SenseNova-Skills is a collection of modular skills that extend SenseNova models with office-assistant capabilities such as image generation, presentation creation, spreadsheet analysis, and research. The skills are designed for use in agent runtimes and can be combined into productivity workflows; the catalogue entries are individual skills and agents from this collection.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add OpenSenseNova/SenseNova-Skills --skill sn-search-academicgit clone --depth 1 https://github.com/OpenSenseNova/SenseNova-SkillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/opensensenova/sensenova-skills/sn-search-academic)<a href="https://agentmods.dev/skills/opensensenova/sensenova-skills/sn-search-academic"><img src="https://agentmods.dev/badge/skills/opensensenova/sensenova-skills/sn-search-academic/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/opensensenova/sensenova-skills/sn-search-academic"><img src="https://agentmods.dev/badge/skills/opensensenova/sensenova-skills/sn-search-academic.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00031 | $0.04322 |
| Opus 5 | $0.00015 | $0.02161 |
| Sonnet 5 | $0.00006 | $0.00864 |
| Haiku 4.5 | $0.00003 | $0.00432 |
Grade A, and why
sn-search-academic scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 379 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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 firefox
arxiv_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 |
What ships with it
27 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- references/paper.md 6.0 KB
- references/refTree.md 8.9 KB
- references/search.md 5.4 KB
- requirements.txt 108 B
- scripts/arxiv_crawler_search.py 14 KB runs code
- scripts/arxiv_mirror_search.py 6.8 KB runs code
- scripts/arxiv_paper.py 13 KB runs code
- scripts/arxiv_pdf_paper.py 5.1 KB runs code
- scripts/arxiv_search.py 9.4 KB runs code
- scripts/crossref_search.py 5.9 KB runs code
- scripts/deepxiv_paper.py 7.2 KB runs code
- scripts/deepxiv_search.py 9.3 KB runs code
- scripts/google_scholar_search.py 8.5 KB runs code
- scripts/openalex_search.py 5.4 KB runs code
- scripts/paper.py 15 KB runs code
- scripts/pmc_paper.py 16 KB runs code
- scripts/pubmed_search.py 5.7 KB runs code
- scripts/refTree.py 27 KB runs code
- scripts/search_utils.py 4.3 KB runs code
- scripts/search.py 17 KB runs code
- scripts/semantic_scholar_crawler_refTree.py 22 KB runs code
- scripts/semantic_scholar_crawler_search.py 6.5 KB runs code
- scripts/semantic_scholar_refs.py 8.0 KB runs code
- scripts/semantic_scholar_refTree.py 15 KB runs code
- scripts/semantic_scholar_search.py 6.8 KB runs code
- scripts/ssrn_search.py 4.0 KB runs code
- scripts/wikipedia_search.py 2.5 KB runs code
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 379 lines · 31 tokens per session scan A d6ab1b15e966
sn-search-academic is a skill published in the GitHub repository OpenSenseNova/SenseNova-Skills (5,570 stars, last pushed yesterday), licensed MIT. It adds 31 tokens to every session and 4,322 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
ddr-globem-analysis
Analyze a specific participant's longitudinal passive-sensing and psychological data in the GLOBEM digital depression research dataset. Use this skill whenever the task involves: analyzing a user's mental health or behavioral data from wearables/smartphones, generating QA pairs about behavioral/psychological changes…
mimic-iv-patient-analysis
Comprehensive strategy for analyzing individual patient records in MIMIC-IV EHR database and generating high-quality, diverse QA pairs. Use this skill whenever the task involves analyzing a specific patient's clinical data from MIMIC-IV (or similar EHR databases), querying across hospital and ICU tables, and…
globem-user-analysis
Comprehensive individual-user analysis on the GLOBEM dataset — a longitudinal passive-sensing + mental-health study of college students. Use this skill whenever a task involves analyzing a specific participant (e.g. "Analyze user INS-W002") from the GLOBEM dataset, exploring behavioral patterns from smartphone…
mimic-patient-analysis
Comprehensive patient analysis using the MIMIC-IV clinical database. Use this skill whenever asked to analyze, summarize, or investigate a patient's medical history, hospital admissions, diagnoses, medications, procedures, or clinical course from a MIMIC-IV SQLite database. Triggers on prompts like "Analyze patient…
rocket-chief-designer
Expert-level Rocket Chief Designer specializing in launch vehicle system architecture, multi-stage design and staging optimization, trajectory and performance analysis, aerodynamic load analysis, mass budget management, propulsion-to-vehicle integration. Use when: working with...
satellite-communication-engineer
Expert-level Satellite Communication Engineer specializing in link budget analysis (EIRP, G/T, Eb/N0), LEO/MEO/GEO constellation design, DVB-S2X/DVB-RCS2 waveform engineering, ground station design, RF interference analysis, ITU coordination, FCC/OFCOM. Use when: working with...