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Beautiful Soup Akademischer Papier-Parser

Extrahiert strukturierte Zitationsdaten aus akademischen Repositorien mithilfe von BeautifulSoup4 mit lxml-Parser. Analysiert DOI-Metadaten, Autorenzugehörigkeiten und Referenzlisten aus PubMed-, arXiv- und Semantic-Scholar-HTML.

Was ist Beautiful Soup Akademischer Papier-Parser?

Der Beautiful Soup Academic Paper Parser ist ein KI-Agenten-Skill, der darauf ausgelegt ist, strukturierte Zitationsdaten aus akademischen HTML-Seiten wie PubMed, arXiv und Semantic Scholar zu extrahieren. Er nutzt die leistungsstarke BeautifulSoup4-Bibliothek mit dem lxml-Parser, um komplexe HTML-Strukturen zu durchlaufen und DOI-Metadaten, Autorenzugehörigkeiten sowie Referenzlisten zu identifizieren und zu parsen. Dieser Skill ist besonders nützlich für Forscher, Bibliothekare und Entwickler, die automatisierte Workflows zur Literaturverwaltung und Metadatenextraktion erstellen. Er funktioniert plattformunabhängig mit Python 3.7+ und kann in CI/CD-Pipelines, Jupyter-Notebooks oder als Teil größerer Data-Science-Projekte integriert werden. Die Dokumentation enthält Installationsanweisungen, eine einfache Nutzungssyntax und einen Verweis auf die offizielle Paketquelle auf PyPI.

Funktioniert mit~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/agentskillexchange/skills/tree/main/skills/beautifulsoup-academic-paper-parser
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Dokumentation

Was macht Beautiful Soup Akademischer Papier-Parser?

Extrahiert strukturierte Zitationsdaten aus akademischen Repositorien mithilfe von BeautifulSoup4 mit lxml-Parser. Analysiert DOI-Metadaten, Autorenzugehörigkeiten und Referenzlisten aus PubMed-, arXiv- und Semantic-Scholar-HTML.

Installation

Verwenden Sie den Upstream-Installations- oder Einrichtungspfad, der Ihrer Umgebung entspricht:

  • pip install beautifulsoup4
  • format. Führen Sie make html in diesem Verzeichnis aus, um HTML zu erstellen

Anforderungen und Hinweise aus dem Upstream:

  • Erfordert: Python >=3.7.0
  • Python
  • Python :: 3

Grundlegende Nutzung oder Einstiegsnotizen:

Quelle

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

Academic Paper Citation Network Mapper

Builds citation networks from Semantic Scholar API and CrossRef DOI metadata. Visualizes paper influence graphs using NetworkX, identifies seminal works, and tracks research lineage across fields.

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Use UX/UI Agent Skills when Claude should generate tokens, component specs, accessibility audits, and framework-specific UI code from a repeatable design workflow.

Add executable smoke tests for shell scripts and CLIs before ref

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Add graph-backed memory and context retrieval to agent workflows

Use Cognee to ingest project knowledge into graph and vector memory so agents can retrieve durable context across sessions and workflows.

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Add persistent project memory and hook-driven synthesis with Cla

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Add Postgres-native vector retrieval to agent and RAG workflows

Store embeddings beside application data in Postgres, create vector indexes, and query nearest neighbors for semantic search, RAG, recommendations, or agent memory retrieval.

Address GitHub PR review comments from the current branch with g

Find the open PR for the current branch, gather unresolved review comments, and drive a focused comment-resolution workflow with gh-authenticated context.

Add runtime guardrails to TypeScript agents with VoltAgent

Use VoltAgent to intercept, validate, and enforce input/output policies in TypeScript agent workflows.

Add temporal context memory to agent workflows with Zep

Use Zep as an external context layer for agents that need to store events, assemble temporal graph context, and retrieve relevant memory before model calls.

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