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Content Readability Optimizer

Analysiert und optimiert die Lesbarkeit von Inhalten mithilfe der textstat-Python-Bibliothek und Hemingway-API-Mustern. Berechnet Flesch-Kincaid-, Gunning-Fog- und SMOG-Indizes mit automatischen Umschreibungsvorschlägen.

Was ist Content Readability Optimizer?

Der Content Readability Optimizer ist ein KI-Agenten-Skill, der die Lesbarkeit von Texten automatisch analysiert und verbessert. Er nutzt die textstat-Python-Bibliothek, um gängige Lesbarkeitsindizes wie Flesch-Kincaid Grade Level, Gunning Fog Index und SMOG Index zu berechnen. Zusätzlich integriert der Skill Muster aus der Hemingway-API, um komplexe Sätze, Passivkonstruktionen und übermäßige Adverbien zu identifizieren und automatisch Umschreibungsvorschläge zu generieren. Der Skill ist besonders nützlich für Entwickler und Redakteure, die Inhalte barrierefrei und leicht verständlich gestalten möchten. Er unterstützt Plattformen wie Claude Code, Cursor und Codex und kann über einfache Python-Befehle oder als Teil einer Pipeline zur Inhaltsoptimierung eingesetzt werden. Die Installation erfolgt über pip oder Git, und die Dokumentation der textstat-Bibliothek dient als primäre Quelle für die Implementierung.

Funktioniert mit~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/agentskillexchange/skills/tree/main/skills/content-readability-optimizer
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Dokumentation

Was macht Content Readability Optimizer?

Analysiert und optimiert die Lesbarkeit von Inhalten mithilfe der textstat-Python-Bibliothek und Hemingway-API-Mustern. Berechnet Flesch-Kincaid-, Gunning-Fog- und SMOG-Indizes mit automatischen Umschreibungsvorschlägen.

Installation

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

Anforderungen und Hinweise aus dem Upstream:

  • python
  • Sie können textstat entweder über den Python Package Index (PyPI) oder aus dem Quellcode installieren.
  • Laden Sie die neueste Version von textstat von http://pypi.python.org/pypi/textstat/ herunter

Grundlegende Nutzung oder erste Schritte:

Quelle

Individual skills in this repo

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

Academic Paper Citation Network Mapper

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act Local GitHub Actions Runner

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Add Claude design-system and accessibility workflows with UX/UI

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.

Add IDE-style semantic code navigation and symbol-safe refactors

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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.

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Add runtime guardrails to TypeScript agents with VoltAgent

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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.

Adobe Firefly API Batch Image Generator

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