone, normalize the 'Stage' column to [New, Hot, Cold, Closed], and flag missing consent dates.

Read your context and instructions
Compiled the crm data audit
Generated the data export

I have completed the audit of the 50 lead records. The analysis identified 4 duplicate pairs, 12 inconsistent lifecycle stages, and 8 records lacking explicit consent timestamps.

CRM Audit and Cleanup Report

Metric	Count	Details
Total Records Scanned	50	Base dataset
Candidate Duplicates	4 Pairs	Matches on `email` or `phone_primary`
Normalization Targets	12	Mapped to [New, Hot, Cold, Closed]
Missing Consent	8	Records with null `consent_date`
Data Quality Score	76%	Based on field completion and validity

Proposed Duplicate Merges

[
  {
    "master_id": "L-9082",
    "duplicate_id": "L-4412",
    "reason": "Exact email match: j.doe@example.com",
    "conflict": "L-9082 has more recent activity; L-4412 has secondary phone."
  },
  {
    "master_id": "L-1123",
    "duplicate_id": "L-8890",
    "reason": "Phone match: 555-0199",
    "conflict": "None. L-8890 is a partial record."
  }
]

Normalization Mapping

Lead_Stage: 'Interested' -> Hot (5 instances)
Lead_Stage: 'Dead' -> Cold (3 instances)
Lead_Stage: 'Signed' -> Closed (4 instances)

Next steps

Review the Proposed Duplicate Merges to confirm which record should be the primary.
Verify the 8 records flagged for missing consent before including them in the next dialer sync.
Download the proposed cleaned_leads.csv for import into your CRM.

cleaned_leads.csv

CSV · data export

Generated

Example file from a real run - the skill writes it into your workspace.

Connects securely to your tools. The creator never sees your data.

About this skill

Messy CRM data stalls sales cycles and breaks automation. This skill audits real estate CRM exports to identify duplicates, normalize fields, and prepare a reversible cleanup plan without losing original data integrity.

What it does

Data Profiling maps schemas, stable IDs, and field meanings to ensure structural consistency.
Duplicate Detection flags candidate merges with evidence-based uncertainty levels to prevent false positives.
Record Normalization proposes standardized lifecycle stages, contact preferences, and consent fields.
Change Logging generates row-level audit trails and exception queues for transparent oversight.
Import Readiness structures cleaned datasets into import-friendly formats for specific CRM destinations.

How it works

Supply a redacted CRM export, field definitions, and your specific merge or consent policies.
The agent profiles the data quality, identifying specific gaps in contact info or property details.
Review the generated duplicate log and proposed normalization changes.
Export the final cleaned dataset alongside a row-level change log and a rollback plan.

Frameworks & tools

Works with CSV/XLSX exports from major real estate CRMs like Follow Up Boss, KVCore, or Salesforce. Uses internal logic for pattern matching and data reconciliation.

Why this beats prompting it yourself

Generic prompts often hallucinate fixes or fail to track which specific row was changed and why. This skill enforces a strict audit trail and treats every record as evidence, ensuring you can roll back any change if the import fails.

Use cases

Reconciling duplicate leads after merging two separate brokerage databases.
Cleaning up contact consent fields to ensure TCPA and GDPR compliance before a blast.
Standardizing property interest tags and lifecycle stages for better lead routing.
Preparing legacy spreadsheet data for a migration into a new CRM platform.

Known limitations

Requires pre-redacted data to maintain privacy. Large datasets may require chunked processing if context limits are exceeded. Does not perform live API mutations without explicit authorization.

How to use

Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.

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crm data audit and cleanup engine