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data-type-handling

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Atualizado11 de abril de 2026 às 02:13

Route RAG handling for code documentation, APIs, images, tables, diagrams, and multimodal content.

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Goodnight77

Goodnight77/rag-skills

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Ocupações relacionadasSOC

Baseado na classificação ocupacional SOC

Desenvolvedores de softwareInformática e Matemática·SOC 15-1252

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SKILL.md

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name	data-type-handling
title	Data Type Handling
description	Route RAG handling for code documentation, APIs, images, tables, diagrams, and multimodal content.
category	data-type-handling
tags	["data-types","code","multimodal","rag"]
allowed-tools	["Read","Grep","Glob"]

Data Type Handling

Overview

Use this parent skill when source material is not plain prose or when different data types need different parsing, metadata, chunking, and retrieval strategies.

Problem Statement

Code, APIs, tables, images, diagrams, and mixed media lose important meaning when treated as generic text. RAG systems need data-specific handling to preserve symbols, structure, and visual context.

Key Concepts

Code structure: Preserve symbols, paths, APIs, and examples.
Multimodal extraction: Convert images, diagrams, and tables into retrievable representations.
Metadata alignment: Keep source references consistent across modalities.
Specialized retrieval: Match query type to the right content representation.

Implementation Guide

Step 1: Classify Source Content

Identify whether the corpus is code-heavy, image-heavy, table-heavy, or mixed.

Step 2: Preserve Native Structure

Use parsers and metadata that retain symbols, captions, headers, page numbers, and source locations.

Step 3: Choose Retrieval Representations

Index text, summaries, captions, code symbols, table schemas, or multimodal embeddings as appropriate.

When to Use This Skill

Building RAG for technical documentation or code repositories
Indexing PDFs, diagrams, screenshots, or tables
Combining text and non-text evidence in answers

When NOT to Use This Skill

The corpus is simple prose
The main issue is vector database operations
The main issue is production latency

Related Skills

Metrics & Success Criteria

Accurate retrieval of symbols, captions, tables, and source references
Lower hallucination on code or visual questions
Clear traceability back to original artifacts

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rag-skills

Goodnight77/rag-skills

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hierarchical-chunking

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Chunk nested documents into parent-child levels so retrieval can move from broad sections to fine-grained passages.

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semantic-chunking

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Use semantic boundaries and embedding similarity to chunk text for higher-relevance retrieval.

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chunking

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Route RAG chunking decisions across semantic, hierarchical, sliding-window, contextual-header, and framework-selection strategies.

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sliding-window-chunking

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Use overlapping windows to preserve context across chunk boundaries while controlling retrieval size.

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Reduce retrieval latency with caching, batching, and index-level optimization.

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name	data-type-handling
title	Data Type Handling
description	Route RAG handling for code documentation, APIs, images, tables, diagrams, and multimodal content.
category	data-type-handling
tags	["data-types","code","multimodal","rag"]
allowed-tools	["Read","Grep","Glob"]

Data Type Handling

Overview

Use this parent skill when source material is not plain prose or when different data types need different parsing, metadata, chunking, and retrieval strategies.

Problem Statement

Code, APIs, tables, images, diagrams, and mixed media lose important meaning when treated as generic text. RAG systems need data-specific handling to preserve symbols, structure, and visual context.

Key Concepts

Code structure: Preserve symbols, paths, APIs, and examples.
Multimodal extraction: Convert images, diagrams, and tables into retrievable representations.
Metadata alignment: Keep source references consistent across modalities.
Specialized retrieval: Match query type to the right content representation.

Implementation Guide

Step 1: Classify Source Content

Identify whether the corpus is code-heavy, image-heavy, table-heavy, or mixed.

Step 2: Preserve Native Structure

Use parsers and metadata that retain symbols, captions, headers, page numbers, and source locations.

Step 3: Choose Retrieval Representations

Index text, summaries, captions, code symbols, table schemas, or multimodal embeddings as appropriate.

When to Use This Skill

Building RAG for technical documentation or code repositories
Indexing PDFs, diagrams, screenshots, or tables
Combining text and non-text evidence in answers

When NOT to Use This Skill

The corpus is simple prose
The main issue is vector database operations
The main issue is production latency

Related Skills

Metrics & Success Criteria

Accurate retrieval of symbols, captions, tables, and source references
Lower hallucination on code or visual questions
Clear traceability back to original artifacts