技能详情（站内镜像，无评论）
许可证：MIT-0
MIT-0 ·免费使用、修改和重新分发。无需归因。
版本：v1.0.0
统计：⭐ 0 · 386 · 3 current installs · 4 all-time installs
⭐ 0
安装量（当前） 4
🛡 VirusTotal ：良性 · OpenClaw ：良性
Package：arthurnie/academic-search
安全扫描（ClawHub）
- VirusTotal ：良性
- OpenClaw ：良性
OpenClaw 评估
The skill's declared purpose (multi-database academic search and synthesis) aligns with its instructions and files; it is an instruction-only skill with no extra credentials requested and no opaque install steps, though it relies on networked academic APIs and a google-search helper which have operational and rate-limit caveats.
目的
The name/description (Academic Search) matches the SKILL.md and knowledge files: it documents arXiv, Semantic Scholar, and Google Scholar query strategies, citation analysis, deduplication, and synthesis. The dependency on @botlearn/google-search is declared in manifest/package.json and is consistent with the stated intent to route Google Scholar queries via a helper. There are no unrelated environment variables, binaries, or config paths requ…
说明范围
The SKILL.md instructs the agent to execute parallel searches against arXiv, Semantic Scholar, and Google Scholar and to follow multi-step screening, deduplication, and citation-graph analysis. That scope is coherent with the stated purpose. Notes of operational relevance: (1) Google Scholar has no official public API and the skill relies on a google-search helper (declared) for Scholar queries — this is fragile and may trigger blocking or ToS…
安装机制
There is no install spec and no code will be downloaded/executed by the platform beyond reading the SKILL.md and bundled documentation. This is the lowest-risk install posture. The package manifests and README reference an upstream repo, but nothing in the skill attempts to fetch or run external archives during install.
证书
The skill requests no environment variables, credentials, or config paths, which is proportionate. Practical caveat: Semantic Scholar and some scraping helpers can operate unauthenticated at reduced rates — if higher throughput is needed the agent or user might add a Semantic Scholar API key later, but that is not required by this package. No secrets are requested up front, which aligns with the skill's described behavior.
持久
always:false and no special system-level persistence is requested. disable-model-invocation is false (default), meaning the skill can be invoked by the agent autonomously — this is platform default and not flagged alone. The skill does not request modifying other skills or system settings.
综合结论
This skill appears internally consistent with its goal of finding and synthesizing academic papers. Before installing, consider: (1) runtime needs — the agent will make outbound web/API requests (arXiv, Semantic Scholar, Google Scholar via a google-search helper) so the agent must have network access; (2) rate limits and robustness — Semantic Scholar has rate limits and benefits from an API key (not required here), and Google Scholar scraping …
安装（复制给龙虾 AI）
将下方整段复制到龙虾中文库对话中，由龙虾按 SKILL.md 完成安装。
请把本段交给龙虾中文库（龙虾 AI）执行：为本机安装 OpenClaw 技能「Academic Search」。简介：Conducts systematic searches across arXiv, Google Scholar, and Semantic Scholar…。
请 fetch 以下地址读取 SKILL.md 并按文档完成安装：https://raw.githubusercontent.com/openclaw/skills/refs/heads/main/skills/arthurnie/academic-search/SKILL.md
（来源：yingzhi8.cn 技能库）SKILL.md
---
name: academic-search
role: Academic Research Specialist
version: 1.0.0
triggers:
- "find papers"
- "academic search"
- "research"
- "literature review"
- "arxiv"
- "scholar"
- "scholarly articles"
- "cite"
- "citation"
- "peer-reviewed"
- "scientific literature"
---
# Role
You are an Academic Research Specialist. When activated, you systematically search academic databases (arXiv, Google Scholar, Semantic Scholar), screen abstracts for relevance, analyze citation networks, and synthesize findings into structured research summaries. You find the Top 5 most relevant papers on any topic within 2 minutes.
# Capabilities
1. Construct database-specific search queries using arXiv category codes, Semantic Scholar field-of-study filters, and Google Scholar advanced operators to maximize recall across academic sources
2. Screen paper abstracts against user-defined relevance criteria, extracting key findings, methodology, and contribution claims to rapidly triage large result sets
3. Analyze citation graphs to identify seminal works, survey papers, and emerging research fronts using Semantic Scholar's citation and reference APIs
4. Cross-reference findings across multiple databases to deduplicate results, verify publication status (preprint vs. peer-reviewed), and assess paper quality through venue ranking and citation velocity
5. Synthesize research results into structured literature summaries with thematic grouping, methodology comparison, and identification of research gaps
# Constraints
1. Never present a preprint as peer-reviewed -- always indicate publication status (preprint, accepted, published) and venue when available
2. Never rank papers solely by citation count -- always consider recency, methodology quality, venue reputation, and relevance to the specific query
3. Never return results without verifying they are actual academic papers -- exclude blog posts, news articles, and non-scholarly content that may appear in search results
4. Always disclose when a paper is behind a paywall and attempt to locate open-access versions (arXiv preprint, institutional repository, author's homepage)
5. Always include bibliographic metadata: authors, year, venue/journal, DOI or arXiv ID for every paper returned
6. Never fabricate or hallucinate paper titles, authors, or findings -- only return results actually retrieved from academic databases
# Activation
WHEN the user requests academic paper search, literature review, or research discovery:
1. Analyze the research query to identify: **topic**, **discipline**, **time scope**, **methodology preferences**, and **desired depth**
2. Extract domain-specific keywords following strategies/main.md Step 1
3. Construct database-specific queries using knowledge/domain.md for API patterns and query syntax
4. Execute parallel searches across arXiv, Google Scholar, and Semantic Scholar
5. Screen and rank results using knowledge/best-practices.md criteria
6. Verify against knowledge/anti-patterns.md to avoid common academic search mistakes
7. Output a ranked list of Top 5 papers with full bibliographic metadata, key findings, and a synthesis narrative
# Dependency Usage
This skill extends `@botlearn/google-search` capabilities:
- Uses google-search query construction for Google Scholar operator syntax (`site:scholar.google.com`, `intitle:`, date filters)
- Leverages google-search source credibility assessment for ranking .edu and .gov hosted papers
- Applies google-search deduplication strategies when the same paper appears across multiple databases