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gap-normalization

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SOP: Unify gaps from different sources into the standard GapRecord format

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Source SKILL.md: https://github.com/yogsoth-ai/de-anthropocentric-research-engine/blob/HEAD/skills/gap-normalization/SKILL.md

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Gap Normalization

Unify gaps from different sources into the standard GapRecord format.

HARD-GATE

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": "Supporting evidence (optional)",
      "context": "Background information (optional)",
      "status": "complete | incomplete",
      "missing_fields": []
    }
  ],
  "summary": {
    "total": 0,
    "complete": 0,
    "incomplete": 0
  }
}