Record Enrichment

skill

Research accounts and contacts on the open web and stage enrichment updates as a file handed to a human.

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Record Enrichment

Research accounts and contacts across the open web and compile enrichment updates into a file that a human reviews and applies to the CRM (customer relationship management system).

When to use

  • A batch of accounts or contacts has missing firmographic fields (company size, industry, location, job title).
  • Sales or success asks for fresh context on a target account before an outreach or renewal push.
  • A list of leads needs verification and gap-filling before being loaded into the CRM.
  • Periodic hygiene sweeps to catch stale or outdated contact and account data.

Tools

  • browser-search: run web searches to locate a company's site, news, or a contact's current role.
  • browser-navigate: open the company website, LinkedIn-indexed pages, press pages, or other source pages found via search.
  • browser-extract: pull structured facts (industry, employee count, headquarters, title, recent news) off the opened page.
  • write-file: stage the compiled enrichment updates as a review-ready file (e.g. CSV or markdown table).
  • send-message: hand the staged file to a human for review and application to the CRM.

Playbook

  1. Get the list of accounts or contacts needing enrichment and the target fields to fill (from the assigning task or a provided file).
  2. For each record, run browser-search for the company or contact name plus any distinguishing detail (domain, location, role).
  3. Use browser-navigate to open the most relevant result: company homepage, about/team page, or a recent press release.
  4. Use browser-extract to capture the specific fields needed: industry, employee count, headquarters city, contact title, recent funding or news.
  5. If the first source is thin or ambiguous, repeat browser-search with a narrower query (add domain, city, or "site:" style qualifiers) and browser-navigate again to a better source.
  6. Normalize findings per record: field name, old value (if known), new value, and the source URL you pulled it from.
  7. Compile every record's findings into a single enrichment file with write-file, formatted as a table (record ID, field, current value, proposed value, source, confidence).
  8. Note any records you could not confidently enrich (no reliable source found) in a separate section of the same file rather than guessing.
  9. Send the staged file to a human via send-message, naming the CRM object type (accounts/contacts), the record count, and asking them to review and apply the updates — since updating CRM records requires PATCH/PUT access this skill does not have, the human must apply the changes.

Failure modes

  • Search returns no usable company or contact page: broaden or vary the query with browser-search; if still empty, mark the record as unresolved in the output file.
  • Extracted data conflicts across sources: prefer the most recent or most authoritative source (company's own site over third-party), and note the conflict in the file for human judgment.
  • A source page structure changes and browser-extract returns nothing useful: fall back to browser-search for an alternate source rather than guessing values.
  • Large batch runs long or hits repeated empty results: pause, note progress and remaining records in memory, and continue in the next run rather than rushing low-confidence entries.

Done when

  • A single enrichment file exists (via write-file) covering every record in the batch, each row with proposed values, sources, and confidence.
  • Unresolved records are explicitly listed rather than silently dropped.
  • A human has been notified via send-message with the file location, record count, and a clear ask to review and apply the CRM updates.