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在 OpenClaw 中安装
/install semantic-scholar
功能描述
Search, retrieve, and organize scholarly metadata with the Semantic Scholar APIs. Use when Codex needs to find papers or authors, build paper sets from compl...
安全使用建议
This skill appears to do what it says: query Semantic Scholar and save results. Before installing/running: (1) Review and install Python dependencies (requests; pandas only if you need CSV export). The skill has no automated install step. (2) If you want higher rate limits or repeated/bulk jobs, set SEMANTIC_SCHOLAR_API_KEY in your environment; the scripts look for x-api-key but will run without it (with lower limits). (3) The provided smoke-test expects an external 'uv' command (not documented in registry metadata); you don't need to run the smoke-test if 'uv' is unavailable. (4) The scripts perform network requests to api.semanticscholar.org and write JSONL/CSV files to disk—inspect outputs and running directory before sharing them. (5) If you plan to use the Datasets API or large bulk downloads, confirm storage/bandwidth expectations first. Overall: coherent and consistent with the stated purpose, but install/runtime dependencies should be manually verified before execution.
功能分析
Type: OpenClaw Skill
Name: semantic-scholar
Version: 1.0.0
The skill bundle provides a comprehensive and well-structured interface for the Semantic Scholar API, including scripts for bulk search, batch metadata retrieval, and paper recommendations. The Python scripts (e.g., semantic_scholar_bulk_search.py, paper_batch_fetch.py) implement robust error handling with exponential backoff for rate limits and use standard environment variables for API key management. No evidence of data exfiltration, malicious execution, or prompt injection was found; the instructions in SKILL.md and the references are strictly focused on guiding the agent through legitimate API workflows.
能力评估
Purpose & Capability
Name/description match the included scripts and references: Graph API, Recommendations API, Datasets API workflows are implemented or documented. All request URLs point to api.semanticscholar.org and the scripts implement search, batch fetch, recommendations, and dataset guidance—functions consistent with the description.
Instruction Scope
SKILL.md and the scripts limit operations to calling Semantic Scholar endpoints, writing JSONL/CSV outputs, and handling retries/pagination. The instructions do not ask the agent to read unrelated host files or exfiltrate secrets. The scripts preserve raw output before flattening as recommended.
Install Mechanism
There is no registered install spec (skill is effectively delivered as code). The Python scripts declare typical Python deps (requests, optional pandas) but the registry metadata doesn't declare dependency installation; the scripts themselves include comments like 'pip install requests pandas'. This is not malicious but means dependencies must be installed manually. The smoke-test script expects an unlisted 'uv' CLI tool to exist (see environment note).
Credentials
Scripts optionally read SEMANTIC_SCHOLAR_API_KEY via environment; that is expected and proportionate for an API client. Registry metadata lists no required env vars; this is acceptable because the API key is optional in code, but users should be aware the key increases rate limits. No unrelated credentials or secrets are requested.
Persistence & Privilege
Skill does not request always:true, does not modify other skills or global agent config, and only writes its own output files. Autonomous invocation is allowed by default (platform normal) but combined with the rest of the footprint does not introduce unusual privilege.
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 semantic-scholar - 安装完成后，直接呼叫该 Skill 的名称或使用
/semantic-scholar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
- Initial release of the Semantic Scholar skill.
- Enables searching, retrieving, and organizing scholarly metadata using Semantic Scholar APIs.
- Supports interactive search, batch fetching by IDs, recommendations from seed papers, and offline dataset workflows.
- Provides guidance on workflow selection: Graph API for lookups, Recommendations API for related work, Datasets API for offline needs.
- Includes best practices for efficient requests, pagination, authentication, and error handling.
- Offers example scripts for bulk paper harvesting, batch metadata fetch, recommendations, and smoke testing.
元数据
常见问题
semantic-scholar 是什么？
Search, retrieve, and organize scholarly metadata with the Semantic Scholar APIs. Use when Codex needs to find papers or authors, build paper sets from compl... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 347 次。
如何安装 semantic-scholar？
在 OpenClaw 或 Claude Code 对话框中运行命令「/install semantic-scholar」即可一键安装，无需额外配置。
semantic-scholar 是免费的吗？
是的，semantic-scholar 完全免费，采用 MIT-0 许可证，可自由下载、安装和使用。
semantic-scholar 支持哪些平台？
semantic-scholar 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 semantic-scholar？
由 Siyu Liu（@grenzlinie）开发并维护，当前版本 v1.0.0。
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