bio-innovation-check

SkillMedia

Estimate whether a research idea is sufficiently novel for a strong methods-style paper by expanding the topic and searching the literature.

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the bio-innovation-check skill

About this capability

A curated collection of AI agent skills for biomedical research, covering genomics, proteomics, single-cell analysis, clinical AI, and protein design.

What this skill tells your AI

The instructions your AI receives, as published by biotender-max/awesome-bio-agent-skills in skills/bioclaw/bio-innovation-check/SKILL.md and read by ahel’s review.

Step 1: Innovation assessment (创新性检测)

Estimate whether a research idea is sufficiently novel for a strong methods-style paper by expanding the topic and searching the literature.

Purpose

  1. Generate multiple topic variants and synonyms
  2. Search PubMed, bioRxiv, and arXiv q-bio
  3. Count and de-duplicate related papers
  4. Assign a novelty level
  5. Suggest how to sharpen or reposition the idea if needed

Input Format

topic: [research topic]

Workflow

Step 1.1: Topic expansion

Use several types of expansions:

  1. Core term substitution
  2. Phrase re-ordering
  3. Parent / child concept expansion
  4. Adjacent-domain vocabulary borrowing
  5. Method keyword enrichment

Example:

  • "spatial multi-omics integration"
  • "integration of spatial transcriptomics and proteomics"
  • "spatial multi-modal data fusion"

Target output: 15-20 topic variants by default.

Step 1.2: Literature search

Search these sources:

  • PubMed
  • bioRxiv
  • arXiv q-bio

Suggested pattern:

for variant in topic_variants:
    results = search(variant, platforms=["PubMed", "bioRxiv", "arXiv"])
    all_papers.extend(results)

unique_papers = deduplicate(all_papers, threshold=0.8)

Step 1.3: Novelty scoring

Use a simple first-pass threshold:

if paper_count <= 2:
    level = "strong novelty / methods-journal candidate"
elif paper_count <= 5:
    level = "promising but needs sharpening"
else:
    level = "needs repositioning"

This is only a heuristic. Final judgment should still use human reasoning.

Step 1.4: Repositioning suggestions

If the project is not yet strong enough, suggest improvements from one or more of these angles:

  1. Method angle
  2. Task angle
  3. Data / validation angle
  4. Analysis angle

Output Format

# Innovation Assessment Report

## Search Strategy
- Number of variants:
- Search sources:
- Search date:

## Topic Variants
| No. | Variant |
|-----|---------|
| 1 | ... |

## Search Results Summary
| Variant | PubMed | bioRxiv | arXiv | Total |
|---------|--------|---------|-------|-------|
| ... | ... | ... | ... | ... |

## De-duplicated Counts
- Total related studies:
- Published papers:
- Preprints:

## Novelty Decision
- Level:
- Reason:

## Representative Related Work
1. [title]
   - Source:
   - Year:
   - Main method:
   - Overlap with the proposed idea:

## Repositioning Suggestions
1. Method:
2. Task:
3. Data / validation:

## Next Step
- If novelty is strong: continue to Step 2
- If the idea needs sharpening: refine and continue
- If it needs repositioning: redesign before proceeding

Usage

/bio-innovation-check "spatial multi-omics integration"

Notes

  1. Use timeouts because search latency varies by source.
  2. De-duplication matters; otherwise novelty will be overestimated or underestimated.
  3. Overlap scoring still needs human judgment.
  4. Journal-specific novelty expectations can differ by field.

Signals

GitHub stars
178
Forks
32
Last commit
Jul 2026
Advanced
Catalog kind
skill
Gateway key
bio-innovation-check
Source
github.com/biotender-max/awesome-bio-agent-skills