Dify is an open-source LLM application development platform designed to orchestrate AI workflows, agents, and RAG-based knowledge bases.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install dify
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install dify using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Dify AI Application Development Skill?
Dify provides a comprehensive ecosystem for developers to move from LLM experimentation to production-grade applications. It features a visual Studio for building complex AI logic, a robust Knowledge Base for Retrieval-Augmented Generation (RAG), and seamless integration with various model providers. This Openclaw Skills resource explains how to leverage Dify to create everything from simple chatbots to autonomous agents capable of independent planning.
By providing a unified interface for model management, prompt engineering, and workflow orchestration, Dify simplifies the complexity of the AI stack. Whether you are deploying on-premise or using a cloud version, it offers the tools necessary to build reliable, scalable AI services with built-in observability and API access.
Dify AI Application Development Skill Use Cases
- Building conversational AI agents with persistent session history for customer service.
- Designing complex LLM workflows for automated translation, technical writing, and document summarization.
- Implementing RAG-enhanced knowledge bases using private data from PDF, Markdown, or Word files.
- Orchestrating multi-step logic with parallel execution, conditional branching (IF/ELSE), and iterative loops.
- Integrating external APIs and custom Python or JavaScript code into automated AI pipelines.
How Dify AI Application Development Skill Works
- Select the application type—Chat, Workflow, Agent, or Completion—within the Dify Studio interface.
- Configure model providers and define the necessary input variables for user interactions.
- Construct the logic flow using specialized nodes such as Knowledge Retrieval, Question Classifiers, and Parameter Extractors.
- Integrate external tools or custom code blocks to perform specialized data operations or HTTP requests.
- Deploy the application as a standalone web interface or integrate it into existing systems via the Dify REST API using blocking or streaming response modes.
Dify AI Application Development Skill Setup
To deploy Dify using Docker, follow these steps to get your environment ready for Openclaw Skills development:
# Clone the latest release
git clone --branch "$(curl -s https://api.github.com/repos/langgenius/dify/releases/latest | jq -r .tag_name)" https://github.com/langgenius/dify.git
# Navigate to the docker directory
cd dify/docker
cp .env.example .env
docker compose up -d
Once the containers are running, visit http://localhost/install in your browser to initialize the administrator account and start building.
Dify AI Application Development Skill Data Schema & Taxonomy
Dify organizes its data through a structured taxonomy of applications and knowledge assets:
| Component | Description | Data Type |
|---|---|---|
| Application | Definitions of prompts, nodes, and workflow logic | YAML/JSON |
| Knowledge Base | Indexed document segments for RAG retrieval | Vector/Full-text |
| API Key | Authentication tokens for secure endpoint access | Bearer Token |
| Conversations | Persistent storage for session IDs and message history | DB Records |
| Variables | Input and output parameters defined per application | Structured Key-Value |
Dify AI Application Development Skill Advanced Features
- Hybrid Search capabilities that combine vector embeddings with full-text search for maximum RAG accuracy.
- Advanced Parameter Extraction nodes that use LLMs to turn unstructured natural language into structured JSON.
- Support for custom code execution (Python/Node.js) to handle complex logic that LLMs cannot perform alone.
- Iteration and Parallel nodes for high-performance processing of lists and concurrent task execution.
- Flexible response modes supporting both synchronous blocking calls and asynchronous Server-Sent Events (SSE) streaming.
SKILL.md
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