CRM Auditor

Performs a comprehensive data quality audit on CRM data. Checks for missing fields, duplicate entries, stale leads, invalid emails, inconsistent formatting, and leads stuck in the same stage too long. Calculates a completeness score per record and overall, then outputs an audit report with specific records to fix. Works with any CRM accessible via crm-writer.

Prerequisites

• agency.config.json in the project root
• CRM data accessible via crm-writer or provided as CSV/JSON
• Optional: email validation tool (email-validator skill or ZeroBounce API)

Phase 0: Read Config

1. Read agency.config.json from the project root.
2. Extract CRM configuration:
   • crm.tabs[] -- CRM tab names and their purpose
   • crm.required_fields -- fields that must be populated per record
   • crm.stages[] -- valid pipeline stages
   • crm.stale_threshold_days -- days before a lead is considered stale (default: 30)
3. Extract tools.crm for data access method (Google Sheets webhook, API, etc.).
4. Extract tools.email_validation for email checking capability.
5. Define required fields if not in config:

   Default required fields:
   - company_name
   - contact_name
   - email
   - stage
   - last_updated
   - source
   

Phase 1: Pull CRM Data

Read all CRM data via crm-writer or from provided export:

Tabs to audit:

• Researched Leads
• Outreach CRM
• Inbound Leads
• Any other tabs defined in crm.tabs[]

For each tab, load all rows and parse into structured records. Note:

• Total record count per tab
• Column headers present
• Date of earliest and most recent record

Phase 2: Missing Field Analysis

For each record, check required fields:

MISSING FIELD ANALYSIS
===
Tab: Outreach CRM (150 records)

Field            | Present | Missing | % Complete
-----------------|---------|---------|----------
company_name     | 148     | 2       | 98.7%
contact_name     | 145     | 5       | 96.7%
email            | 140     | 10      | 93.3%
phone            | 85      | 65      | 56.7%
stage            | 150     | 0       | 100.0%
last_updated     | 130     | 20      | 86.7%
source           | 120     | 30      | 80.0%
deal_value       | 95      | 55      | 63.3%
next_action      | 75      | 75      | 50.0%

OVERALL COMPLETENESS: 80.6%

Flag records with 3+ missing required fields as "critically incomplete."

Phase 3: Duplicate Detection

Check for duplicates across multiple dimensions:

Email duplicates:

• Exact match on email field
• Group duplicate sets together

Company name duplicates:

• Exact match
• Fuzzy match (case insensitive, whitespace normalized, common suffixes stripped: Inc, Ltd, LLC, Pvt)
• Flag potential duplicates for manual review

Phone duplicates:

• Normalize phone numbers (strip spaces, dashes, country codes)
• Exact match on normalized form

DUPLICATE REPORT
===
EXACT DUPLICATES (same email): 8 sets, 18 records
  - john@acme.com appears in rows 12, 45, 89
  - sarah@beta.com appears in rows 23, 67

PROBABLE DUPLICATES (fuzzy company match): 5 sets, 12 records
  - "Acme Corp" / "Acme Corporation" / "ACME" -- rows 12, 34, 56
  - "Beta Inc" / "Beta Inc." -- rows 23, 78

CROSS-TAB DUPLICATES: 3 records appear in both Researched Leads and Outreach CRM
  - john@acme.com -- Researched Leads row 5, Outreach CRM row 12

Phase 4: Stale Lead Detection

Identify leads that have not been updated recently:

STALE LEADS (not updated in 30+ days)
===
Company          | Contact      | Stage           | Last Updated | Days Stale
-----------------|-------------|-----------------|--------------|----------
Acme Corp        | John Smith  | Discovery Call  | 2024-01-15   | 60 days
Beta Inc         | Sarah Jones | Proposal Sent   | 2024-01-28   | 47 days
Gamma Ltd        | Mike Chen   | Qualified       | 2024-02-01   | 43 days

TOTAL STALE: 23 leads (15.3% of pipeline)

Categorize stale leads:

• Re-engage: in active stages (Qualified, Discovery, Proposal) -- needs follow-up
• Archive: in early stages (New Lead) for 60+ days -- likely dead
• Urgent: in late stages (Negotiation, Verbal Commit) -- deal at risk

Phase 5: Email Validation

If email validation tools are available:

EMAIL QUALITY CHECK
===
Valid emails: 128 (91.4%)
Invalid format: 5 (3.6%)
  - "john@" (row 34) -- missing domain
  - "sarah.jones" (row 67) -- missing @ and domain
  - "mike@.com" (row 89) -- empty domain name
Risky/disposable: 4 (2.9%)
  - "contact@mailinator.com" (row 12)
Bounced (if validation API used): 3 (2.1%)

If no validation tool, check format only:

• Valid email regex pattern
• Common typos (gmial.com, gamil.com, outlok.com)
• Generic addresses (info@, contact@, hello@) flagged as low quality

Phase 6: Consistency Checks

Audit formatting consistency across the CRM:

Stage names:

• Check all stage values against crm.stages[]
• Flag non-standard stage names
• Example issues: "qualified" vs "Qualified" vs "QUALIFIED", "Prop Sent" vs "Proposal Sent"

Date formats:

• Check all date fields for consistent formatting
• Flag mixed formats (MM/DD/YYYY vs DD/MM/YYYY vs YYYY-MM-DD)

Phone formats:

• Check for consistent phone number formatting
• Flag entries with letters, special characters, or incomplete numbers

Company name formatting:

• Flag all-caps entries
• Flag entries with leading/trailing whitespace
• Flag entries with inconsistent capitalization

CONSISTENCY ISSUES
===
Stage name variations: 4 non-standard values found
  - "qual" should be "Qualified" (3 records)
  - "proposed" should be "Proposal Sent" (1 record)

Date format inconsistencies: 12 records
  - 8 records use MM/DD/YYYY, 130 use YYYY-MM-DD

Company name issues: 7 records
  - "ACME CORP" should be "Acme Corp" (2 records)
  - " Beta Inc " has leading/trailing spaces (1 record)

Phase 7: Stage Stuck Analysis

Identify leads stuck in the same stage too long:

STAGE DURATION ANALYSIS
===
Stage             | Avg Days | Expected Max | Stuck (over max)
------------------|----------|-------------|------------------
New Lead          | 5 days   | 7 days      | 8 leads
Qualified         | 8 days   | 14 days     | 3 leads
Discovery Call    | 12 days  | 14 days     | 2 leads
Proposal Sent     | 15 days  | 21 days     | 5 leads
Negotiation       | 10 days  | 14 days     | 1 lead

For each stuck lead, recommend an action:

• Advance to next stage (if ready)
• Send a follow-up
• Schedule a call
• Disqualify and archive

Phase 8: Completeness Score

Calculate a per-record completeness score:

score = (filled_required_fields / total_required_fields) * 100

Categorize:

• Green (90-100%): record is complete, no action needed
• Yellow (70-89%): partially complete, minor fixes needed
• Red (below 70%): critically incomplete, needs immediate attention

CRM HEALTH SUMMARY
===
Overall completeness score: 80.6%
Green records (90%+): 95 (63%)
Yellow records (70-89%): 35 (23%)
Red records (<70%): 20 (13%)

Data quality grade: B-

Phase 9: Output

Return structured JSON:

{
  "crm_audit": {
    "audit_date": "2024-03-15",
    "total_records": 150,
    "tabs_audited": ["Researched Leads", "Outreach CRM", "Inbound Leads"],
    "overall_completeness": 80.6,
    "data_quality_grade": "B-",
    "missing_fields": {
      "by_field": {},
      "critically_incomplete_records": []
    },
    "duplicates": {
      "exact_email_duplicates": 8,
      "probable_company_duplicates": 5,
      "cross_tab_duplicates": 3,
      "records_to_merge": []
    },
    "stale_leads": {
      "total": 23,
      "re_engage": [],
      "archive": [],
      "urgent": []
    },
    "email_quality": {
      "valid": 128,
      "invalid_format": 5,
      "risky": 4,
      "records_to_fix": []
    },
    "consistency_issues": {
      "stage_variations": [],
      "date_format_issues": [],
      "name_formatting": []
    },
    "stuck_leads": [],
    "record_scores": {
      "green": 95,
      "yellow": 35,
      "red": 20
    },
    "priority_actions": [
      "Fix 5 invalid email addresses (rows 34, 67, 89, 91, 103)",
      "Merge 8 duplicate email sets",
      "Re-engage 12 stale leads in active stages",
      "Archive 11 stale New Leads (60+ days old)",
      "Standardize 4 non-standard stage names"
    ]
  }
}

Example Usage

Trigger phrases:

• "Audit our CRM data quality"
• "How clean is our pipeline data?"
• "Check for duplicate leads in the CRM"
• "Find stale leads that need follow-up"
• "Run a data hygiene check"
• "What's our CRM completeness score?"

User: Audit our CRM data quality
Assistant: [reads CRM data via crm-writer, checks all fields for completeness, finds 8 duplicate sets, 23 stale leads, 5 invalid emails, calculates 80.6% completeness, outputs prioritized action list]

User: Find leads stuck in our pipeline
Assistant: [analyzes stage durations, identifies 19 leads past expected stage time, categorizes by urgency, recommends specific next actions per lead]