What problem does it solve? Developers integrating DeepSeek models often struggle with choosing between deepseek-chat, deepseek-coder, and deepseek-reasoner, writing correct API call code, estimating token costs, and diagnosing API errors. This Skill provides direct guidance for all of these tasks. ## Core Features & Use Cases - Model Selection Guidance: Compares deepseek-chat, deepseek-coder, and deepseek-reasoner by use case and context length so you pick the right model. - API Call Examples: Generates ready-to-use call code in Python, curl, and Node.js using the OpenAI-compatible endpoint at https://api.deepseek.com. - Pricing Estimation: Calculates monthly costs from token usage based on input and output rates. - Error Troubleshooting: Explains common API error codes such as 429 rate limiting and how to resolve them. - Use Case: You are building a customer service bot and ask which DeepSeek model fits, then request a Python multi-turn conversation script and a monthly cost estimate for 1000 messages per day. ## Quick Start Ask the assistant to write a Python script that calls the DeepSeek API for multi-turn conversation and recommend the right model for your scenario.
