Daily Papers
Xiangyue-Zhang/auto-deep-researcher-24x7
Daily arXiv paper recommendations with automatic deduplication
“用于学术调研、论文精读、相关工作梳理、百科知识查询和引用链追溯。”
Install Sn Search Academic
$ npx skills add OpenSenseNova/SenseNova-Skills --skill sn-search-academic -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install OpenSenseNova/SenseNova-Skills sn-search-academic --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/OpenSenseNova/SenseNova-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/sn-search-academic .claude/skills/sn-search-academic && 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 "sn-search-academic" agent skill from https://github.com/OpenSenseNova/SenseNova-Skills/tree/main/skills/sn-search-academic into .claude/skills/sn-search-academic/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sn-search-academic", 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/OpenSenseNova/SenseNova-Skills/tree/main/skills/sn-search-academicType 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 OpenSenseNova/SenseNova-Skills --skill sn-search-academic -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install OpenSenseNova/SenseNova-Skills sn-search-academic --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/OpenSenseNova/SenseNova-Skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/sn-search-academic .agents/skills/sn-search-academic && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "sn-search-academic" agent skill from https://github.com/OpenSenseNova/SenseNova-Skills/tree/main/skills/sn-search-academic into .agents/skills/sn-search-academic/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sn-search-academic", 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 OpenSenseNova/SenseNova-Skills --skill sn-search-academic -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install OpenSenseNova/SenseNova-Skills sn-search-academic --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/OpenSenseNova/SenseNova-Skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/sn-search-academic .cursor/skills/sn-search-academic && 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 "sn-search-academic" agent skill from https://github.com/OpenSenseNova/SenseNova-Skills/tree/main/skills/sn-search-academic into .cursor/skills/sn-search-academic/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sn-search-academic", 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/OpenSenseNova/SenseNova-Skills.git --path skills/sn-search-academic--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 OpenSenseNova/SenseNova-Skills --skill sn-search-academic -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install OpenSenseNova/SenseNova-Skills sn-search-academic --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/OpenSenseNova/SenseNova-Skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/sn-search-academic .gemini/skills/sn-search-academic && 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 "sn-search-academic" agent skill from https://github.com/OpenSenseNova/SenseNova-Skills/tree/main/skills/sn-search-academic into .gemini/skills/sn-search-academic/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sn-search-academic", 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 OpenSenseNova/SenseNova-Skills sn-search-academicInstalls 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 OpenSenseNova/SenseNova-Skills --skill sn-search-academic -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/OpenSenseNova/SenseNova-Skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/sn-search-academic .github/skills/sn-search-academic && 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 "sn-search-academic" agent skill from https://github.com/OpenSenseNova/SenseNova-Skills/tree/main/skills/sn-search-academic into .github/skills/sn-search-academic/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sn-search-academic", 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 OpenSenseNova/SenseNova-Skills --skill sn-search-academic -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install OpenSenseNova/SenseNova-Skills sn-search-academic --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/OpenSenseNova/SenseNova-Skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/sn-search-academic .opencode/skills/sn-search-academic && 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 "sn-search-academic" agent skill from https://github.com/OpenSenseNova/SenseNova-Skills/tree/main/skills/sn-search-academic into .opencode/skills/sn-search-academic/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sn-search-academic", 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.
Facts
- Skill name
sn-search-academic- GitHub stars
- 5.7k
- Token cost
- ~2.6k tokens
- SKILL.md length
- 559 words
- Files
- 28 (incl. scripts, references)
- Skills in repo
- 36
- Repo updated
- First seen
- Licence
- MIT
At a glance
- Works in 5 steps: 先用 search.py 搜索，优先从… → 如果条目有 arxiv_id，先用 python3… → 如果条目有 pmc_id，先用 python3 scripts/paper.py… → …
- SKILL.md covers 凭证配置, 可用脚本, 执行约定 and 依赖, plus 7 more sections
- Runs Python scripts from its folder; calls python3; reaches arxiv.org
About this skill
Sn Search Academic is a skill in OpenSenseNova/SenseNova-Skills (5.7k stars). Its SKILL.md is about 2.6k tokens, with 27 other files in the folder (scripts, references). Licence: MIT.
Workflow steps
5 steps, taken from the first numbered list in SKILL.md.
- 先用 search.py 搜索，优先从 source_results[*].items 里记录 title、arxiv_id、pmc_id、paper_id、doi、citation_count。
- 如果条目有 arxiv_id，先用 python3 scripts/paper.py --source arxiv --list_section 查看章节；再用 --section 精读。
- 如果条目有 pmc_id，先用 python3 scripts/paper.py --source pmc --list_section 查看章节；再按需读 --section 。
- 如果需要整体理解，再不带 --section / --list_section 读取全文。
- 全文很长时使用 --output results/paper.json，再读取 content、sections、char_count 等字段。
What it can do on your machine
Read from SKILL.md and the folder at commit 7838651. It shows what the files ask for, not the result of running them.
Tool permissions
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.
Runs code
Ships 14 files in scripts/ (Python, from the files we listed), which the agent can run.
Shell commands in SKILL.md call:
python3
From the folder's file list and the shell code blocks in SKILL.md.
Network
Hosts in commands or code, which the agent is likely to contact:
arxiv.org
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Sn Search Academic loads about 2.6k tokens when it runs, and up to ~7.8k if it reads all its reference files. Until then it costs about 13 tokens; SKILL.md has 559 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.
Safety
Auto-check: notes
The automated check noted patterns worth knowing about, such as sudo or a known installer.
- NoteMentions a .env fileSKILL.md:10
API key、token 与 cookie 统一建议写在仓库根目录 `.env`（参考 `.env.example`），并由 runtime 或用户在执行前加载为同名环境变量。脚本仍只从环境变量或显式 CLI 参数读取凭证；不要把真实密钥
Automated 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); the scripts in this folder are not scanned.
SKILL.md
The full file from OpenSenseNova/SenseNova-Skills at commit 7838651, republished under its MIT licence (© OpenSenseNova). 559 words, ~2,626 tokens.
Download SKILL.mdSave it as
.claude/skills/sn-search-academic/SKILL.md (or your agent's skills folder). This skill also uses 27 other files; get the full folder from GitHub.- name
- sn-search-academic
- description
- 用于学术调研、论文精读、相关工作梳理、百科知识查询和引用链追溯。
sn-search-academic - 学术搜索
凭证配置
API key、token 与 cookie 统一建议写在仓库根目录 .env（参考 .env.example），并由 runtime 或用户在执行前加载为同名环境变量。脚本仍只从环境变量或显式 CLI 参数读取凭证；不要把真实密钥写入 skill payload、报告、日志或提交。
使用三个统一入口完成学术调研：
search.py：搜索论文和百科条目paper.py：列出论文章节，读取论文全文或指定章节refTree.py：查询论文的 references 和 citations
不要直接调用历史 provider 脚本；它们只是统一入口的内部实现细节。
需要 provider 回退链、参数分发或完整输出字段时，按需读取 references/search.md、references/paper.md、references/refTree.md。
可用脚本
| 脚本 | 用途 | 主要输入 | 主要输出 |
|---|---|---|---|
scripts/search.py | 搜索论文/百科 | query，可选 --source、--limit、--category、--lang | 按 source 分组的论文/百科条目，位于 source_results[*].items |
scripts/paper.py | 列出章节，读取论文全文或章节 | 论文 ID，可选 --source、--list_section、--section | 章节列表位于 sections；全文或章节正文位于 content |
scripts/refTree.py | 查询引用树 | --paper_id、--title，可选 --direction | 参考文献与被引论文，位于 source_results[*].references / source_results[*].citations |
执行约定
本技能的 scripts/...、requirements.txt、references/... 路径均相对本 skill 目录；若当前工作目录不同，先解析为绝对路径，不要依赖 ${SKILL_DIR} 运行时变量。
调用约定：
- 不要并行启动多个本技能脚本；
search.py和refTree.py内部已经处理并发、超时和 provider 回退链。 - 长结果优先加
--output <path>写入文件，再读取必要字段，避免终端输出过长。 --provider-timeout表示单个 provider 超时；默认使用脚本内置超时。
依赖
首次运行或脚本提示缺库时，使用本技能的依赖清单安装到当前 Python 环境：
python3 -m pip install -r requirements.txt不要在脚本内部自动安装依赖。若安装失败、网络不可用或包不可用，停止使用对应脚本并改用 WebSearch/browser-use，说明缺少依赖。
Crawler 回退还需要额外运行时环境：
python3 -m playwright install firefoxarxiv_crawler_search.py 和 semantic_scholar_crawler_refTree.py 还需要 Node.js，以及某个当前目录或祖先目录中已安装 camoufox-js 的 node_modules。缺少这些环境时，不要尝试绕过；改用非 crawler provider 或网页搜索。
参数说明
search.py
统一搜索入口。默认搜索所有支持的 source，并按 source 分组返回结果。
python3 scripts/search.py <query> [选项]| 参数 | 说明 | 默认值 |
|---|---|---|
query | 搜索关键词，必填位置参数 | - |
--source, --sources, -s | 搜索源；支持重复传参或逗号分隔 | all |
--limit, -n | 每个 source 返回数量 | 10 |
--category, -c | ArXiv 分类过滤，只传给支持分类的 source | - |
--lang, -l | 语言提示，只传给支持语言参数的 source | - |
--output, -o | 将最终 JSON 写入文件 | - |
--provider-timeout | 每个 provider 的超时时间，单位秒；0 表示不限制 | 60 |
支持的 --source：
allarxivsemanticgoogle_scholarpubmedssrnwikipedia
示例：
python3 scripts/search.py "retrieval augmented generation" --limit 5
python3 scripts/search.py "diffusion model" --source arxiv,semantic --category cs.CV --limit 5
python3 scripts/search.py "阿尔茨海默病 多模态诊断" --source pubmed,wikipedia --lang zh --limit 5
python3 scripts/search.py "open source governance" --source ssrn --limit 10
python3 scripts/search.py "agentic memory" --source all --limit 8 --output results/search.jsonpaper.py
统一论文阅读入口。默认按 arXiv 论文读取；读取 PMC 论文时显式传 --source pmc。不确定章节名时先用 --list_section 列出可用章节，再用 --section 精读。
python3 scripts/paper.py <id> [选项]| 参数 | 说明 | 默认值 |
|---|---|---|
id | 论文 ID。arXiv 支持原始 ID、arXiv: 前缀、abs/pdf URL；PMC 支持 PMC11119143、11119143、PMC URL | - |
--source | 论文来源：arxiv 或 pmc | arxiv |
--section, -s | 读取指定章节；不填则读取全文 | - |
--list_section, --list-section | 列出论文可用章节，不返回正文；不能和 --section 同时使用 | false |
--output, -o | 将最终 JSON 写入文件 | - |
示例：
python3 scripts/paper.py 2603.00729
python3 scripts/paper.py 2603.00729 --list_section
python3 scripts/paper.py arXiv:2603.00729 --section introduction
python3 scripts/paper.py 2603.00729 --section method --output results/paper-method.json
python3 scripts/paper.py PMC11119143 --source pmc
python3 scripts/paper.py PMC11119143 --source pmc --list-section
python3 scripts/paper.py PMC11119143 --source pmc --section resultsrefTree.py
统一引用树入口。--paper_id 和 --title 都必填；标题用于回退时精确匹配。
python3 scripts/refTree.py --paper_id <paper_id> --title <title> [选项]| 参数 | 说明 | 默认值 |
|---|---|---|
--paper_id | 论文 ID：Semantic Scholar ID、DOI、ArXiv ID、PMID 等 | - |
--title | 论文标题，必填 | - |
--direction | 查询方向：references 或 citations；不填则两者都查 | - |
--source, --sources, -s | 引用树 source；当前支持 all、semantic | all |
--limit, -n | 每个 source、每个 direction 返回数量 | 10 |
--api-key | Semantic Scholar API 密钥，可选 | - |
--provider-timeout | 每个 provider 的超时时间，单位秒；0 表示不限制 | 60 |
--output, -o | 将最终 JSON 写入文件 | - |
注意：参数名是 --paper_id，不是 --paper-id；paper_id 不支持位置参数。
示例：
python3 scripts/refTree.py --paper_id "2309.16609" --title "Qwen Technical Report"
python3 scripts/refTree.py --paper_id "2309.16609" --title "Qwen Technical Report" --direction references --limit 20
python3 scripts/refTree.py --paper_id "10.1038/s41586-024-07487-w" --title "AlphaFold 3" --direction citations
python3 scripts/refTree.py --paper_id "2309.16609" --title "Qwen Technical Report" --output results/refTree.json输出格式
所有脚本都输出 JSON。先看顶层 success；失败时读取 error、errors 和 attempts 判断是无结果、超时还是 provider 失败。
search.py 输出
CLI 输出的顶层不包含 items，论文条目在 source_results[*].items 中：
{
"success": true,
"query": "retrieval augmented generation",
"provider": "search.py",
"sources": ["arxiv", "semantic"],
"source_results": [
{
"source": "arxiv",
"success": true,
"provider": "arxiv_official",
"items": [
{
"source": "arxiv",
"provider": "arxiv_official",
"title": "Example title",
"abstract": "Example abstract",
"citation_count": null,
"arxiv_id": "2301.00001",
"url": "https://arxiv.org/abs/2301.00001"
}
],
"attempts": [],
"error": null
}
],
"errors": [],
"error": null
}常用 item 字段：
- 通用：
title、abstract、snippet、url、citation_count、doi - arXiv：
arxiv_id、pdf_url、categories - Semantic Scholar：
paper_id、venue、year - SSRN：
doi、year、publication_date、publisher、container（container为 SSRN working paper 系列名称，如 "SSRN Electronic Journal"） - PubMed：
pmid、pmc_id、journal、pub_date - Wikipedia：
page_id、word_count、section_title
paper.py 输出
默认读取全文；指定 --section 时读取章节。正文在顶层 content：
{
"success": true,
"source": "arxiv",
"provider": "arxiv_html",
"arxiv_id": "2603.00729",
"section": "introduction",
"content": "<全文或章节正文>",
"char_count": 12345,
"attempts": [],
"error": null
}指定 --list_section 时只返回章节结构，不返回 content：
{
"success": true,
"source": "arxiv",
"provider": "arxiv_html",
"arxiv_id": "2603.00729",
"section_count": 2,
"sections": [
{"name": "Abstract", "level": 0},
{"name": "1 Introduction", "level": 1}
],
"attempts": [],
"error": null
}常用字段：
- arXiv：
arxiv_id、title、abs_url、html_url、pdf_url、section_count、sections - PMC：
pmc_id、pmid、title、pmc_url、section_count、sections - 指定
--list_section时返回sections和section_count，不包含content - 指定
--section时会包含section；不指定--list_section/--section时读取全文
refTree.py 输出
引用树结果在 source_results[*].references 和 source_results[*].citations：
{
"success": true,
"id": "2309.16609",
"title": "Qwen Technical Report",
"provider": "refTree.py",
"direction": "all",
"source_results": [
{
"source": "semantic",
"success": true,
"provider": "semantic_official",
"references": [
{
"title": "Example reference",
"abstract": "Example abstract",
"citation_count": 128,
"paper_id": "example-reference-id",
"arxiv_id": "2301.00001"
}
],
"citations": [
{
"title": "Example citing paper",
"abstract": "Example abstract",
"citation_count": 42,
"paper_id": "example-citing-id",
"doi": "10.1234/example"
}
],
"attempts": [],
"error": null
}
],
"errors": [],
"error": null
}如果使用 --output，三个脚本都会在 JSON 中额外加入 output_path。
并发与限流约定
这些脚本会访问外部学术服务，必须控制请求频率。 执行本技能脚本时：
- 不要并发运行多个搜索脚本。
- 不要使用并行工具同时调用多个
python3 scripts/...命令。 - 一次只运行一个脚本命令，等待结果返回后再运行下一个。
- 批量查询时，优先使用脚本自带的
--limit、--id-list等参数，而不是启动多个进程。 - 如果需要连续调用，按顺序执行，并在必要时等待数秒。
Show full SKILL.md (220 more words)Show less
全文阅读工作流
搜索结果只有摘要时，用 paper.py 先列章节，再补充全文或关键章节。
- 先用
search.py搜索，优先从source_results[*].items里记录title、arxiv_id、pmc_id、paper_id、doi、citation_count。 - 如果条目有
arxiv_id，先用python3 scripts/paper.py <arxiv_id> --source arxiv --list_section查看章节；再用--section <section>精读。 - 如果条目有
pmc_id，先用python3 scripts/paper.py <pmc_id> --source pmc --list_section查看章节；再按需读--section <section>。 - 如果需要整体理解，再不带
--section/--list_section读取全文。 - 全文很长时使用
--output results/paper.json，再读取content、sections、char_count等字段。
引用追溯工作流
通过论文的引用关系发现关键词搜索覆盖不到的相关工作。
通过 references 找奠基工作，通过 citations 找后续进展。refTree.py 需要同时传论文 ID 和标题。
后向追溯（找奠基工作）：
- 关键词搜索找到高相关论文 → 取其
paper_id或arxiv_id和title refTree.py --paper_id "<id>" --title "<title>" --direction references --limit 20→ 找到高引参考文献- 筛选与研究问题相关的条目 → 用
paper.py深入阅读
前向追踪（找后续进展）：
- 找到领域奠基论文或关键论文 → 取其 ID
refTree.py --paper_id "<id>" --title "<title>" --direction citations --limit 20→ 找到近期高引跟进工作- 筛选与研究问题相关的条目 → 用
paper.py深入阅读
引用链：构建演化路径
- 从种子论文 A 出发 → backward 找到 A 的关键参考文献 B
- 从 B 出发 → forward 找到引用 B 的后续工作（可能发现 A 没引用的相关论文 C）
- 形成 B → A → ... 和 B → C → ... 的知识脉络
主工作流
严格遵循本工作流去执行学术搜索的全流程
- 在提供的学术平台选择所有可能的平台搜索学术文献
- 如果摘要不足或论文高度相关时，列出章节，尝试读取论文章节或全文，判断论文和搜索需求的相关性
- 选择相关性高的论文，搜索它的参考文献和被引（使用引用追溯工作流）。
- 选择引用树中 高引用的文献，执行步骤 2、步骤 3。
- 重复以上步骤，进行多轮搜索，尽可能多的进行搜索。
- 当文献数量、引用链和全文证据足够支撑回答时停止搜索，并在结论中说明主要依据。
ArXiv 分类速查
顶层领域可直接用（如 --category cs），子分类更精确（如 --category cs.AI）。
| 领域 | 分类代码 | 说明 |
|---|---|---|
| 计算机科学 | cs.AI | 人工智能 |
cs.LG | 机器学习 | |
cs.CL | 计算语言学 / NLP | |
cs.CV | 计算机视觉 | |
cs.IR | 信息检索 | |
cs.RO | 机器人 | |
cs.SE | 软件工程 | |
cs.DC | 分布式/并行计算 | |
cs.NI | 网络与互联网 | |
cs.CR | 密码学与安全 | |
cs.DB | 数据库 | |
cs.HC | 人机交互 | |
| 统计 | stat.ML | 统计机器学习 |
stat.AP | 应用统计 | |
stat.ME | 统计方法论 | |
| 数学 | math.OC | 优化与控制 |
math.ST | 统计理论 | |
math.CO | 组合数学 | |
| 物理 | physics | 物理（全类） |
cond-mat | 凝聚态物理 | |
quant-ph | 量子物理 | |
hep-th | 高能理论物理 | |
| 经济/金融 | econ.GN | 经济学综合 |
q-fin.CP | 计算金融 | |
q-fin.ST | 统计金融 | |
| 生物/医学 | q-bio.NC | 神经科学 |
q-bio.GN | 基因组学 | |
q-bio.QM | 定量方法 |
© OpenSenseNova, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 27 other files (scripts, references) in skills/sn-search-academic of OpenSenseNova/SenseNova-Skills.
- SKILL.md
- references/paper.md
- references/refTree.md
- references/search.md
- requirements.txt
- scripts/arxiv_crawler_search.py
- scripts/arxiv_mirror_search.py
- scripts/arxiv_paper.py
- scripts/arxiv_pdf_paper.py
- scripts/arxiv_search.py
- scripts/crossref_search.py
- scripts/deepxiv_paper.py
- scripts/deepxiv_search.py
- scripts/google_scholar_search.py
- scripts/openalex_search.py
- scripts/paper.py
- scripts/pmc_paper.py
- scripts/pubmed_search.py
- scripts/refTree.py
- … and 9 more
Open the folder on GitHubat commit 7838651
Compare with similar skills
Sn Search Academic 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 |
|---|---|---|---|---|---|---|
| Sn Search Academic this skillOpenSenseNova/SenseNova-Skills | 5.7k | — | ~2.6k | Automated safety check: Notes | MIT | |
| Daily PapersXiangyue-Zhang/auto-deep-researcher-24x7 | 1.3k | — | ~309 | Automated safety check: Pass | Apache-2.0 | |
| Read arXiv Paperkarpathy/nanochat | 59k | 1 repos | ~494 | Automated safety check: Pass | MIT | |
| Literature Reviewneflibata-feng/MyArxiv-Agent | 126 | 20 repos | ~5.9k | Automated safety check: Notes | MIT | |
| Openalex Databaseneflibata-feng/MyArxiv-Agent | 126 | 12 repos | ~3k | Automated safety check: Pass | Custom licence | |
| Citation ManagementK-Dense-AI/claude-scientific-writer | 2.4k | 2 repos | ~3.9k | Automated safety check: Notes | MIT |
Similar skills
Read arXiv Paper
karpathy/nanochat
Fetches the TeX source of an arXiv paper from its URL, reads it and writes a markdown summary tied to the nanochat project.
Literature Review
neflibata-feng/MyArxiv-Agent
Conduct comprehensive, systematic literature reviews using multiple academic databases (PubMed, arXiv, bioRxiv, Semantic Scholar, etc.).
Openalex Database
neflibata-feng/MyArxiv-Agent
Query and analyze scholarly literature using the OpenAlex database.
Citation Management
K-Dense-AI/claude-scientific-writer
Finds papers in OpenAlex, PubMed and Google Scholar, turns DOIs, PMIDs and arXiv IDs into clean BibTeX, and validates citations for a manuscript or thesis.
Citation Management
neflibata-feng/MyArxiv-Agent
Comprehensive citation management for academic research. An agent skill from neflibata-feng/MyArxiv-Agent.
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Works with
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Questions about Sn Search Academic
How do I install Sn Search Academic in Claude Code?
Run `npx skills add OpenSenseNova/SenseNova-Skills --skill sn-search-academic -a claude-code`. Or copy the skill folder (skills/sn-search-academic in OpenSenseNova/SenseNova-Skills) into .claude/skills/sn-search-academic in your project. Claude Code loads it when a task matches its description.
How do I install Sn Search Academic in Codex?
Run `npx skills add OpenSenseNova/SenseNova-Skills --skill sn-search-academic -a codex`. Or copy the skill folder (skills/sn-search-academic in OpenSenseNova/SenseNova-Skills) into .agents/skills/sn-search-academic in your project. Codex loads it when a task matches its description.
Can I use Sn Search Academic in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add OpenSenseNova/SenseNova-Skills --skill sn-search-academic -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/sn-search-academic, .gemini/skills/sn-search-academic, .github/skills/sn-search-academic and .opencode/skills/sn-search-academic in your project.
What does Sn Search Academic need to run?
Going by SKILL.md and its folder, Sn Search Academic needs Python for the scripts in its folder and the command-line tools its instructions call (python3).
Does Sn Search Academic access the network?
SKILL.md names 1 domain. In commands or code: arxiv.org; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.
Is Sn Search Academic safe to install?
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. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does Sn Search Academic use?
Sn Search Academic is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Sn Search Academic use?
About 2.6k tokens (SKILL.md is roughly 11k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 5.2k tokens, read only when the agent opens those files.
What are the alternatives to Sn Search Academic?
Skills that share tags, products or a category with Sn Search Academic: Daily Papers (Xiangyue-Zhang/auto-deep-researcher-24x7, 1.3k stars), Read arXiv Paper (karpathy/nanochat, 59k stars), Literature Review (neflibata-feng/MyArxiv-Agent, 126 stars) and Openalex Database (neflibata-feng/MyArxiv-Agent, 126 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Sn Search Academic?
OpenSenseNova (a GitHub organization) maintains it in OpenSenseNova/SenseNova-Skills, which has 5,749 GitHub stars. The repository holds 36 skills in this directory. The repository was last updated on October 9, 2026.
Source: OpenSenseNova/SenseNova-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.