How to install novelty-assessment
npx skills add https://github.com/lingzhi227/agent-research-skills --skill novelty-assessmentFull instructions (SKILL.md)
Source of truth, from lingzhi227/agent-research-skills.
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:
- Generate a targeted search query
- Search Semantic Scholar / arXiv / OpenAlex
- Review top-10 results with abstracts
- Assess overlap with the idea
- 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, deep-research
- Downstream: idea-generation, research-planning
- See also: related-work-writing
Related skills
More from lingzhi227/agent-research-skills and the wider catalog.
literature-review
Conduct comprehensive literature reviews using multi-perspective dialogue simulation. Generate diverse expert personas, conduct grounded Q&A conversations, and synthesize findings into structured knowledge. Use when starting a new research project or writing a survey section.
literature-search
Search academic literature using Semantic Scholar, arXiv, and OpenAlex APIs. Returns structured JSONL with title, authors, year, venue, abstract, citations, and BibTeX. Use when the user needs to find papers, check related work, or build a bibliography.
figure-generation
Generate publication-quality scientific figures using matplotlib/seaborn with a three-phase pipeline (query expansion, code generation with execution, VLM visual feedback). Handles bar charts, line plots, heatmaps, training curves, ablation plots, and more. Use when the user needs figures, plots, or visualizations for a paper.
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.
latex-formatting
Handle LaTeX formatting, templates, and styling for academic papers. Set up conference templates (ICML, ICLR, NeurIPS, AAAI, ACL), fix formatting issues, manage packages, and ensure venue-specific compliance. Use when the user needs to set up a paper template, fix LaTeX formatting, or prepare for submission.
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.