Criteria for evaluating paper relevance (EvaluatePapers) and filtering (FilterPapers). Use before EvaluatePapers/FilterPapers to ensure consistent scoring and selection. Score 1–5, should_retry when score < 3.
Install
npx skillscat add dozybot001/maars/paper-evaluation Install via the SkillsCat registry.
SKILL.md
Paper Evaluation
Guidelines for EvaluatePapers and FilterPapers. Use before these tools to ensure consistent scoring and selection.
EvaluatePapers Scoring (1–5)
| Score | Meaning | Action |
|---|---|---|
| 1 | Irrelevant | should_retry: true if not yet retried |
| 2 | Mostly irrelevant | should_retry: true |
| 3 | Partially relevant | Proceed; consider retry if idea is still fuzzy |
| 4 | Relevant | Proceed |
| 5 | Highly relevant | Proceed |
should_retry Logic
- score < 3 and not yet retried: Call ExtractKeywords again with refined idea, then SearchArxiv again
- score >= 3: Proceed to FilterPapers
- suggestion: Use the suggestion field to guide keyword refinement (e.g. "Try adding domain terms like 'medical imaging'")
FilterPapers Principles
- Count: Select 5–8 most relevant papers
- Order: By relevance to idea (not by arXiv date)
- Indices: Use 1-based indices from papers_summary (e.g. [1, 3, 5, 7, 9] for top 5)
- Diversity: Prefer papers that cover different aspects of the idea when possible
- Avoid: Duplicate or near-duplicate papers (same authors, same method)
Relevance Criteria
When evaluating, consider:
- Direct: Paper directly addresses the idea's core question
- Method: Paper introduces methods relevant to the idea
- Domain: Paper is in the same domain (e.g. NLP, ML)
- Recency: Prefer recent papers when idea is time-sensitive
- Citation: Highly cited papers may indicate foundational work
Output Format
EvaluatePapers returns: {"score": 1–5, "should_retry": bool, "suggestion": "string"}
FilterPapers receives indices and returns: {"count": N, "indices": [1, 2, ...]}