A comprehensive developer guide for integrating DeepSeek AI models, optimizing reasoning chains, and managing local LLM deployments.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install deepseek-r1-guide
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 deepseek-r1-guide 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 DeepSeek R1 Guide?
The DeepSeek R1 Guide is an essential resource within the Openclaw Skills ecosystem, designed to empower developers with deep technical expertise in DeepSeek's high-performance AI models. This skill covers everything from the cost-efficient deepseek-chat (V3) to the high-reasoning deepseek-reasoner (R1) and the specialized DeepSeek-Coder-V2.
By leveraging this skill, developers can drastically reduce API costs—often by 90% compared to GPT-4o—while maintaining elite-level performance in mathematics, logic, and code generation. It provides the necessary blueprints for both cloud-based API consumption and private, local hosting using tools like Ollama and vLLM.
DeepSeek R1 Guide Use Cases
- Replacing expensive LLM APIs with deepseek-chat to reduce operational costs by up to 90%.
- Implementing complex logical reasoning or mathematical proofs using the R1 reasoner model.
- Building high-performance RAG (Retrieval-Augmented Generation) systems with low-cost token windows.
- Deploying private, secure AI instances locally using Ollama for sensitive data processing.
- Accelerating software development cycles with specialized Coder-V2 integration.
How DeepSeek R1 Guide Works
- Analyze the specific task requirements to select the optimal model (V3 for general chat, R1 for reasoning, or Coder for dev tasks).
- Configure the API connection using the OpenAI-compatible base URL (https://api.deepseek.com).
- Implement the reasoning chain logic by handling the unique reasoning_content field returned by R1 models.
- Optimize token usage and costs automatically through built-in Prefix Caching.
- Scale or secure the implementation by transitioning to local deployment using GGUF/AWQ quantized versions.
DeepSeek R1 Guide Setup
To start using the DeepSeek Openclaw Skills integration, you can use the following commands for local setup or API testing:
# Install Ollama for local deployment
ollama pull deepseek-r1:8b
# Run the 8B model locally
ollama run deepseek-r1:8b
# Test API connectivity via curl
curl https://api.deepseek.com/chat/completions \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"deepseek-chat","messages":[{"role":"user","content":"hello"}]}'
DeepSeek R1 Guide Data Schema & Taxonomy
The skill manages data across different models with specific focus on reasoning transparency and cost metrics:
| Feature | deepseek-chat (V3) | deepseek-reasoner (R1) |
|---|---|---|
| Core Field | content | reasoning_content + content |
| Cost (Input) | 1/M tokens | 4/M tokens |
| Cache Hit | 0.1/M tokens | 1/M tokens |
| Best For | General NLP | Complex Logic & Math |
DeepSeek R1 Guide Advanced Features
- Reasoning Chain Access: Extract internal thought processes via the reasoning_content field for debugging.
- FIM (Fill-In-the-Middle): Advanced code completion support using the suffix parameter for precise injections.
- Multi-GPU Local Inference: Scaling guides for vLLM with tensor parallelism for 671B parameter models.
- Automatic Prefix Caching: Built-in cost optimization for repetitive system prompts and context.
- Quantization Support: Optimized local hosting instructions for GGUF and AWQ formats to reduce VRAM usage by 50%.
SKILL.md
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