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SKILL verified Apache-2.0 Self-run

Novelty Assessment

skill-aravindan20-claude-research-paper-os-novelty-assessment · by ARAVINDAN20

Assess research idea novelty through systematic literature search. Multi-round search-evaluate loops with harsh critic persona. Binary novel/not-novel decision with justification. Use before committing to a research direction.

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Install

$ agentstack add skill-aravindan20-claude-research-paper-os-novelty-assessment

✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • ✓ Prompt-injection patterns
  • ✓ Secret / credential exfiltration
  • ✓ Dangerous shell & filesystem operations
  • ✓ Untrusted network calls
  • ✓ Known-malicious package signatures

What it can access

  • ✓ Network access No
  • ✓ Filesystem access No
  • ✓ Shell / process execution No
  • ✓ Environment & secrets No
  • ✓ Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

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Reliability & compatibility

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Declared compatibility

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Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.

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About

Novelty Assessment

Rigorously assess whether a research idea is novel through systematic literature search.

Input

  • $0 — Research idea description, title, or JSON file

Scripts

Automated novelty check

python ~/.claude/skills/idea-generation/scripts/novelty_check.py \
  --idea "Your research idea description" \
  --max-rounds 10 --output novelty_report.json

Literature search

python ~/.claude/skills/deep-research/scripts/search_semantic_scholar.py \
  --query "relevant search query" --max-results 10

References

  • Assessment prompts and criteria: ~/.claude/skills/novelty-assessment/references/assessment-prompts.md

Workflow

Step 1: Understand the Idea

  • Identify the core contribution
  • List the key technical components
  • Determine the research area and subfield

Step 2: Multi-Round Literature Search (up to 10 rounds)

For each round:

  1. Generate a targeted search query
  2. Search Semantic Scholar / arXiv / OpenAlex
  3. Review top-10 results with abstracts
  4. Assess overlap with the idea
  5. Decide: need more searching, or ready to decide

Step 3: Make Decision

  • Novel: After sufficient searching, no paper significantly overlaps
  • Not Novel: Found a paper that significantly overlaps

Step 4: Position the Idea

If novel, identify:

  • Most similar existing papers (for Related Work)
  • How the idea differs from each
  • The specific gap this idea fills

Harsh Critic Persona

Be a harsh critic for novelty. Ensure there is a sufficient contribution
for a new conference or workshop paper. A trivial extension of existing
work is NOT novel. The idea must offer a meaningfully different approach,
formulation, or insight.

Output Format

{
  "decision": "novel" | "not_novel",
  "confidence": "high" | "medium" | "low",
  "justification": "After searching X rounds...",
  "most_similar_papers": [
    {"title": "...", "year": 2024, "overlap": "..."}
  ],
  "differentiation": "Our idea differs because..."
}

Rules

  • Minimum 3 search rounds before declaring novel
  • Try to recall exact paper names for targeted queries
  • A paper idea is NOT novel if it's a trivial extension
  • Consider both methodology novelty AND application novelty
  • Check for concurrent/recent arXiv submissions

Related Skills

  • Upstream: [literature-search](../literature-search/), [deep-research](../deep-research/)
  • Downstream: [idea-generation](../idea-generation/), [research-planning](../research-planning/)
  • See also: [related-work-writing](../related-work-writing/)

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

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Versions

  • v0.1.0 Imported from the upstream source.