| name | evergreen-stale-data-audit |
| description | Audits Evergreen CRM for stale or incomplete contact data — outdated titles, missing fields, potential duplicates, and contacts that may have changed jobs. Use for periodic database hygiene, before a big outreach push, or when data quality feels off. |
Stale Data Audit
Works with Evergreen, a local-first personal CRM for macOS. Get it on the Mac App Store.
When to Use
- Quarterly database cleanup
- Before a bulk outreach or newsletter send
- When you notice contact details are outdated
- After importing contacts from another source
How It Works
- Pull contacts with
search_contacts, always passing an explicit limit: 100 — its maximum. Without a limit it returns only 20 and the audit silently covers a fraction of the database
- If the database is larger than 100 contacts, sweep it in batches using the filters
search_contacts already exposes (tags, organization, location, hasEmail, hasPhone, overdue, hasNextAction) so each batch stays under the cap, and keep track of which slices you covered
- For each contact retrieved, check for data quality issues using
get_contact
- Flag incomplete records, stale information, and potential problems
- Prioritize fixes by relationship importance
- Generate a cleanup task list with suggested fixes, and state up front how many contacts the audit actually covered
Data Quality Checks
| Check | What to Look For | Priority |
|---|
| Missing email | Contact has no email address | High |
| Missing organization | No company/org listed | Medium |
| Stale title | Title unchanged for 12+ months on active contact | Medium |
| No interactions | Contact exists but zero interactions logged | Low |
| No tags | Contact has no tags for filtering | Low |
| Empty notes | No context about the relationship | Medium |
| No location | Missing city/region | Low |
| Orphaned contacts | No relationships, no interactions, no tags | High |
Example Output
## Data Audit Report — April 2026
### Coverage
Audited **100 of 187 contacts** (total per Evergreen's contact count).
`search_contacts` returns at most 100 per call, so this run covered the first
100 it returned. Sweep the remaining 87 by filtering on `tags`, `organization`
or `location` and re-running.
### Summary
- **Contacts audited:** 100
- **Complete records:** 60 (60%)
- **Needs attention:** 40 (40%)
### Critical Issues (12)
1. **Sarah Chen** — Email missing (have everything else)
2. **Marcus Webb** — Title still says "Stealth Mode" (company launched 6 months ago)
3. **[5 contacts]** — No interactions ever logged (orphaned?)
4. **[5 contacts]** — Duplicate suspected (same org + similar name)
### Missing Fields Summary
| Field | Missing Count | % of Audited |
|-------|--------------|--------------|
| Email | 12 | 12% |
| Organization | 10 | 10% |
| Title | 17 | 17% |
| Location | 24 | 24% |
| Tags | 20 | 20% |
### Likely Job Changes
Contacts where title was set 12+ months ago and they're at a startup
(high likelihood of role change):
1. **Jamie Rodriguez** — "Senior Engineer at Acme Labs" (set 14 months ago)
2. **Priya Sharma** — "ML Engineer at NewCo" (set 13 months ago)
### Suggested Actions
1. Enrich the 12 critical contacts (use contact-enrichment skill)
2. Review 5 suspected duplicates for merging
3. Archive or delete 5 orphaned contacts with no activity
4. Batch-update missing tags for 20 untagged contacts
5. Re-run the audit over the 87 contacts not covered by this batch
Checklist
Data Audit:
- [ ] `search_contacts` called with an explicit `limit: 100` (never the default 20)
- [ ] Report states how many contacts were audited out of how many exist
- [ ] Any uncovered slice named so the next run can pick it up
- [ ] Contacts retrieved scanned for completeness
- [ ] Critical issues (missing email, duplicates) flagged first
- [ ] Missing fields summarized by type
- [ ] Potentially stale titles identified
- [ ] Orphaned contacts flagged for review
- [ ] Cleanup actions prioritized by impact
Learn More
