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Search Document Ingestion , found 14 results

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Clarity Gate Verification

sickn33/antigravity-awesome-skills

Clarity Gate ensures documents entering RAG knowledge bases retain epistemic quality by generating Clarity-Gated Documents, validating Source of Truth tables, and running deterministic claim/document hash scripts before ingestion.

Production Patterns for LLM Applications

llm-app-patterns

sickn33/antigravity-awesome-skills

This comprehensive guide provides production-ready patterns for building advanced LLM applications. It details the entire RAG pipeline, covering document ingestion, various chunking strategies, and advanced retrieval methods (Hybrid, Multi-query). Furthermore, it covers sophisticated agent architectures, such as ReAct, enabling complex reasoning and tool use. Ideal for developers designing enterprise-grade AI systems.

MarkItDown Document Conversion

K-Dense-AI/scientific-agent-skills

Convert PDF, Office, HTML, CSV, EPUB and other documents into structure-preserving Markdown with Microsoft MarkItDown. Covers safe local and stream conversion, batch workflows, plugins, vision OCR, Azure extraction, and MCP integration for search and LLM/RAG ingestion.

Glean Enterprise Search Deployment Integration

glean-deploy-integration

jeremylongshore/claude-code-plugins-plus-skills

This skill provides a complete solution for deploying Glean custom connectors as scheduled jobs on container platforms (e.g., Cloud Run). It covers building a production-ready Docker image to manage document ingestion, custom data source connectivity, and search query execution. Includes robust health checks and rolling update strategies to ensure continuous, high-availability indexing for enterprise search environments.

Glean Data Indexing and Governance

glean-data-handling

jeremylongshore/claude-code-plugins-plus-skills

This skill governs the secure ingestion and indexing of enterprise documents from diverse connectors (e.g., Google Drive, Confluence, Jira). Key functions include mandatory PII filtering (stripping emails, phones, SSNs) before indexing, preserving strict permission boundaries, and enforcing data retention policies to ensure compliance with global regulations like GDPR and CCPA.

Grammarly Document Data Handling Pipeline

grammarly-data-handling

jeremylongshore/claude-code-plugins-plus-skills

Implements a robust data pipeline for handling large documents with the Grammarly API. This utility manages text chunking to comply with API character limits (max 100k), processes text in manageable segments, and aggregates multiple scoring metrics (like correctness, clarity, and tone) into a single, comprehensive report. Ideal for large-scale content analysis and data ingestion.

Comprehensive PDF Manipulation Toolkit

document-skills-pdf

aitytech/agentkits-marketing

This comprehensive guide covers essential operations for handling PDF documents, including text, table, and metadata extraction using libraries like pdfplumber. It details document creation with reportlab, merging/splitting with pypdf/qpdf, and advanced OCR for scanned PDFs. Ideal for automating complex document workflows and structured data ingestion.

Data Pipeline Specification Generator

data-pipeline-spec

mohitagw15856/pm-claude-skills

This skill generates a complete and detailed specification for an ETL/ELT data pipeline. It covers everything from source systems, transformation logic, and data quality rules to destination data warehousing and required SLAs. Use this when planning data ingestion, designing complex data flows, or needing comprehensive architecture documentation for engineering handoff.

Clinical Document Ingestion and Preprocessing

ingesting-clinical-documents

maziyarpanahi/openmed

This module converts diverse clinical inputs—including scanned images, faxes, CSV/TSV exports, and C-CDA XML—into a normalized, clean text format (`ExtractedDocument`). It supports on-device OCR (Tesseract/PaddleOCR) and structured redaction for tables. It is the crucial first stage for feeding de-identification and Named Entity Recognition (NER) pipelines.

Clarity Gate RAG Verification

sickn33/agentic-awesome-skills

Pre-ingestion verification skill for RAG systems that checks documents for epistemic clarity, flags unmarked assumptions, and generates CGDs to reduce hallucination risk before knowledge base ingestion.

Table Extraction from Documents

extracting-tables

Extract structured tabular data from PDFs, spreadsheets, and images using layout-aware detection and configurable table models, with markdown or JSON cell output for LLM ingestion and downstream processing.

Text Chunking for LLM Context

This skill enables splitting extracted text into optimized chunks for LLM context windows or RAG ingestion pipelines. It supports configurable chunk sizes, overlap settings, and various chunker types including text, markdown, YAML, and semantic splitting. Users can utilize token-based sizing for specific models or standalone commands for pre-existing text. Ideal for preparing documents for vector stores or summarization tasks.

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