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在 OpenClaw 中安装
/install papercash
功能描述
论文全流程辅助: 8源检索、综述生成、查重预检、降AI率、参考文献格式化、Word导出
安全使用建议
This skill appears to implement what it claims, but take these precautions before installing or giving it sensitive inputs:
- It executes the included Python CLI: inspect scripts/papercash.py and the features/ modules yourself if possible. The skill will read files you point it at and send queries (and parts of your text) to external academic services during search and plagiarism checks.
- Optional configuration asks for CNKI/WANFANG cookies and a Google Scholar proxy. Do not paste real account cookies or long-lived session tokens into config files unless you trust the author and understand the risk; prefer using only free public sources (Semantic Scholar, arXiv, CrossRef) if privacy matters.
- The 'humanize' (降AI率) capability helps evade AI-detection and could facilitate academic misconduct; use responsibly and follow your institution's policies.
- Review hooks/session_start.py (and any hook code) before installing — hooks can run at session start and increase the blast radius.
- If you decide to use it, run it in an isolated environment (container or VM), avoid supplying sensitive documents to optional sources that require cookies, and monitor outbound network requests (or restrict networking) until you are comfortable with its behavior.
功能分析
Type: OpenClaw Skill
Name: papercash
Version: 1.0.0
PaperCash is a comprehensive academic assistant tool designed for paper searching, literature review generation, and citation formatting. It integrates with eight academic data sources including Semantic Scholar, arXiv, and CrossRef. While the tool handles sensitive information such as session cookies for paywalled Chinese databases (CNKI and Wanfang) and performs network requests to various academic APIs, these behaviors are transparently documented and strictly necessary for its stated functionality. The code is well-structured, follows security best practices like input sanitization for filenames in `docx_export.py`, and lacks any evidence of data exfiltration, obfuscation, or malicious intent. The instructions in `SKILL.md` are appropriately aligned with the tool's purpose and do not contain prompt-injection attacks against the agent.
能力标签
能力评估
Purpose & Capability
Name/description (multi-source paper search, review generation, plagiarism pre-check, AI-rate reduction, citation formatting) align with the included code: multiple source modules, search/format/plagiarism/humanize features, and docx export are implemented. No unrelated cloud credentials or unusual binaries are requested.
Instruction Scope
SKILL.md instructs the agent to run the packaged CLI (python scripts/papercash.py ...) which will read files or pasted content and issue HTTP requests to third‑party academic endpoints (Semantic Scholar, arXiv, CrossRef, Baidu Scholar, Google Scholar via proxy, CNKI/Wanfang via cookies). That means user-provided paper text or sentences may be transmitted to external services; the documentation does not strongly call out potential data exposure beyond a short '查重声明'. The 'humanize' feature explicitly aims to reduce AI-detection rates, which is functionally coherent but ethically sensitive.
Install Mechanism
No install spec / remote downloads; the repo is instruction-plus-code. Dependencies are standard Python packages (jieba, requests, beautifulsoup4, python-docx) listed in requirements.txt. No suspicious download URLs or archive extraction steps were found.
Credentials
Registry metadata lists no required environment variables; the code supports optional configuration via ~/.config/papercash/.env or .papercash.env for SEMANTIC_SCHOLAR_API_KEY, GOOGLE_SCHOLAR_PROXY, CNKI_COOKIE, WANFANG_COOKIE. Asking for CNKI/Wanfang cookies (to enable those sources) is understandable for scraping, but copying browser cookies can expose account/session tokens — users should be cautious and understand what they paste into env files.
Persistence & Privilege
always:false and normal autonomous invocation are fine. However the package contains a hooks/session_start.py file (hooks are present in the manifest), which may be executed by platform integrations at agent session start. The SKILL.md doesn't document hook behavior; any code run at session start expands the skill's runtime surface beyond explicit commands and should be reviewed before install.
如何使用
- 确保已安装 OpenClaw(本地或 Docker 部署)
- 在对话框中输入安装命令:
/install papercash - 安装完成后,直接呼叫该 Skill 的名称或使用
/papercash触发 - 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.0.0
PaperCash 1.0.0 — 论文全流程辅助工具上线
- 支持8大数据源的论文检索与评分排序展示。
- 一键生成结构化综述,含引用与背景对比,支持Word导出。
- 提供大纲生成、学术扩写、润色等写作辅助功能。
- 集成查重预检和AI生成内容降重(降AI率)工具。
- 支持参考文献一键格式化(GB/T 7714、APA、BibTeX等)。
- 内置格式检查与多源可用性诊断工具。
元数据
常见问题
PaperCash — 论文全流程辅助 Skill 是什么?
论文全流程辅助: 8源检索、综述生成、查重预检、降AI率、参考文献格式化、Word导出. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 89 次。
如何安装 PaperCash — 论文全流程辅助 Skill?
在 OpenClaw 或 Claude Code 对话框中运行命令「/install papercash」即可一键安装,无需额外配置。
PaperCash — 论文全流程辅助 Skill 是免费的吗?
是的,PaperCash — 论文全流程辅助 Skill 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。
PaperCash — 论文全流程辅助 Skill 支持哪些平台?
PaperCash — 论文全流程辅助 Skill 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。
谁开发了 PaperCash — 论文全流程辅助 Skill?
由 Jesse(@jesseovo)开发并维护,当前版本 v1.0.0。
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