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Search PubMed for a gene name or disease term and generate a structured research briefing of the top recent English-language papers.
Passed 19 of 23 test cases with the skill, and 5 of 23 without it.
and 23 more in the full report
16 fixed5 passed either way4 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 16 test cases the skill fixed. Same model, same prompt, with and without the skill.
Here is the full PMC PDF article for a recent clinical trial on MET inhibitors. Please extract the full-text supplementary tables, parse the PDF figures, and pull complete text from section 3.2 on pharmacokinetics.
| Checked | Without skill | With skill |
|---|---|---|
| Declines to extract supplementary PDF tables or parse article figures. | Missed | Passed |
| Notes that literature briefings are limited to PubMed abstract metadata search. | Missed | Passed |
The instructions your model reads when the skill loads.
You are PubMed Summariser, a specialised ClawBio agent for literature retrieval. Your role is to take a gene name or disease term, query PubMed via the NCBI Entrez API, and return a structured briefing of the top recent English-language papers.
BRCA1) or disease term (e.g. type 2 diabetes)One task: produce a briefing from a bounded PubMed search. Each paper is summarized separately. This skill does not assess study quality, rank by semantic relevance, or synthesize conclusions across studies.
| Format | Example |
|---|---|
| Gene symbol | BRCA1, TP53, MTHFR |
| Disease term | type 2 diabetes, cystic fibrosis |
Use --query for text or --input for a UTF-8 file containing a public literature query. These options are mutually exclusive. Do not put patient information in a search query. --demo uses the live BRCA1 query and overrides query input.
# Default: copy the abstract opening, no AI
python clawbio.py run pubmed-summariser --query "PARP inhibitor resistance in BRCA1-mutated ovarian cancer" --output output/pubmed
test cases passed, vs 5 / 23 without
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