hypothesis-formation-novelty-scoring

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SOP: Assess the novelty potential of a research gap, identify differentiation directions, and output a score

yogsoth-ai By yogsoth-ai schedule Updated 6/16/2026

name: hypothesis-formation-novelty-scoring description: 'SOP: Assess the novelty potential of a research gap, identify differentiation directions, and output a score' version: 1.0.0 category: hypothesis-formation type: sop campaign: gap-prioritization input: GapRecord — a single standardized gap record output: NoveltyScore — includes dimension scores, composite score (1-5), list of differentiation directions, and rationale dependencies: sops: - hypothesis-formation-paper-research - hypothesis-formation-paper-search - hypothesis-formation-web-research - hypothesis-formation-web-search

Novelty Scoring

Assess the novelty potential of a research gap, identify differentiation directions, and output a score.

HARD-GATE

- Input must be a GapRecord with status: "complete" - The output composite score must be within the interval [1, 5] - The differentiation_directions list must not be empty (at least 1 entry) - Each sub-dimension must be accompanied by at least 1 sentence of textual rationale

Pipeline

  1. Precondition check: Verify the completeness of the input GapRecord; extract keywords for literature scanning
  2. Existing-work scan: Use literature-engine and web-browsing to retrieve recent work (past 3 years) directly related to the gap; record existing solutions and partial solutions
  3. Differentiation-space identification: Compare existing work against the gap's full requirements to identify angles not yet covered (methods, data, problem setup, evaluation dimensions, etc.)
  4. Innovation-potential assessment: Judge the likelihood of producing a genuinely novel contribution within the differentiation space (1-5); consider: size of the white space, competition density
  5. Frontier assessment: Judge whether the gap is at the frontier of the field rather than already well-studied (1-5)
  6. Composite scoring: Equal-weight average of the two dimensions, keeping one decimal place; list specific differentiation directions
  7. Output: Return the NoveltyScore object

Output Format

{
  "gap_id": "gap_001",
  "existing_work_summary": "Brief summary of existing work (2-3 sentences)",
  "dimension_scores": {
    "innovation_potential": { "score": 4, "rationale": "..." },
    "frontier_position": { "score": 4, "rationale": "..." }
  },
  "composite_score": 4.0,
  "differentiation_directions": [
    "Direction 1: ...",
    "Direction 2: ..."
  ],
  "overall_rationale": "Overall basis (2-4 sentences)"
}

Available SOPs

Optional, no fixed order; the final leaf is always a sop.

SOP When to use
hypothesis-formation-paper-research Import SOP: Deep literature research, raw full text + PDF Q&A (from literature-engine)
hypothesis-formation-paper-search Import SOP: Medium-depth literature search, AI summary report (from literature-engine)
hypothesis-formation-web-research Import SOP: deep web research, full-text fetching and analysis (from web-browsing)
hypothesis-formation-web-search Import SOP: quick web scan, discover URLs and snippets (from web-browsing)
Install via CLI
npx skills add https://github.com/yogsoth-ai/de-anthropocentric-research-engine --skill hypothesis-formation-novelty-scoring
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