gap-normalization

name	gap-normalization
description	SOP: Unify gaps from different sources into the standard GapRecord format
version	1.0.0
category	hypothesis-formation
type	sop
campaign	gap-prioritization
input	Raw gap entries from different sources (string list, structured objects, or mixed format)
output	GapRecord[] — array of standardized gap records
dependencies	{"skills":["subagent-spawning"]}

Gap Normalization

Unify gaps from different sources into the standard GapRecord format.

HARD-GATE

- Input must not be empty: must contain at least 1 raw gap entry - Each output GapRecord must contain non-empty id, title, description, domain, and source fields - If any required field cannot be extracted, the entry is marked `status: "incomplete"` rather than silently dropped

Pipeline

1. Precondition check: Verify input is non-empty; count the entries; identify the input format (plain text / JSON / mixed)
2. Format identification: Determine the format type of each entry — free-text description, partially structured object, fully structured object
3. Field extraction: Extract id (generate or reuse), title, description, domain, source, evidence, context from each raw entry
4. Normalization: Denoise and truncate title (≤120 characters); complete description into full sentences; map domain to a controlled vocabulary
5. Validation: Check the completeness of required fields for each GapRecord; tag incomplete entries with status: "incomplete" and record the missing fields
6. Output: Return GapRecord[] and a processing summary (total / complete / incomplete)

Output Format

{
  "records": [
    {
      "id": "gap_001",
      "title": "Short title (≤120 characters)",
      "description": "Full description (1-3 sentences)",
      "domain": "Domain label",
      "source": "Source identifier",
      "evidence":