/novelty-assessment
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.
$ npx -y skills add lingzhi227/agent-research-skills --skill novelty-assessment --agent claude-codeHow it fires
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/novelty-assessment
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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.
SKILL.md
novelty-assessment.SKILL.mdname: novelty-assessment
description: 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.
argument-hint: [idea]
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/)
Read more
name: novelty-assessment description: 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. argument-hint: [idea]
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/)
31 skills for Claude Code covering the full academic research paper lifecycle — from literature search to slide generation — plus GitHub repository analysis for research topics. Extracted from 17 GitHub repos studying LLM-agent-driven research automation.
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Open skill - /citation-management
Manage BibTeX citations for LaTeX papers. Harvest missing citations from a draft using Semantic Scholar, validate cite keys against .bib files, deduplicate entries, and format bibliography. Use when working with references, BibTeX, or citations.
Open skill - /code-debugging
Debug experiment code with structured error analysis. Categorize errors, apply targeted fixes with retry logic, and use reflection to prevent recurring issues. Use when experiment code fails or produces incorrect results.
Open skill - /data-analysis
Generate statistical analysis code with 4-round review. Select appropriate statistical tests, interpret results, and produce analysis reports with p-values, effect sizes, and confidence intervals. Use when analyzing experimental data for a paper.
Open skill

