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在 OpenClaw 中安装
/install paper-research-agent
功能描述
Autonomous multi-agent paper research system. When user wants to research a topic, find related papers, or analyze academic literature, use this skill to orc...
安全使用建议
Before installing or running this skill:
- Inspect scripts/research_pipeline.py and references/analysis_standards.md yourself to verify behavior and confirm there are no hidden network endpoints or unexpected commands.
- Note it will pip-install packages at runtime (arxiv, requests, pdfplumber). If you need supply-chain assurance, pre-install vetted versions or run in an isolated environment.
- The skill expects a paper-reader tool at ~/.openclaw/skills/paper-reader/read_paper.py and references a launch_agents.py that is not present; ensure those dependencies exist and are trustworthy.
- Limit parallelism: do not launch 'as many agents as possible' on your machine—test with a small max_papers and controlled concurrency to avoid resource exhaustion or runaway agent spawning.
- Run first in a sandbox or restricted environment (network and process limits) and review generated _agent_tasks.json and task files before actually invoking sub-agents.
- If you lack the ability to audit the skill code, treat it as higher-risk and prefer manual execution of its components rather than fully autonomous runs.
功能分析
Type: OpenClaw Skill
Name: paper-research-agent
Version: 1.0.0
The paper-research-agent is a specialized tool for autonomous academic research, facilitating arXiv searches, PDF downloads, and multi-agent analysis. The core logic in scripts/research_pipeline.py uses standard libraries (arxiv, requests, pdfplumber) and implements responsible practices like rate-limiting for downloads. While the skill utilizes subprocess.run for dependency installation and orchestration, and sessions_spawn for parallel processing, these actions are strictly aligned with the stated purpose of generating comprehensive literature surveys as detailed in SKILL.md and references/analysis_standards.md.
能力评估
Purpose & Capability
The name/description align with the code and instructions: it searches arXiv, downloads PDFs, and coordinates per-paper analyses. However the skill assumes the presence of an external 'paper-reader' skill/tool at a hard-coded path (~/.openclaw/skills/paper-reader/read_paper.py) and suggests a launch_agents.py script that is not present in the bundle. Those undeclared dependencies are unexpected and weaken the declared self-contained purpose.
Instruction Scope
Runtime instructions perform network downloads and write PDFs and task files to the workspace (expected), but they also instruct the agent to 'spawn as many agents in parallel as possible' using sessions_spawn. That gives the skill broad discretion to create many sub-agents (resource exhaustion or wide action surface). The SKILL.md also runs subprocess commands and expects external tools; it references other skill paths and an absent launch script, giving ambiguous/incomplete guidance.
Install Mechanism
There is no formal install spec in the registry, but the bundled script auto-installs Python packages via pip at runtime (arxiv, requests, pdfplumber). Auto-pip-install is common but increases risk because it executes package installation from PyPI during execution rather than a reviewed install step. This is moderate risk (supply-chain / arbitrary code from PyPI) and should be considered when running in production.
Credentials
The skill requests no secrets or environment variables (good). It does, however, access and write files under the agent workspace and references other skills' paths (~/.openclaw/skills/paper-reader), which is not declared. That implies implicit reliance on other skill artifacts and file-system access that the description doesn't call out explicitly.
Persistence & Privilege
The skill is not forced-always and allows normal autonomous invocation. The main privilege concern is operational: instructions to spawn many parallel sub-agents can amplify the blast radius of any misbehaving sub-agent. There is no sign the skill modifies other skills' configs, but it does read/write workspace files and produce tasks for autonomous agents.
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-research-agent - 安装完成后，直接呼叫该 Skill 的名称或使用
/paper-research-agent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
Paper Research Agent v1.0.0 initial release:
- Introduces an autonomous multi-agent system for comprehensive academic paper research.
- Automates literature search, PDF download, parallel full-paper analysis, and integrated survey generation.
- Each analyzed paper produces a structured 6-section report covering background, problem, innovation, experiments, insights, and future work.
- Enforces strict quality standards: minimum 3000 words/report, real data tables, source citations, and no fabricated content.
- Supports both English and Chinese trigger phrases for research requests.
- Includes robust error handling for failed downloads and agent analyses.
元数据
常见问题
Paper Research Agent 是什么？
Autonomous multi-agent paper research system. When user wants to research a topic, find related papers, or analyze academic literature, use this skill to orc... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 225 次。
如何安装 Paper Research Agent？
在 OpenClaw 或 Claude Code 对话框中运行命令「/install paper-research-agent」即可一键安装，无需额外配置。
Paper Research Agent 是免费的吗？
是的，Paper Research Agent 完全免费，采用 MIT-0 许可证，可自由下载、安装和使用。
Paper Research Agent 支持哪些平台？
Paper Research Agent 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 Research Agent？
由 崔之行（@changer-changer）开发并维护，当前版本 v1.0.0。
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