Semantic Paper Radar

Semantic literature discovery and synthesis across arXiv/OpenAlex/PubMed (and optional Google Scholar adapters). Use when users ask for domain must-read pape...

Security Scan

The skill's code and instructions are mostly coherent with a literature-discovery tool, but the provided script was truncated in the bundle preview and there are a few minor operational/privacy concerns (unencrypted arXiv fetch, external network calls, optional cross-skill invocation) that merit caution before installation.

Purpose & Capability

The SKILL.md describes semantic literature discovery across arXiv/OpenAlex/PubMed and the included script implements those exact network queries and ranking logic. Required resources (none) align with the stated purpose.

Instruction Scope

Runtime instructions tell the agent to run the included Python script and optionally call a Scholar-capable tool if present. The workflow intentionally performs network retrievals from public APIs and can export HTML locally. This is in-scope for a literature discovery skill, but it means user queries and terms will be sent to external services; SKILL.md also forces Chinese output by default—an operational/policy choice to be aware of.

Install Mechanism

No install spec is present (instruction-only with an included script). Nothing is downloaded or installed automatically by the skill manifest.

Credentials

The skill requests no environment variables or credentials. All external calls use public APIs (OpenAlex, PubMed, arXiv) and no secret material is required by the code shown.

Persistence & Privilege

always is false and model invocation is normal. The skill does not request persistent platform privileges in the manifest. It may write an exported HTML file if the user requests --export-html / --html-out, which is reasonable for this functionality.

What to consider before installing

This skill appears to do what it says — it queries OpenAlex, PubMed, and arXiv and ranks papers — but take these precautions before installing or enabling it: - Review the full, untruncated script on disk (the preview was truncated). The analysis above used the visible portion; any hidden/truncated code could change this assessment. - Be aware queries are sent to external public APIs (api.openalex.org, eutils.ncbi.nlm.nih.gov, export.arxiv.org). Do not send private or patient-identifiable data through this skill. - Note: the arXiv query uses HTTP (export.arxiv.org) in the visible code (unencrypted); if confidentiality is important, update to HTTPS or confirm arXiv's recommended endpoint. - The tool can export HTML to a path you control; confirm file paths before exporting to avoid accidental overwrites. - The SKILL.md suggests calling an existing Scholar-capable tool if present — ensure any cross-skill calls don't expose tokens/credentials belonging to other skills. - If you require higher assurance, run the script in a sandboxed environment or review the entire file contents locally to ensure there is no unexpected network endpoint or data-exfiltration behavior.

Build a domain reading list from natural-language intent, then output a concise research map.

Workflow

1. Clarify query intent in one line:

   • topic + scope + time window + priority (foundational / frontier / balanced).

2. Run aggregated retrieval:

   • General: python3 scripts/paper_radar.py search --query "<topic>" --max 40 --years 8
   • Biomedical force-on: python3 scripts/paper_radar.py search --query "<topic>" --max 40 --years 8 --biomed

3. Generate synthesis report:

   • python3 scripts/paper_radar.py report --query "<topic>" --max 40 --years 8 --top 12 --mode balanced
   • Biomedical force-on: add --biomed
   • Export clickable HTML: add --export-html (optional --html-out <path>)

4. Present results in Chinese unless user asked otherwise:

   • 必读文献（分层）
   • 学术脉络（时间线）
   • 阅读顺序（先读3篇）
   • 可选下一步（细分子方向）

Output Rules

• Prefer OpenAlex entries with DOI/citation metadata for "经典" judgement.
• Keep arXiv entries for "最新前沿" and unreviewed but high-momentum work.
• If the query is biomedical/clinical, explicitly include a caution that arXiv papers may be preprint.
• If retrieval is sparse, broaden query with synonyms and rerun once.

Recommended Prompt Pattern

Use this framing when user asks for recommendations:

• "按 经典奠基(3-5) + 方法跃迁(3-5) + 近两年新进展(3-5) 输出"
• "每篇给：一句贡献、为什么必读、适合第几步读"
• "最后给该领域 3 条学术脉络主线"

Optional Scholar Integration

If the environment already has a Scholar-capable tool/skill (e.g., serper-scholar), call it after report and use it only for:

• citation cross-check
• venue/author authority补充

Do not block core workflow if Scholar integration is unavailable.