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paper-lark-report
by
LeoGoat2004
· GitHub ↗
· v1.1.1
· MIT-0
136
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3
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Install in OpenClaw
/install paper-lark-report
Description
全自动科研论文日报/周报生成。通过 arXiv RSS 抓取最新论文,arXiv API 获取完整摘要,LLM 语义评分筛选,生成基于原文的学术报告,推送飞书 Wiki。
Usage Guidance
Before installing or running this skill: (1) Understand that it will read ~/.openclaw/openclaw.json to obtain Feishu appId/appSecret — inspect that file for other secrets and consider creating a dedicated Feishu app with minimal permissions. (2) The skill does not itself perform LLM scoring: it writes data/daily_papers.json and expects an LLM step to produce data/selected_papers.json (you or another skill must perform scoring). (3) Check config.yaml and set feishu_space_id, feishu_parent_node/feishu_root and research_direction appropriately. (4) Be aware the script prints a tenant token prefix to stdout (logged output) — avoid running where logs are publicly accessible. (5) If you prefer clearer boundaries, modify create_feishu_doc.load_token to read credentials from a dedicated skill config or environment variables and avoid loading the entire openclaw.json. If you want me to, I can suggest a small patch to require explicit env vars (FEISHU_APP_ID/FEISHU_APP_SECRET) and stop reading ~/.openclaw/openclaw.json.
Capability Analysis
Type: OpenClaw Skill
Name: paper-lark-report
Version: 1.1.1
The skill automates arXiv paper retrieval and generates Feishu (Lark) Wiki reports. It is classified as suspicious because 'scripts/create_feishu_doc.py' directly accesses the user's home directory to read sensitive credentials (appId and appSecret) from '~/.openclaw/openclaw.json'. While this capability is plausibly needed to authenticate with the Feishu API (open.feishu.cn) for the stated purpose, direct file-based credential harvesting is a high-risk behavior. The skill also instructs the AI agent via 'templates/daily_report.md' to execute shell commands, which is standard for OpenClaw but increases the attack surface. No evidence of intentional data exfiltration or malicious redirection was found.
Capability Assessment
Purpose & Capability
The code and SKILL.md match the stated purpose: querying arXiv, preparing JSON for an LLM step, and creating Feishu Wiki docs. However, the skill metadata claims no required credentials or env vars while the Feishu integration relies on appId/appSecret stored in ~/.openclaw/openclaw.json. That mismatch between declared requirements and actual credential access is noteworthy.
Instruction Scope
SKILL.md documents the overall flow and mentions using openclaw.json to get Feishu tokens. The runtime scripts only perform network calls to arXiv and Feishu and local file reads/writes in the skill directory, plus one read of the user's home openclaw.json. The code does not perform broad file system enumeration, does not exfiltrate data to unexpected endpoints, and does not call external installers. It does, however, print part of the tenant token to stdout (token prefix), which could leak sensitive info to logs.
Install Mechanism
No install spec / remote downloads are used (instruction-only plus included scripts). No archives or third-party package installs are pulled in by the skill itself, so there is no high-risk install URL or extraction step.
Credentials
The skill declares no required env vars or primary credential, yet create_feishu_doc.py reads ~/.openclaw/openclaw.json to fetch channels.feishu.appId and appSecret and exchanges them for a tenant_access_token. Accessing a home-config JSON with potentially multiple credentials is not declared and increases exposure. The Feishu credentials themselves are proportionate to the stated Feishu publishing capability, but the skill should have declared this requirement and instructed where/how users must provide credentials (or use env vars).
Persistence & Privilege
always:false and no attempt to modify other skills or global agent settings. The skill writes only to its SKILL_DIR/data and processed_log, and registers created docs in a local doc_registry.json. The only cross-directory read is the user's ~/.openclaw/openclaw.json to obtain Feishu credentials.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install paper-lark-report - After installation, invoke the skill by name or use
/paper-lark-report - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.1.1
- Added installation instructions for ClawHub and OpenClaw CLI to the documentation.
- Removed the introductory file paper_lark_report_intro.md.
- No changes to core workflow or functionality.
v1.1.0
Version 1.1.0
- Major refactor: transitioned from RSS-based paper retrieval to direct arXiv API query and filtering.
- New scripts added: `arxiv_search.py` for advanced paper search, and `create_feishu_doc.py` for direct Feishu Wiki integration.
- Removed prompt templates in favor of fully embedded workflow and templates.
- Introduced semantic arXiv query building and improved paper deduplication.
- Updated configuration fields and directory structure for clearer data and workflow separation.
- Enhanced documentation with detailed workflow, API usage, and config table.
v1.0.0
paper-lark-report v1.0.0
- 全自动生成科研论文日报和周报,支持指定研究方向
- 抓取 arXiv RSS、调用 arXiv API 获取摘要,利用大模型进行语义评分筛选
- 生成结构化学术报告并自动推送至飞书 Wiki
- 支持日报、周报自动归档和去重,含样例配置与定时任务说明
- 明确配置项和评分机制,最大化相关性与可追溯性
Metadata
Frequently Asked Questions
What is paper-lark-report?
全自动科研论文日报/周报生成。通过 arXiv RSS 抓取最新论文,arXiv API 获取完整摘要,LLM 语义评分筛选,生成基于原文的学术报告,推送飞书 Wiki。 It is an AI Agent Skill for Claude Code / OpenClaw, with 136 downloads so far.
How do I install paper-lark-report?
Run "/install paper-lark-report" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is paper-lark-report free?
Yes, paper-lark-report is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does paper-lark-report support?
paper-lark-report is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created paper-lark-report?
It is built and maintained by LeoGoat2004 (@leogoat2004); the current version is v1.1.1.
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