소스 정보
- 저장소
- HezaoHezao/poirot
- 최근 소스 활동
- 2026년 7월 28일 12:58
- 감지된 SKILL.md 언어
- 영어
- 스타
- 212
- 포크
- 15
설치 방법
기본적으로 소스를 먼저 확인하는 Prompt가 선택됩니다. 직접 명령으로 전환하거나 로컬 사본을 다운로드할 수도 있습니다.
소스 파일 검토
설치 여부를 결정하기 전에 SKILL.md와 SkillsMP에 표시된 보조 파일을 읽어 보세요.
메뉴
기본적으로 소스를 먼저 확인하는 Prompt가 선택됩니다. 직접 명령으로 전환하거나 로컬 사본을 다운로드할 수도 있습니다.
설치 여부를 결정하기 전에 SKILL.md와 SkillsMP에 표시된 보조 파일을 읽어 보세요.
SOC 직업 분류 기준
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
직접 명령은 검토 Prompt를 거치지 않습니다. 실행하기 전에 소스를 확인하세요.
npx skills add https://github.com/HezaoHezao/poirot --skill systematic-literature-review명령은 한 줄로 유지됩니다. 복사하기 전에 가로로 스크롤해 전체 내용을 확인하세요.
로컬 사본을 원하시나요? SkillsMP에서 현재 제공할 수 있는 파일을 다운로드하세요.
SKILL.md 표시 중
| name | systematic-literature-review |
| description | Systematic literature review across multiple arXiv papers. |
| allowed-tools | ["bash","web_search","browse_page","write_file","present_files"] |
| enabled | true |
| related-skills | ["arxiv","academic-paper-review"] |
| license | MIT |
| author | Adapted from deer-flow (Bytedance, MIT) |
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.