Docling is an IBM-backed open-source toolkit that converts PDF, DOCX, PPTX, XLSX, HTML, images, audio, and LaTeX files into structured formats for gen AI workflows. It features advanced PDF layout understanding, OCR, table extraction, and integrations with LangChain, LlamaIndex, and CrewAI.
Scanned 6/8/2026
Install via CLI
openskills install agentskillexchange/skills---
name: "Docling AI Document Intelligence Pipeline"
slug: "docling-ai-document-intelligence-pipeline"
description: "Docling is an IBM-backed open-source toolkit that converts PDF, DOCX, PPTX, XLSX, HTML, images, audio, and LaTeX files into structured formats for gen AI workflows. It features advanced PDF layout understanding, OCR, table extraction, and integrations with LangChain, LlamaIndex, and CrewAI."
github_stars: 56871
verification: "security_reviewed"
source: "https://github.com/docling-project/docling"
category: "Data Extraction & Transformation"
framework: "Claude Code"
tool_ecosystem:
github_repo: "docling-project/docling"
github_stars: 56871
---
# Docling AI Document Intelligence Pipeline
Docling is an IBM-backed open-source toolkit that converts PDF, DOCX, PPTX, XLSX, HTML, images, audio, and LaTeX files into structured formats for gen AI workflows. It features advanced PDF layout understanding, OCR, table extraction, and integrations with LangChain, LlamaIndex, and CrewAI.
## Installation
Use the upstream install or setup path that matches your environment:
- pip install docling
Requirements and caveats from upstream:
- [](https://pypi.org/project/docling/)
- **Note:** Python 3.9 support was dropped in docling version 2.70.0. Please use Python 3.10 or higher.
- ## 3. Python usage (recommended)
Basic usage or getting-started notes:
- 🔌 Connect to any agent using the [MCP server](https://docling-project.github.io/docling/usage/mcp/)
- 🔌 [MCP server](https://docling-project.github.io/docling/usage/mcp/) for agentic applications
- ### 1. Install
- Source: https://github.com/docling-project/docling
- Extracted from upstream docs: https://raw.githubusercontent.com/docling-project/docling/HEAD/README.md
## Source
- [Agent Skill Exchange](https://agentskillexchange.com/skills/docling-ai-document-intelligence-pipeline/)
No comments yet. Be the first to comment!
Ultra-compressed communication mode. Cuts token usage ~75% by speaking like caveman while keeping full technical accuracy. Supports intensity levels: lite, full (default), ultra, wenyan-lite, wenyan-full, wenyan-ultra. Use when user says "caveman mode", "talk like caveman", "use caveman", "less tokens", "be brief", or invokes /caveman. Also auto-triggers when token efficiency is requested.
Adversarial multi-agent planning skill. Self-orchestrates 5 hostile category members (unspecified-low, unspecified-high, deep, ultrabrain, artistry) via team-mode for ruthless cross-critique debate, distills only the defensible insights, then MANDATORILY hands the distilled insight bundle to the `plan` agent for executable plan formalization. Use when planning needs maximum rigor and surfacing of weak assumptions, blind spots, and over-engineering. Triggers: 'hyperplan', 'hpp', '/hyperplan', ...
**Complete production-ready guide for Google Gemini embeddings API** This skill provides comprehensive coverage of the `gemini-embedding-001` model for generating text embeddings, including SDK usage, REST API patterns, batch processing, RAG integration with Cloudflare Vectorize, and advanced use cases like semantic search and document clustering. ---
Interview, source-challenge, verify, save, and ADR-gate fuzzy coding requests into Codex-ready implementation specs. Use when a feature, bugfix, refactor, migration, repo-wide change, or architecture task needs user-verified requirements, source-backed decisions, durable architecture decisions, acceptance criteria, validation commands, rollout notes, saved spec/ADR files, and a Codex execution prompt. Do not use when already fully specified or when the user wants direct implementation now.
Use when a repo needs CodeGraph plus ast-grep for Codex MCP setup, exploration, impact analysis, structural search, or safe refactor planning.