anysite-crm-enrich

CRM Enrich

Fill gaps in existing CRM records with anysite data. The most common flow: pull a list from the CRM, enrich only what is missing, write back only to mapped fields.

Prerequisites

1. crm_list_connections → an active connection. Missing → send the user to /anysite-crm-setup (or Profile → CRM Integration in the dashboard).
2. The anysite-crm-profile skill exists → its mapping is law. Missing → minimal safe mode: standard properties only, recommend running setup.
3. Read the Writing rules in anysite-crm-setup — they apply to every write below.

Flow

1. Scope — what to enrich

Ask (or infer from the request): which records and which fields. Pull them:

crm_query_records(object_type="contacts", list_id=<working list> | search=... | emails=[...],
                  properties=[record_id + match keys + mapped target fields])

Page through everything in scope. Locally split records into:

• complete — all mapped target fields filled → skip (report count),
• enrichable — has a match key (email / linkedin_url / domain) and gaps,
• unmatchable — no key at all → report, do not guess identities.

2. Resolve identities (contacts)

• Has linkedin_url → execute linkedin/user/user (full profile: title, company, location).
• Only email → try reverse lookup first: execute linkedin/email/email_sql_user (cached, cheap) → remainder via email_user (live). Both take ONE email per call — loop, don't batch, and expect misses (verified to return empty even for people who are on LinkedIn). Then the cascade that actually works, because the CRM knows the name: email domain → resolve company (verified, per anysite-mcp recipe) → organizational_urn → search_users {first_name, last_name, current_company: [{"type": "company", "value": "<id>"}]} → usually exactly one match, WITH the profile URN as a bonus. Company filter mandatory — bare names return namesakes. On BIG batches, do the same cheaper in one call: search_sql_users {last_name: [...], current_company_domain: [<email domains>], count} resolves many name+domain pairs at DB cost (then live-verify what you'll write). Still nothing → leave record, report.
• Needs email → cascade: user_email (batch ≤10, cheap, low yield, a MIX of personal and work addresses incl. past employers — group by profile, match domain to current company) → remainder via user_find_email_by_url {url: <vanity profile URL>} (50cr each — estimate cost on large lists first). Check valid_email/email_status in its response and write only addresses that pass; report the rest as "found, unverified".