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**apcore-toolkit** is a shared scanner, schema extraction, and output toolkit for the [apcore](https://github.com/aipartnerup/apcore-python) ecosystem. It provides framework-agnostic logic to extract metadata from existing code and make it "AI-Perceivable".
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**apcore-toolkit** is a shared scanner, schema extraction, and output toolkit for the [apcore](https://github.com/aiperceivable/apcore-python) ecosystem. It provides framework-agnostic logic to extract metadata from existing code and make it "AI-Perceivable".
-**🚀 Multi-Language Support**: Implementation available for [🐍 Python](https://github.com/aipartnerup/apcore-toolkit-python), [📘 TypeScript](https://github.com/aipartnerup/apcore-toolkit-typescript), and [🦀 Rust](https://github.com/aipartnerup/apcore-toolkit-rust).
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-**🚀 Multi-Language Support**: Implementation available for [🐍 Python](https://github.com/aiperceivable/apcore-toolkit-python), [📘 TypeScript](https://github.com/aiperceivable/apcore-toolkit-typescript), and [🦀 Rust](https://github.com/aiperceivable/apcore-toolkit-rust).
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-**🔍 Smart Scanning**: Abstract base classes for framework scanners with filtering and deduplication.
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-**📄 Output Generation**: Writers for YAML bindings, language-specific wrappers, and direct Registry registration.
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-**🛠️ Schema Utilities**: Tools for Pydantic/Zod model flattening and OpenAPI schema extraction.
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-**🤖 AI Enhancement**: Built-in `AIEnhancer` with local SLM support, pluggable `Enhancer` protocol, and [apcore-refinery](https://github.com/aipartnerup/apcore-refinery) for production use.
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-**🤖 AI Enhancement**: Built-in `AIEnhancer` with local SLM support, pluggable `Enhancer` protocol, and [apcore-refinery](https://github.com/aiperceivable/apcore-refinery) for production use.
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-**📝 Markdown Formatting**: Convert arbitrary data structures to structured Markdown.
Copy file name to clipboardExpand all lines: docs/ai-enhancement.md
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## 5. Recommended: apcore-refinery
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For production-grade enhancement, we recommend **[apcore-refinery](https://github.com/aipartnerup/apcore-refinery)** — a dedicated tool with specialized prompts, flexible model selection, and CI integration.
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For production-grade enhancement, we recommend **[apcore-refinery](https://github.com/aiperceivable/apcore-refinery)** — a dedicated tool with specialized prompts, flexible model selection, and CI integration.
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```bash
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# Scan with toolkit, then refine with apcore-refinery
Copy file name to clipboardExpand all lines: docs/features/overview.md
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|**[Schema Utilities](pydantic.md)**| Flatten complex models (Pydantic / Zod) for easier AI interaction. |
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|**[Output Writers](output-writers.md)**| Export metadata to YAML bindings, source code wrappers, or direct Registry registration — with optional output verification. |
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|**[Formatting](formatting.md)**| Convert data structures into beautiful, human-readable Markdown. |
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|**[AI Enhancement](../ai-enhancement.md)**| Pluggable `Enhancer` protocol with built-in `AIEnhancer` for local SLMs; [apcore-refinery](https://github.com/aipartnerup/apcore-refinery) recommended for production. |
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|**[AI Enhancement](../ai-enhancement.md)**| Pluggable `Enhancer` protocol with built-in `AIEnhancer` for local SLMs; [apcore-refinery](https://github.com/aiperceivable/apcore-refinery) recommended for production. |
Static analysis can detect some of these patterns. For ambiguous cases, the built-in [AIEnhancer](../ai-enhancement.md) or [apcore-refinery](https://github.com/aipartnerup/apcore-refinery) can assist with AI-based inference.
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Static analysis can detect some of these patterns. For ambiguous cases, the built-in [AIEnhancer](../ai-enhancement.md) or [apcore-refinery](https://github.com/aiperceivable/apcore-refinery) can assist with AI-based inference.
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```
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3.**Run your scanner** with `--ai-enhance`: Missing descriptions, annotations, and schemas will be inferred automatically.
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For production use, we recommend **[apcore-refinery](https://github.com/aipartnerup/apcore-refinery)** which provides better prompts, model flexibility, and CI integration:
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For production use, we recommend **[apcore-refinery](https://github.com/aiperceivable/apcore-refinery)** which provides better prompts, model flexibility, and CI integration:
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