| name | document-screening |
| version | 1.0.0 |
| description | Screen inbound documents for completeness, policy risk, and routing readiness before extraction or case workflows |
| author | Happy Technologies LLC |
| tags | ["document","screening","intake","triage","compliance","routing","risk"] |
| platforms | ["claude-code","claude-desktop","chatgpt","cursor","any"] |
| tools | {"mcp":["SN-Query-Table","SN-Read-Record","SN-Create-Record","SN-Update-Record","SN-Add-Work-Notes"],"rest":["/api/now/table/sys_attachment","/api/now/table/sn_doc_template","/api/now/table/sn_doc_intelligence_extraction","/api/now/table/sn_doc_intelligence_extraction_result","/api/now/table/task"],"native":["Bash"]} |
| complexity | advanced |
| estimated_time | 15-30 minutes |
Document Screening
Overview
This skill adds a document screening stage before extraction or fulfillment workflows in ServiceNow®. It helps teams:
- Validate document completeness and format readiness
- Identify policy-sensitive or high-risk submissions early
- Route documents to automated extraction or human review lanes
- Record auditable screening rationale in work notes
Use this when your intake process receives mixed document quality, high volume, or compliance-sensitive content.
Prerequisites
- Roles:
admin, sn_doc_intelligence_admin, or equivalent intake operations role
- Plugins: Document Intelligence app enabled where extraction is required
- Access: Read/write access to
sys_attachment, sn_doc_template, sn_doc_intelligence_extraction, and task
- Inputs: Defined screening rules (required fields, prohibited content, confidence thresholds)
Procedure
Step 1: Build the Intake Queue
Collect newly submitted documents and classify by source, type, and priority.
Using MCP:
Tool: SN-Query-Table
Parameters:
table_name: sys_attachment
query: sys_created_on>=javascript:gs.hoursAgoStart(24)^table_nameISNOTEMPTY
fields: sys_id,file_name,content_type,size_bytes,table_name,table_sys_id,sys_created_on,sys_created_by
limit: 100
order_by: sys_created_on
Using REST API:
GET /api/now/table/sys_attachment?sysparm_query=sys_created_on>=javascript:gs.hoursAgoStart(24)^table_nameISNOTEMPTY&sysparm_fields=sys_id,file_name,content_type,size_bytes,table_name,table_sys_id,sys_created_on,sys_created_by&sysparm_limit=100
Step 2: Match to Screening Template
Map each document to a known template and required evidence checklist.
Decision points:
- If template exists and is active -> continue to automated screening
- If template missing -> create review task for template owner
- If unsupported format -> route to manual conversion queue
Step 3: Run Screening Checks
Evaluate required attributes and extraction confidence signals.
Using MCP:
Tool: SN-Query-Table
Parameters:
table_name: sn_doc_intelligence_extraction_result
query: extraction=[extraction_sys_id]
fields: field_name,extracted_value,confidence_score,validation_status
limit: 200
Checks to apply:
- Required field presence
- Minimum confidence threshold (for example 0.85)
- Prohibited content indicators (policy-defined)
- Duplicate submission checks by hash or metadata fingerprint
Step 4: Assign Screening Outcome
Set one of three outcomes:
- Pass: Continue to extraction-to-record workflow
- Conditional Pass: Continue with mandatory human verification
- Fail: Route for remediation and return requester guidance
Using MCP:
Tool: SN-Create-Record
Parameters:
table_name: task
fields:
short_description: "Document screening failed: missing required evidence"
description: "File [file_name] failed screening checks. Review checklist and resubmit."
priority: 3
state: 1
Step 5: Record Audit Notes and Next Action
Document the screening rationale and the exact routing decision.
Tool Usage
| Tool | Purpose |
|---|
SN-Query-Table | Retrieve attachments, templates, and extraction results |
SN-Read-Record | Inspect source record context for submitted documents |
SN-Create-Record | Create remediation tasks for failed screenings |
SN-Update-Record | Update screening status and routing fields |
SN-Add-Work-Notes | Persist decision rationale for auditability |
Best Practices
- Keep screening criteria versioned and tied to document type
- Treat confidence thresholds as policy controls, not hardcoded assumptions
- Separate business validation failures from technical OCR/extraction failures
- Capture decision evidence to support audit and dispute handling
- Periodically review false positives and false negatives to refine rules
Troubleshooting
Screening Queue Is Empty
Symptom: No documents appear in intake query.
Cause: Time-window filter or source table filter is too restrictive.
Solution: Broaden query window and verify attachment source integration.
High False Failure Rate
Symptom: Most documents route to fail or conditional pass.
Cause: Confidence threshold or required field set is misaligned with real inputs.
Solution: Tune thresholds by document type and add exception handling rules.
Related Skills
document/document-extraction - Extract structured data from documentsdocument/smart-documents - Manage document templates and generationprocurement/invoice-management - Apply screened invoice documents to AP workflows
References
