Attio Smart List Builder

Task

Build targeted lists in Attio by discovering the workspace schema, understanding available fields, querying records that match specific criteria, and adding qualified records to a target list. Use @Attio/List ObjectsName it "Attio/List Objects" and call it with @Attio/List Objects to discover all objects in the workspace, @Attio/Get ObjectName it "Attio/Get Object" and call it with @Attio/Get Object to understand each object's fields and structure, @Attio/List Records and @Attio/Search RecordsName it "Attio/Search Records" and call it with @Attio/Search Records to find records matching the user's criteria, and @Attio/Add List EntryName it "Attio/Add List Entry" and call it with @Attio/Add List Entry to add qualified records to the target list.

Example: Build a list of enterprise companies in fintech with more than 500 employees from my Attio workspace.

Input

The user will provide:

1. The criteria for the target list (industry, size, stage, location, etc.)
2. The name of the list to add records to (or create)
3. Optional: exclusion criteria (e.g., "exclude existing customers")
4. Optional: maximum number of records to add

Example: "Build a list of SaaS companies with 100-500 employees that are in the Proposal stage" or "Find all contacts who are VPs or C-level at companies in healthcare"

Context

Schema Discovery

Why it matters:

• Every Attio workspace has different objects and custom fields
• You need to understand the schema before querying effectively
• Custom fields like "ICP Tier" or "Lead Source" vary by workspace
• Object relationships determine how to cross-reference data

Discovery process:

1. List all objects to see what exists (People, Companies, custom objects)
2. Get each relevant object to understand its fields
3. Map field names to user criteria (e.g., "size" might be "Employee Count" or "Company Size")
4. Identify filterable fields vs. free-text fields

Query Strategy

1. Discover the workspace schema with List Objects and Get Object
2. Map user criteria to actual field names
3. Search for records matching the primary criteria
4. Filter results by secondary criteria
5. Check for duplicates against the target list
6. Add qualified records with relevant attributes

What Makes a Good List

• Records match ALL specified criteria, not just some
• No duplicates (check before adding)
• Include enough context on each entry for follow-up (company, role, last interaction)
• Ordered by relevance or priority
• Exclusions are properly applied

Output

Smart List Builder Report:

List Name: [Target List Name] Criteria: [Natural language description of criteria] Records Found: [count] Records Added: [count] Duplicates Skipped: [count]

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Schema Discovery:

| Object | Fields Used | Record Count | |--------|------------|-------------| | Companies | Industry, Size, Stage | [count] | | People | Title, Department | [count] |

Fields Mapped:

• "Industry" mapped to field: [actual field name]
• "Size" mapped to field: [actual field name]
• "Stage" mapped to field: [actual field name]

---

Records Added to List:

| # | Company | Industry | Size | Stage | Key Contact | |---|---------|----------|------|-------|-------------| | 1 | [Company] | [Industry] | [Size] | [Stage] | [Name, Title] | | 2 | [Company] | [Industry] | [Size] | [Stage] | [Name, Title] | | 3 | [Company] | [Industry] | [Size] | [Stage] | [Name, Title] |

---

Records Excluded:

| Company | Reason | |---------|--------| | [Company] | Already on target list | | [Company] | Existing customer (excluded by criteria) | | [Company] | Missing required field: [field] |

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List Quality Check:

• All records match criteria: [Yes/No]
• Duplicate check passed: [Yes/No]
• Exclusions applied: [Yes/No]
• Records with complete data: [X] of [Y]

Summary:

• [X] records added to "[List Name]"
• [Y] records skipped (duplicates or exclusions)
• [Z] records need manual review (partial criteria match)

Next Steps:

1. Review [X] partial matches for manual inclusion
2. Assign list to [owner] for outreach
3. Re-run in [timeframe] with updated criteria