Execute high-performance AIMLAPI chat completions and complex reasoning workflows with built-in retries and structured output support.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install aiml-llm-reasoning
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 aiml-llm-reasoning 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 AIMLAPI LLM + Reasoning?
The AIMLAPI LLM + Reasoning skill provides a robust interface for interacting with various language models via the AIMLAPI platform. It is designed to handle scripted prompting and reasoning calls that require advanced configurations like custom User-Agent headers, automated retries, and specialized reasoning parameters. This skill is a core component for developers using Openclaw Skills to build reliable AI-driven automation.
By encapsulating the complexities of API communication within a dedicated script, it allows for consistent execution of LLM tasks across different environments. Whether you are generating concise summaries or performing deep reasoning tasks with specific effort levels, this skill ensures that the underlying AI agent protocols are followed correctly while maintaining high observability through custom headers.
AIMLAPI LLM + Reasoning Use Cases
- Automating complex reasoning tasks that require specific cognitive effort settings.
- Generating structured JSON datasets for integration into web applications or databases.
- Executing scripted prompts within CI/CD pipelines or local development environments.
- Implementing resilient AI chat workflows that require automatic retry logic for production stability.
How AIMLAPI LLM + Reasoning Works
- The user authenticates by setting the AIMLAPI_API_KEY environment variable.
- Commands are issued through the run_chat.py script, specifying the target model and prompt content.
- The skill processes optional parameters such as reasoning effort, temperature, and response format from a JSON string.
- A request is dispatched to the AIMLAPI endpoint, including a mandatory User-Agent header for better request tracking.
- The script handles any necessary retries and either streams the response to the terminal or saves it to a designated output file.
AIMLAPI LLM + Reasoning Setup
To get started with this skill, ensure you have your API key ready and follow these steps:
# Set your environment variable
export AIMLAPI_API_KEY="sk-aimlapi-..."
# Run a quick test to verify connectivity
python3 scripts/run_chat.py --model aimlapi/openai/gpt-5-nano-2025-08-07 --user "Test prompt"
AIMLAPI LLM + Reasoning Data Schema & Taxonomy
The skill manages data primarily through CLI arguments and outputs structured JSON or plain text. Below is the primary schema for interaction:
| Argument | Purpose | Example |
|---|---|---|
--model |
Specifies the LLM to use | aimlapi/openai/gpt-5-nano |
--extra-json |
Passes advanced API parameters | {\"reasoning\": {\"effort\": \"medium\"}} |
--output |
Path to save the result | ./results/output.json |
--user-agent |
Custom header for identification | openclaw-custom/1.0 |
AIMLAPI LLM + Reasoning Advanced Features
- Support for deep reasoning parameters to control model thinking effort.
- Integration of structured output modes to ensure the model returns valid JSON objects.
- Custom User-Agent header injection for enterprise-level monitoring and analytics.
- Flexible API key fallback mechanisms for various deployment scenarios within Openclaw Skills.
SKILL.md
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