Structured peer-review of academic papers.
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Systematic literature review across multiple arXiv papers.
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Produces a structured systematic literature review (SLR) across multiple academic papers on a research topic. Given a topic query, searches arXiv, extracts structured metadata from each paper, synthesizes themes, and emits a final report with consistent citations.
Distinct from academic-paper-review: that skill does deep peer review of
a single paper. This skill does breadth-first synthesis across many papers.
Poirot note: The original deer-flow skill uses a bundled
scripts/arxiv_search.py+ subagenttasktool for parallel extraction. Poirot has neither, so this version usesbashwithcurlto the arXiv API directly + sequential single-agent extraction.
Do not use when:
academic-paper-review)Confirm with the user:
cs.CL)If user says "50+ papers", cap at 50 and explain synthesis quality degrades past that.
Use bash with curl to the arXiv API. Extract 2-3 core keywords before
searching — don't pass the full topic description as the query.
# Search arXiv (use 2-3 core keywords, not the full topic)
curl -s "https://export.arxiv.org/api/query?search_query=all:transformer+attention&max_results=20&sortBy=relevance" | python3 -c "
import sys, xml.etree.ElementTree as ET, json
ns = {'a': 'http://www.w3.org/2005/Atom'}
root = ET.fromstring(sys.stdin.read())
papers = []
for entry in root.findall('a:entry', ns):
papers.append({
'id': entry.find('a:id', ns).text.split('/')[-1],
'title': entry.find('a:title', ns).text.strip().replace('\n', ' '),
'authors': [a.find('a:name', ns).text for a in entry.findall('a:author', ns)],
'published': entry.find('a:published', ns).text[:10],
'abstract': entry.find('a:summary', ns).text.strip(),
'pdf_url': [l.get('href') for l in entry.findall('a:link', ns) if l.get('title') == 'pdf'],
'abs_url': entry.find('a:id', ns).text,
})
print(json.dumps(papers, indent=2, ensure_ascii=False))
"
Query tips:
--category (arXiv cat: field) to narrow, not stuffing field names into querysortBy=relevance (not submittedDate) for topical searchesPoirot note: The original skill delegates extraction to parallel subagents. Poirot has no subagents, so extract sequentially in your own context. For >20 papers, warn the user that sequential extraction is token-heavy and suggest splitting.
For each paper, extract from its abstract:
arxiv_idtitleauthorspublished_dateresearch_question (1 sentence — what problem the paper tackles)methodology (1-2 sentences — how they tackle it)key_findings (3-5 bullet points)limitations (1-2 sentences)Cross-paper synthesis — the report must do more than list papers:
Citation formatting (inline, no bundled templates — format manually):
APA (default):
Author, A., & Author, B. (Year). Title. arXiv preprint arXiv:XXXX.XXXXX.
IEEE:
[1] A. Author and B. Author, "Title," arXiv preprint arXiv:XXXX.XXXXX, Year.
BibTeX (arXiv papers are @misc, not @article):
@misc{authorYear,
title={Title},
author={Author, A. and Author, B.},
year={Year},
eprint={XXXX.XXXXX},
archivePrefix={arXiv}
}
Save the full report to .poirot/outputs/slr-<topic-slug>-<YYYYMMDD>.md via
write_file. Present via present_files.
In the chat message, show a short preview:
Do NOT dump the full report inline — per-paper annotations and references belong in the file.
# Systematic Literature Review: [Topic]
## Executive Summary
[3-5 sentence overview]
## Methodology
[Search strategy, paper count, inclusion criteria]
## Themes
### Theme 1: [Name]
[Cross-paper analysis with citations]
### Theme 2: [Name]
[...]
## Convergences
[Findings multiple papers agree on]
## Disagreements
[Where papers diverge]
## Gaps
[What the literature doesn't address]
## Paper Annotations
### [Paper 1 Title]
- **Authors**: ...
- **Year**: ...
- **Research Question**: ...
- **Methodology**: ...
- **Key Findings**: ...
- **Limitations**: ...
### [Paper 2 Title]
[...]
## References
[Formatted per chosen citation style]
"diffusion models in computer vision" → 0 results.
Use 2-3 core keywords + category filter.sortBy=relevance.No reviews yet. Tell others what you thought.
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Structured peer-review of academic papers.
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