Literature Review
neflibata-feng/MyArxiv-Agent
Conduct comprehensive, systematic literature reviews using multiple academic databases (PubMed, arXiv, bioRxiv, Semantic Scholar, etc.).
Read an academic paper end to end and extract professional research insights, figures, metadata, and critique.
$ npx skills add ShZhao27208/Aut_Sci_Write --skill sci-extract -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install ShZhao27208/Aut_Sci_Write sci-extract --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/ShZhao27208/Aut_Sci_Write.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/sci-extract .claude/skills/sci-extract && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "sci-extract" agent skill from https://github.com/ShZhao27208/Aut_Sci_Write/tree/main/skills/sci-extract into .claude/skills/sci-extract/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sci-extract", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/ShZhao27208/Aut_Sci_Write/tree/main/skills/sci-extractType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add ShZhao27208/Aut_Sci_Write --skill sci-extract -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install ShZhao27208/Aut_Sci_Write sci-extract --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ShZhao27208/Aut_Sci_Write.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/sci-extract .agents/skills/sci-extract && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "sci-extract" agent skill from https://github.com/ShZhao27208/Aut_Sci_Write/tree/main/skills/sci-extract into .agents/skills/sci-extract/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sci-extract", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add ShZhao27208/Aut_Sci_Write --skill sci-extract -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install ShZhao27208/Aut_Sci_Write sci-extract --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ShZhao27208/Aut_Sci_Write.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/sci-extract .cursor/skills/sci-extract && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "sci-extract" agent skill from https://github.com/ShZhao27208/Aut_Sci_Write/tree/main/skills/sci-extract into .cursor/skills/sci-extract/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sci-extract", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/ShZhao27208/Aut_Sci_Write.git --path skills/sci-extract--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add ShZhao27208/Aut_Sci_Write --skill sci-extract -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install ShZhao27208/Aut_Sci_Write sci-extract --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ShZhao27208/Aut_Sci_Write.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/sci-extract .gemini/skills/sci-extract && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "sci-extract" agent skill from https://github.com/ShZhao27208/Aut_Sci_Write/tree/main/skills/sci-extract into .gemini/skills/sci-extract/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sci-extract", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install ShZhao27208/Aut_Sci_Write sci-extractInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add ShZhao27208/Aut_Sci_Write --skill sci-extract -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/ShZhao27208/Aut_Sci_Write.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/sci-extract .github/skills/sci-extract && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "sci-extract" agent skill from https://github.com/ShZhao27208/Aut_Sci_Write/tree/main/skills/sci-extract into .github/skills/sci-extract/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sci-extract", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add ShZhao27208/Aut_Sci_Write --skill sci-extract -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install ShZhao27208/Aut_Sci_Write sci-extract --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ShZhao27208/Aut_Sci_Write.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/sci-extract .opencode/skills/sci-extract && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "sci-extract" agent skill from https://github.com/ShZhao27208/Aut_Sci_Write/tree/main/skills/sci-extract into .opencode/skills/sci-extract/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sci-extract", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
sci-extractRead an academic paper end to end and extract professional research insights, figures, metadata, and critique.
Sci Extract is an agent skill from ShZhao27208/Aut_Sci_Write. Read an academic paper end to end and extract professional research insights, figures, metadata, and critique. Use this skill whenever the user shares a scientific paper, review paper, survey paper, systematic review, meta-analysis, scoping review, arXiv link, DOI, PDF, or pasted paper text and asks to read, summarize, analyze, extract, digest, review, critique, or explain it. For original research papers, produce a modified Heilmeier analysis. For review literature, produce a field-map extraction covering scope…
Its SKILL.md is about 5.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 15 other files (for example `README.md`, `_meta.json` and `extract_core_insights.py`).
It sits in Research & Science, covering Literature review and Academic paper search. It works with arXiv. The repository describes itself as: Academic research skills suite for AI Agent — literature search/download (WoS+Elsevier+Springer), PDF extraction, figure cropping, review writing, Zotero sync, and PPT/Html… The licence is MIT.
2 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit 357766f. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Ships script files (Python), which the agent can run.
Shell commands in SKILL.md call:
pythonFrom the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Sci Extract loads about 5.8k tokens when it runs. Until then it costs about 171 tokens; SKILL.md has 2,792 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check noted patterns worth knowing about, such as sudo or a known installer.
redentials are read from the suite-wide `.env` at `~/.aut_sci_write/.env`; run `python harvest_paper.py --sources` to seAutomated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from ShZhao27208/Aut_Sci_Write at commit 357766f, republished under its MIT licence (© ShZhao27208). 2,792 words, ~5,777 tokens.
.claude/skills/sci-extract/SKILL.md (or your agent's skills folder). This skill also uses 14 other files; get the full folder from GitHub.Professional extraction of core insights and figures from scientific PDF papers.
Note: This skill includes contributions from two authors. See Copyright & License section for details.
raw.md instead of leaving you to read a PDF.Always read the paper fresh. Never rely on memory of the paper, even if the title looks familiar.
| Input type | Action |
|---|---|
| DOI, PMID, PMCID, arXiv ID, title, or query | Run the harvester (Mode 3 below). It writes raw.md with the full text and figures embedded, which you then read instead of a PDF. |
PDF in /mnt/user-data/uploads/ | Read it via the appropriate tool (see the pdf-reading skill if available). |
| arXiv link | Extract the arXiv ID and run the harvester. Fall back to web_fetch on the abstract page if the harvester returns no full text. |
| Pasted text in the chat | Use directly. |
| Just a title with no link | Run the harvester with the title; it lists candidates and confirms the match before fetching. Do not guess the paper. |
If the paper is long, first classify the paper type, then prioritize the sections relevant to that type. For original research, prioritize abstract, introduction, method/theory, experiments, conclusion. For review literature, prioritize abstract, introduction, search/selection methods, taxonomy/classification sections, major thematic sections, summary tables, limitations, and future perspectives.
Before choosing the extraction template, classify the paper as one of:
Use the title, abstract, introduction, and section headings. Signals for review literature include "review", "survey", "systematic review", "meta-analysis", "scoping review", "bibliometric", "taxonomy", "current status", "recent progress", "challenges", "future perspectives", and broad comparison tables.
If the paper is a review, survey, systematic review, meta-analysis, scoping review, perspective, or tutorial overview, do not force it into the original-research Heilmeier template. Use the Review Literature Extraction Mode below. Original research asks "what did this paper do and prove"; review literature asks "how does this paper map the field and judge the evidence".
Answer each of the seven questions below as a labeled subsection, in order. For each question, the rules differ on (a) whether your own evaluation is allowed and (b) whether external citations are allowed. Read the rules carefully before writing each subsection.
Open with a one-sentence statement of the paper's contribution written for a smart non-specialist, with absolutely no jargon. Ban acronyms and any technical term a first-year undergrad would not know. If a term of art is unavoidable, define it parenthetically in plain words. Then add one or two sentences expanding the objective in slightly more technical language.
Opinions allowed: no. Stay faithful to the paper. External citations allowed: no.
Describe the real-world or scientific problem the paper addresses, then give a brief overview of how the field handles it at the time of the paper, and what the limitations are. This is meant to be a self-contained landscape paragraph, not a literature review. Cover the main competing approaches in plain prose.
Opinions allowed: a small amount, only if it sharpens the framing of the limits. External citations allowed: no. Do not search for or cite outside sources here. Just give an overview from the paper and your general knowledge of the field.
This is the technical heart of the response and absorbs what would otherwise be a "method" summary. Cover, in this order:
\left and \right for brackets, keep inline math on one line, and prefer standard LaTeX notation.Opinions allowed: NO. This subsection is strictly about what the paper says and proposes. Save your evaluation for questions 4, 5, and 6. External citations allowed: no.
Discuss the impact: which communities benefit, what becomes possible, and whether this paper has actually shifted the field since publication.
Opinions allowed: yes. This is one of the questions where your judgment matters most.
External citations allowed: yes, and encouraged when assessing post-publication impact (adoption by other groups, follow-up papers, deployment). Every external citation must come from a web_search or web_fetch you actually ran in this turn.
Cover both the risks the paper itself acknowledges and the ones you see independently. Be concrete: contamination, reward hacking, failure modes, narrow benchmarks, scaling, reproducibility.
Opinions allowed: yes. External citations allowed: yes, when an outside source materially supports a risk claim.
Interpret as compute, data, engineering effort, or deployment cost, depending on the paper. State which interpretation you are using. Pull whatever numbers the paper provides (token counts, batch sizes, GPU hours, data volumes) and translate into a rough sense of "what would it take to reproduce this".
Opinions allowed: yes, especially for the "what would it take to reproduce" framing. External citations allowed: yes. Be careful not to conflate this paper's costs with related work by the same authors. If you cite a cost figure, state exactly which paper or model that figure refers to.
Cover the experimental setup (benchmarks, datasets, baselines, metrics, ablations) and the headline results. This subsection answers "what are the criteria for success and did the paper meet them". Note any conspicuous gap between claims and evidence.
Opinions allowed: small amount, only for noting gaps between claims and evidence. External citations allowed: no.
Use this mode for review papers, survey papers, systematic reviews, meta-analyses, scoping reviews, perspective reviews, and tutorial overviews. Do not ask for a single proposed method, single experiment, or reproduction cost unless the review itself is about benchmarking or a method protocol. The goal is to reconstruct the paper's map of the field and assess how reliable that map is.
Return the following labeled subsections, in order:
State what field, problem, population, method family, material class, data type, or application area the paper reviews. Include explicit inclusion and exclusion boundaries when the paper gives them. If the scope is vague, say so.
Explain the motivation: field fragmentation, rapid growth, conflicting evidence, unclear taxonomy, translation gap, reproducibility problem, new technology, or practical need. Keep this faithful to the paper before adding your own judgment.
Extract the categories the authors use to organize the field. Preserve the author's hierarchy and terminology. If there are multiple taxonomies, separate them. For each category, give a one-sentence meaning and the main representative approaches, study types, models, materials, datasets, diseases, interventions, or applications.
For systematic reviews, scoping reviews, and meta-analyses, extract databases searched, search period, search terms if available, inclusion criteria, exclusion criteria, screening process, final included study count, and any quality-assessment tool. For narrative reviews and surveys, state whether the search and selection method is unspecified or informal.
Summarize the major research directions covered by the review. Distinguish mature areas from emerging areas. Mention important datasets, benchmark tasks, experimental platforms, clinical cohorts, model families, materials, or instruments when they are central to the review.
Extract what the reviewed literature broadly agrees on. Separate strong consensus from tentative patterns. If the review does not clearly identify consensus, say so instead of inventing one.
Extract where studies conflict, where mechanisms or interpretations differ, and what explanations the review gives for inconsistent findings. For meta-analyses, include heterogeneity statistics and subgroup/sensitivity findings if reported.
Assess the reliability of the reviewed evidence using what the paper reports: study design, sample size, data quality, benchmark leakage, confounding, publication bias, reproducibility, missing controls, annotation quality, evaluation metrics, or risk-of-bias tools. Mark your own assessment with a first-person phrase such as "My analysis is that," so paper content and your critique remain separate.
Extract the open questions, missing datasets, missing experiments, technical barriers, translation barriers, standardization needs, clinical/industrial/policy needs, and concrete future directions identified by the authors. Add your own prioritized gap assessment only with a first-person marker.
Identify taxonomy figures, workflow diagrams, evidence maps, PRISMA flow diagrams, summary tables, comparison tables, benchmark tables, and meta-analysis forest/funnel plots. For each important figure or table, explain what role it plays in understanding the review. If figure extraction is requested, crop or save the relevant figure/table regions when tooling is available.
End with three to five concise takeaways. Each takeaway should describe something a researcher can use: a field structure, a reliable conclusion, an unresolved controversy, a weak evidence area, or a concrete research opportunity.
The user must always be able to tell paper content apart from your own analysis. In any subsection where opinions are allowed, prefix every personal judgment with one of: "In my opinion,", "My analysis is that,", "My read is," or an equivalent first-person marker. Never blur the line. In the subsections where opinions are not allowed (questions 1 and 3), do not use these markers at all.
Every external citation in your response must come from a web_search or web_fetch you actually ran in this turn. No citations from memory. There is exactly one carve-out: if the paper itself cites a prior work and you are exactly repeating what the paper says about that cited work, you may mention it without a web search. The moment you add anything beyond what the paper literally says, search and cite the search result.
When you do search, cite the source inline so the user can follow up.
Keep the response tight. The user has explicitly asked for fast, non-redundant output. Do not repeat the same point under multiple questions. Aim for the shortest response that fully answers all seven questions; if a question genuinely has little to say for a particular paper, keep its subsection to two or three sentences.
Return everything as a single inline markdown response. Use one top-level header naming the paper, then a ## header per question. Math compliant with: \left / \right for display brackets, inline math on one line, every symbol defined, standard LaTeX. Do not use em-dashes or en-dashes anywhere; use commas, semicolons, parentheses, or new sentences instead.
This skill has three independent modes:
Mode 1 now starts by classifying the paper type. Original research papers use the modified Heilmeier framework. Review literature uses the Review Literature Extraction Mode, which is designed for review papers, surveys, systematic reviews, scoping reviews, and meta-analyses.
Mode 2 now supports --paper-type auto|research|review. For original research, it extracts research problem, methodology, key results, innovation, application, and limitations. For review literature, it extracts review scope, taxonomy, literature selection method, major themes, consensus findings, controversies, evidence quality, research gaps, future directions, and key tables/figures.
Mode 1 — Heilmeier Analysis (AI-driven, no script needed) Simply share a paper and ask for analysis. The AI follows the 7-question framework above directly. No local script is required.
Mode 2 — Core Insights Extractor (Python CLI)
A standalone script that extracts 6 structured fields (research problem, methodology, key results, innovation, application, limitations) with confidence scores. Run from the skills/sci-extract/ directory:
# Single PDF — outputs JSON by default
python extract_core_insights.py paper.pdf
# Choose output format
python extract_core_insights.py paper.pdf --format markdown
python extract_core_insights.py paper.pdf --format csv
# Force or auto-detect paper type
python extract_core_insights.py review.pdf --paper-type review
python extract_core_insights.py paper.pdf --paper-type auto
# Batch process a folder (4 parallel workers)
python extract_core_insights.py papers/ --batch
# Save to a specific file
python extract_core_insights.py paper.pdf --output results.jsonMode 3 — Paper Harvester (Python CLI + your own analysis)
Fetches a paper from the academic databases and lays it out on disk as markdown, so you read text and images rather than a PDF. Run from the skills/sci-extract/ directory:
# By identifier: DOI, PMID, PMCID, or arXiv ID
python harvest_paper.py 10.1038/s41586-020-2649-2
python harvest_paper.py PMC7759461
python harvest_paper.py 2006.10256
# By title or search query: lists candidates and asks before fetching
python harvest_paper.py "array programming with numpy"
python harvest_paper.py "array programming with numpy" --pick 1 # non-interactive
# Where to write, and what to skip
python harvest_paper.py 10.1038/s41586-020-2649-2 --output-dir ./papers
python harvest_paper.py 10.1038/s41586-020-2649-2 --skip-pdf --skip-figures
# Several papers, one identifier per line
python harvest_paper.py --batch dois.txt
# Which databases are configured
python harvest_paper.py --sourcesEach paper lands in its own directory under ./sci_extract_out/ (override with --output-dir):
2020_harris_array-programming-numpy/
├── raw.md # complete capture in English, figures embedded inline
├── analysis.md # YOU write this; see below
├── metadata.json # merged record from every database, plus an audit trail
├── fulltext.xml # publisher XML exactly as received
├── figures/ # publication-resolution images
├── paper.pdf # kept for reference
└── _analysis_prompt.md # instructions for writing analysis.mdAfter the harvester finishes, you write analysis.md. The script deliberately does not call any language model: you are the model, so read _analysis_prompt.md, read raw.md in full, apply the Mode 1 template that matches the paper type, and write analysis.md into the same directory in Chinese. No LLM API key is involved, and none should be requested from the user.
Two markdown files per paper is the point of this mode: raw.md is the faithful source you read, analysis.md is your judgment of it, and keeping them separate means the analysis never quietly rewrites the evidence.
Coverage is honest about its limits. Full text and figures require the paper to be open access; for paywalled papers the harvester still writes complete metadata, the abstract, and the PDF when one is reachable, and raw.md states plainly which parts were unavailable. Check the Provenance section at the bottom of raw.md before treating a gap as an absence in the paper itself.
The three modes are independent: Mode 1 produces a narrative analysis from a paper you already have; Mode 2 produces structured data fields from a local PDF; Mode 3 fetches the paper and prepares it for Mode 1. A common chain is Mode 3 then Mode 1.
Mode 2 requires PyMuPDF, pdfplumber, and numpy.
Mode 3 requires requests, and uses PyMuPDF when it needs to crop figures out of a PDF. Database credentials are read from the suite-wide .env at ~/.aut_sci_write/.env; run python harvest_paper.py --sources to see what is configured. Crossref, OpenAlex, Semantic Scholar, PubMed, Europe PMC, and arXiv need no key, so the harvester is useful with an empty .env. Adding keys for Web of Science, Scopus, Springer, Elsevier, or IEEE widens metadata coverage and, for entitled Elsevier and Springer keys, full text.
| Author | Contribution | Copyright |
|---|---|---|
| Shuo Zhao | Core extraction engine (features, figure detection, metadata parsing) | © 2026 Shuo Zhao |
| Zhiyao Zhang | Heilmeier Analysis module (7-question catechism framework) | © 2026 Zhiyao Zhang |
MIT License — see LICENSE file in the project root.
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
Copyright (c) 2026 Shuo Zhao. All rights reserved.
Copyright (c) 2026 Zhiyao Zhang. All rights reserved.
This work includes contributions from both authors under MIT license.
- Core extraction module: Copyright (c) 2026 Shuo Zhao
- Heilmeier analysis module: Copyright (c) 2026 Zhiyao ZhangThis is an original collaborative work created by the authors. No reproduction, redistribution, or commercial use without explicit permission from both authors.
Permission is hereby granted, free of charge, to any person obtaining a copy of this software... (See the LICENSE file in the root directory for the full MIT terms.)
© ShZhao27208, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 14 other files in skills/sci-extract of ShZhao27208/Aut_Sci_Write.
Open the folder on GitHubat commit 357766f
Sci Extract next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Sci Extract this skillShZhao27208/Aut_Sci_Write | 209 | — | ~5.8k | Automated safety check: Notes | MIT | |
| Literature Reviewneflibata-feng/MyArxiv-Agent | 126 | 20 repos | ~5.9k | Automated safety check: Notes | MIT | |
| Systematic Literature Review Builderbytedance/deer-flow | 84k | 2 repos | ~4.3k | Automated safety check: Pass | MIT | |
| Paper Research on arXivXiaomiMiMo/MiMo-Code | 14k | — | ~1.5k | Automated safety check: Pass | MIT | |
| Literature Review AgentAr9av/PaperOrchestra | 679 | 1 repos | ~5.2k | Automated safety check: Pass | Custom licence | |
| Arxiv MCP Serverblazickjp/arxiv-mcp-server | 3.2k | — | ~353 | Automated safety check: Pass | Apache-2.0 |
neflibata-feng/MyArxiv-Agent
Conduct comprehensive, systematic literature reviews using multiple academic databases (PubMed, arXiv, bioRxiv, Semantic Scholar, etc.).
bytedance/deer-flow
Searches arXiv across many papers on one topic, extracts each paper's methodology and findings in parallel, and synthesizes a cited literature review.
XiaomiMiMo/MiMo-Code
Searches arXiv, fetches metadata, generates BibTeX, downloads PDFs and finds citations and related papers using a bundled Python script.
Ar9av/PaperOrchestra
Step 3 of the PaperOrchestra pipeline (arXiv:2604.05018). An agent skill from Ar9av/PaperOrchestra.
blazickjp/arxiv-mcp-server
A skill your agent uses when finding, comparing, reading, or monitoring arXiv papers, including requests for abstracts, citation graphs, original LaTeX, section-level technical details, or…
appautomaton/latex-arxiv-SKILL
Write LaTeX ML/AI review articles for arXiv using the IEEEtran template and verified BibTeX citations.
ShZhao27208/Aut_Sci_Write
Two-stage academic paper polishing skill. An agent skill from ShZhao27208/Aut_Sci_Write.
ShZhao27208/Aut_Sci_Write
Specialized workflows for drafting, refining, and responding to academic literature reviews and peer review feedback.
ShZhao27208/Aut_Sci_Write
Extracts figures and sub-figures from academic PDF papers. An agent skill from ShZhao27208/Aut_Sci_Write.
ShZhao27208/Aut_Sci_Write
Generate academic presentation-style HTML slide decks and browser reports from PDFs, structured text, Markdown, paper summaries, outlines, or research notes.
ShZhao27208/Aut_Sci_Write
Generate professional academic PowerPoint (PPTX) presentations from paper PDFs, structured outlines, or plain text.
ShZhao27208/Aut_Sci_Write
Academic paper search and metrics analysis. An agent skill from ShZhao27208/Aut_Sci_Write.
Works with
Categories
Read an academic paper end to end and extract professional research insights, figures, metadata, and critique. Sci Extract is an agent skill from ShZhao27208/Aut_Sci_Write. Read an academic paper end to end and extract professional research insights, figures, metadata, and critique.
Sci Extract fits situations like: the user shares a scientific paper; systematic review; pasted paper text and asks to read; non-academic articles.
Run `npx skills add ShZhao27208/Aut_Sci_Write --skill sci-extract -a claude-code`. Or copy the skill folder (skills/sci-extract in ShZhao27208/Aut_Sci_Write) into .claude/skills/sci-extract in your project. Claude Code loads it when a task matches its description.
Run `npx skills add ShZhao27208/Aut_Sci_Write --skill sci-extract -a codex`. Or copy the skill folder (skills/sci-extract in ShZhao27208/Aut_Sci_Write) into .agents/skills/sci-extract in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add ShZhao27208/Aut_Sci_Write --skill sci-extract -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/sci-extract, .gemini/skills/sci-extract, .github/skills/sci-extract and .opencode/skills/sci-extract in your project.
Going by SKILL.md and its folder, Sci Extract needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found notes only (mentions a .env file), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.
Sci Extract is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
About 5.8k tokens (SKILL.md is roughly 23k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Sci Extract: Literature Review (neflibata-feng/MyArxiv-Agent, 126 stars), Systematic Literature Review Builder (bytedance/deer-flow, 84k stars), Paper Research on arXiv (XiaomiMiMo/MiMo-Code, 14k stars) and Literature Review Agent (Ar9av/PaperOrchestra, 679 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
ShZhao27208 (a GitHub user) maintains it in ShZhao27208/Aut_Sci_Write, which has 209 GitHub stars. The repository holds 9 skills in this directory. The repository was last updated on August 13, 2026.
Source: ShZhao27208/Aut_Sci_Write on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.