Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub ยท Developer Tools
One API key for Chinese AI models. Route to Qwen, Deepseek
China LLM Gateway - Unified interface for Chinese LLMs including Qwen, DeepSeek, GLM, Baichuan. OpenAI compatible, one API Key for all models.
skill openclawclawhub Free
China LLM Gateway - Unified interface for Chinese LLMs including Qwen, DeepSeek, GLM, Baichuan. OpenAI compatible, one API Key for all models.
โฌ 0 downloads โ
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Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- README.md, SKILL.md, scripts/cn_llm_client.py
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Then review README.md for any prerequisites, environment setup, or post-install checks. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Then review README.md for any prerequisites, environment setup, or post-install checks. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.0
Provenance
- Publisher
- chaimengphp
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
OpenClaw CN-LLM ๐
China LLM Unified Gateway. Powered by AIsa. One API Key to access all Chinese LLMs. OpenAI compatible interface. Qwen, DeepSeek, GLM, Baichuan, Moonshot, and more - unified API access.
Intelligent Chat
"Use Qwen to answer Chinese questions, use DeepSeek for coding"
Deep Reasoning
"Use DeepSeek-R1 for complex reasoning tasks"
Code Generation
"Use DeepSeek-Coder to generate Python code with explanations"
Long Text Processing
"Use Qwen-Long for ultra-long document summarization"
Model Comparison
"Compare response quality between Qwen-Max and DeepSeek-V3"
Qwen (Alibaba)
ModelInput PriceOutput PriceFeaturesqwen3-max$1.37/M$5.48/MMost powerful general modelqwen3-max-2026-01-23$1.37/M$5.48/MLatest versionqwen3-coder-plus$2.86/M$28.60/MEnhanced code generationqwen3-coder-flash$0.72/M$3.60/MFast code generationqwen3-coder-480b-a35b-instruct$2.15/M$8.60/M480B large modelqwen3-vl-plus$0.43/M$4.30/MVision-language modelqwen3-vl-flash$0.86/M$0.86/MFast vision modelqwen3-omni-flash$4.00/M$16.00/MMultimodal modelqwen-vl-max$0.23/M$0.57/MVision-languageqwen-plus-2025-12-01$1.26/M$12.60/MPlus versionqwen-mt-flash$0.168/M$0.514/MFast machine translationqwen-mt-lite$0.13/M$0.39/MLite machine translation
DeepSeek
ModelInput PriceOutput PriceFeaturesdeepseek-r1$2.00/M$8.00/MReasoning model, supports Toolsdeepseek-v3$1.00/M$4.00/MGeneral chat, 671B parametersdeepseek-v3-0324$1.20/M$4.80/MV3 stable versiondeepseek-v3.1$4.00/M$12.00/MLatest Terminus version Note: Prices are in M (million tokens). Model availability may change, see marketplace.aisa.one/pricing for the latest list.
Quick Start
export AISA_API_KEY="your-key"
OpenAI Compatible Interface
POST https://api.aisa.one/v1/chat/completions Qwen Example curl -X POST "https://api.aisa.one/v1/chat/completions" \ -H "Authorization: Bearer $AISA_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "model": "qwen3-max", "messages": [ {"role": "system", "content": "You are a professional Chinese assistant."}, {"role": "user", "content": "Please explain what a large language model is?"} ], "temperature": 0.7, "max_tokens": 1000 }' DeepSeek Example # DeepSeek-V3 general chat (671B parameters) curl -X POST "https://api.aisa.one/v1/chat/completions" \ -H "Authorization: Bearer $AISA_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "model": "deepseek-v3", "messages": [{"role": "user", "content": "Write a quicksort algorithm in Python"}], "temperature": 0.3 }' # DeepSeek-R1 deep reasoning (supports Tools) curl -X POST "https://api.aisa.one/v1/chat/completions" \ -H "Authorization: Bearer $AISA_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "model": "deepseek-r1", "messages": [{"role": "user", "content": "A farmer needs to cross a river with a wolf, a sheep, and a cabbage. The boat can only carry the farmer and one item at a time. If the farmer is not present, the wolf will eat the sheep, and the sheep will eat the cabbage. How can the farmer safely cross?"}] }' # DeepSeek-V3.1 Terminus latest version curl -X POST "https://api.aisa.one/v1/chat/completions" \ -H "Authorization: Bearer $AISA_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "model": "deepseek-v3.1", "messages": [{"role": "user", "content": "Implement an LRU cache with get and put operations"}] }' Qwen3 Code Generation Example curl -X POST "https://api.aisa.one/v1/chat/completions" \ -H "Authorization: Bearer $AISA_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "model": "qwen3-coder-plus", "messages": [{"role": "user", "content": "Implement a thread-safe Map in Go"}] }' Parameter Reference ParameterTypeRequiredDescriptionmodelstringYesModel identifiermessagesarrayYesMessage listtemperaturenumberNoRandomness (0-2, default 1)max_tokensintegerNoMaximum tokens to generatestreambooleanNoStream output (default false)top_pnumberNoNucleus sampling parameter (0-1) Response Format { "id": "chatcmpl-xxx", "object": "chat.completion", "created": 1234567890, "model": "qwen-max", "choices": [ { "index": 0, "message": { "role": "assistant", "content": "A large language model (LLM) is a deep learning-based..." }, "finish_reason": "stop" } ], "usage": { "prompt_tokens": 30, "completion_tokens": 150, "total_tokens": 180, "cost": 0.001 } }
Streaming Output
curl -X POST "https://api.aisa.one/v1/chat/completions" \ -H "Authorization: Bearer $AISA_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "model": "qwen-plus", "messages": [{"role": "user", "content": "Tell a Chinese folk story"}], "stream": true }' Returns Server-Sent Events (SSE) format: data: {"id":"chatcmpl-xxx","choices":[{"delta":{"content":"Once"}}]} data: {"id":"chatcmpl-xxx","choices":[{"delta":{"content":" upon"}}]} ... data: [DONE]
CLI Usage
# Qwen chat python3 {baseDir}/scripts/cn_llm_client.py chat --model qwen3-max --message "Hello, please introduce yourself" # Qwen3 code generation python3 {baseDir}/scripts/cn_llm_client.py chat --model qwen3-coder-plus --message "Write a binary search algorithm" # DeepSeek-R1 reasoning python3 {baseDir}/scripts/cn_llm_client.py chat --model deepseek-r1 --message "Which is larger, 9.9 or 9.11? Please reason in detail" # DeepSeek-V3 chat python3 {baseDir}/scripts/cn_llm_client.py chat --model deepseek-v3 --message "Tell a story" --stream # With system prompt python3 {baseDir}/scripts/cn_llm_client.py chat --model qwen3-max --system "You are a classical poetry expert" --message "Write a poem about plum blossoms" # Model comparison python3 {baseDir}/scripts/cn_llm_client.py compare --models "qwen3-max,deepseek-v3" --message "What is quantum computing?" # List supported models python3 {baseDir}/scripts/cn_llm_client.py models
Python SDK Usage
from cn_llm_client import CNLLMClient client = CNLLMClient() # Uses AISA_API_KEY environment variable # Qwen chat response = client.chat( model="qwen3-max", messages=[{"role": "user", "content": "Hello!"}] ) print(response["choices"][0]["message"]["content"]) # Qwen3 code generation response = client.chat( model="qwen3-coder-plus", messages=[ {"role": "system", "content": "You are a professional programmer."}, {"role": "user", "content": "Implement a singleton pattern in Python"} ], temperature=0.3 ) # Streaming output for chunk in client.chat_stream( model="deepseek-v3", messages=[{"role": "user", "content": "Tell a story about an idiom"}] ): print(chunk, end="", flush=True) # Model comparison results = client.compare_models( models=["qwen3-max", "deepseek-v3", "deepseek-r1"], message="Explain what machine learning is" ) for model, result in results.items(): print(f"{model}: {result['response'][:100]}...")
1. Chinese Content Generation
# Copywriting response = client.chat( model="qwen3-max", messages=[ {"role": "system", "content": "You are a professional copywriter."}, {"role": "user", "content": "Write a product introduction for a smart watch"} ] )
2. Code Development
# Code generation and explanation response = client.chat( model="qwen3-coder-plus", messages=[{"role": "user", "content": "Implement a thread-safe Map in Go"}] )
3. Complex Reasoning
# Mathematical reasoning response = client.chat( model="deepseek-r1", messages=[{"role": "user", "content": "Prove: For any positive integer n, nยณ-n is divisible by 6"}] )
4. Visual Understanding
# Image understanding response = client.chat( model="qwen3-vl-plus", messages=[ {"role": "user", "content": [ {"type": "text", "text": "Describe the content of this image"}, {"type": "image_url", "image_url": {"url": "https://example.com/image.jpg"}} ]} ] )
5. Model Routing Strategy
MODEL_MAP = { "chat": "qwen3-max", # General chat "code": "qwen3-coder-plus", # Code generation "reasoning": "deepseek-r1", # Complex reasoning "vision": "qwen3-vl-plus", # Visual understanding "fast": "qwen3-coder-flash", # Fast response "translate": "qwen-mt-flash" # Machine translation } def route_by_task(task_type: str, message: str) -> str: model = MODEL_MAP.get(task_type, "qwen3-max") return client.chat(model=model, messages=[{"role": "user", "content": message}])
Error Handling
Errors return JSON with error field: { "error": { "code": "model_not_found", "message": "Model 'xxx' is not available" } } Common error codes: 401 - Invalid or missing API Key 402 - Insufficient balance 404 - Model not found 429 - Rate limit exceeded 500 - Server error
Pricing
ModelInput ($/M)Output ($/M)qwen3-max$1.37$5.48qwen3-coder-plus$2.86$28.60qwen3-coder-flash$0.72$3.60qwen3-vl-plus$0.43$4.30deepseek-v3$1.00$4.00deepseek-r1$2.00$8.00deepseek-v3.1$4.00$12.00 Price unit: $ per Million tokens. Each response includes usage.cost and usage.credits_remaining.
Get Started
Register at aisa.one Get API Key Top up (pay-as-you-go) Set environment variable: export AISA_API_KEY="your-key"
Full API Reference
See API Reference for complete endpoint documentation.
Category context
Code helpers, APIs, CLIs, browser automation, testing, and developer operations.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
2 Docs1 Scripts
- SKILL.md
- README.md
- scripts/cn_llm_client.py