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Paper Reader Deep
作者
Jibeilindong
· GitHub ↗
· v0.1.1
· MIT-0
320
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1
当前安装
2
版本数
在 OpenClaw 中安装
/install paper-reader-deep
功能描述
深入理解PDF论文,提取关键信息,批判性分析并生成结构化深度阅读报告,关联用户研究方向。
安全使用建议
This skill appears coherent and not malicious, but review these points before installing: 1) It will read every *.pdf in the folder you point it at and create Markdown reports in that same folder — do not run it on directories containing sensitive/private PDFs you do not want processed or stored as plaintext. 2) The script makes an optional network call to CrossRef (api.crossref.org) to resolve DOIs; this is expected for metadata lookup. If you need fully offline operation, remove or disable the DOI query. 3) There are small inconsistencies: SKILL.md/README list PyYAML as a dependency though the script doesn't use it, and SKILL.md says it will save a record to MEMORY.md but the script does not implement that — if you rely on MEMORY.md logging, confirm/implement it. 4) The actual 'deep AI analysis' sections are left as placeholders in the generated reports and will be produced by the model when the agent is invoked — review how your agent will perform that step and where those outputs will be stored or transmitted. 5) If you have privacy concerns, inspect the script yourself (it's short and readable) or run it in a sandboxed environment. Overall: functionally consistent with its description, no unexpected credentials or hidden endpoints detected.
功能分析
Type: OpenClaw Skill
Name: paper-reader-deep
Version: 0.1.1
The 'paper-reader-deep' skill is a legitimate tool designed to automate the extraction of metadata and key data from PDF research papers. It uses the `pdfplumber` library for local text processing and makes a standard API call to `api.crossref.org` to retrieve publication details via DOI. The Python script (`scripts/deep_reader.py`) and the associated Markdown instructions are well-structured, transparent, and strictly follow the stated purpose of generating structured research reports without any evidence of malicious behavior, prompt injection attacks, or unauthorized data exfiltration.
能力评估
Purpose & Capability
Name/description match the implementation: the script extracts PDF text, parses metadata, extracts key numbers, and writes structured Markdown reports. Declared dependencies (pdfplumber) match usage. Minor mismatches: SKILL.md / README list PyYAML as a dependency but the provided script does not import or use PyYAML; SKILL.md claims a step '保存到MEMORY.md' (save to MEMORY.md) but the code does not implement writing to MEMORY.md. These are implementation/documentation inconsistencies but not evidence of malicious intent.
Instruction Scope
SKILL.md instructs the agent to perform deep understanding and to follow an analysis framework; the code provides extraction and templated report generation but leaves AI analysis sections as placeholders ("[AI分析中…]") for the agent/model to fill. This is consistent but means the substantive 'deep understanding' is performed by the model at runtime (not the script). The SKILL.md's stated step of recording to MEMORY.md is not implemented in code (inconsistency). The instructions otherwise only reference local PDF paths and expected outputs in the same directory.
Install Mechanism
No install spec is included (instruction-only plus a local script). That is low risk. The script depends on pdfplumber (documented). There are no downloads from external or untrusted URLs in the repo.
Credentials
The skill requests no environment variables or credentials. The only network use is an OPTIONAL CrossRef API query to resolve DOI titles (https://api.crossref.org), which is appropriate for a metadata lookup and requires no secret. No other services or secrets are requested.
Persistence & Privilege
always is false and the skill does not request persistent or elevated privileges. It writes generated reports into the same directory as the PDFs (normal behavior). It does not modify other skills or system-wide agent settings.
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 paper-reader-deep - 安装完成后,直接呼叫该 Skill 的名称或使用
/paper-reader-deep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
v0.1.1
- Removed four files, including core implementation (deep_reader.py) and section analysis templates.
- SKILL.md updated to reflect AI analysis instead of "人工" (manual) for报告核心理解 and批判性分析 sections.
- Step names and section headers now describe AI-based analysis, aligning documentation with code removal.
- No new features or functionality added; this version focuses on simplifying and clarifying the skill's structure and documentation.
v0.1.0
Paper Reader Deep Skill 0.1.0 – Initial Release
- Enables in-depth, structural analysis of research papers in PDF format.
- Automatically extracts key metadata and content from PDFs.
- Guides users through a structured critical analysis and reporting framework.
- Generates detailed reading reports and saves reading history.
- Designed to link analysis outputs to users’ own research interests.
元数据
常见问题
Paper Reader Deep 是什么?
深入理解PDF论文,提取关键信息,批判性分析并生成结构化深度阅读报告,关联用户研究方向。 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 320 次。
如何安装 Paper Reader Deep?
在 OpenClaw 或 Claude Code 对话框中运行命令「/install paper-reader-deep」即可一键安装,无需额外配置。
Paper Reader Deep 是免费的吗?
是的,Paper Reader Deep 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。
Paper Reader Deep 支持哪些平台?
Paper Reader Deep 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 Paper Reader Deep?
由 Jibeilindong(@jibeilindong)开发并维护,当前版本 v0.1.1。
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