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Official Verified ai models Safety 4/5

aimlapi-llm-reasoning

Run AIMLAPI LLM and reasoning workflows through chat completions with retries, structured outputs, and explicit User-Agent headers. Use when Codex needs scripted prompting/reasoning calls against AIMLAPI models.

skill-install — Terminal

Install via CLI (Recommended)

clawhub install openclaw/skills/skills/aimlapihello/aiml-llm-reasoning
Or

What This Skill Does

The aimlapi-llm-reasoning skill serves as a specialized gateway for integrating advanced LLM workflows directly into the OpenClaw environment. It provides a robust interface for interacting with AIMLAPI chat completion models, enabling developers and agents to execute sophisticated reasoning tasks. At its core, the skill utilizes a highly resilient Python-based execution script that manages API calls, implements automated retries for network stability, and enforces structured communication through mandated User-Agent headers.

Installation

To integrate this skill into your environment, use the command-line interface to pull from the official OpenClaw repository. Ensure you have your AIMLAPI credentials ready to export into your environment variables. Execute: clawhub install openclaw/skills/skills/aimlapihello/aiml-llm-reasoning.

Use Cases

This skill is ideal for scenarios requiring structured LLM responses, such as generating JSON-formatted project documentation, performing complex task planning, or running chain-of-thought workflows. Because it supports explicit configuration through the --extra-json flag, it is perfectly suited for complex developer tasks where fine-grained control over model temperature, response formats, and reasoning effort is required. Use it when Codex or your autonomous agent needs to output reliable, parseable machine data.

Example Prompts

  1. 'Run a reasoning-based analysis on these three potential software architectural patterns, outputting the result as a structured JSON object.'
  2. 'Draft a technical implementation plan for a Python script, ensuring the output focuses on memory efficiency and utilizes a temperature of 0.2.'
  3. 'Summarize the following log file into a bulleted list of potential security threats using the aimlapi/openai/gpt-5-nano-2025-08-07 model.'

Tips & Limitations

Always ensure the AIMLAPI_API_KEY is properly set in your shell environment to avoid authentication failures. When requesting structured outputs, always use the --extra-json flag with response_format to ensure valid parsing. Note that excessive reliance on high-reasoning parameter configurations may increase latency; test your prompts first with minimal settings. Be aware that this skill performs external network requests, so ensure your system security policy permits outgoing API traffic to the AIMLAPI endpoints.

Metadata

Stars4473
Views0
Updated2026-05-01
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Add to Configuration

Paste this into your clawhub.json to enable this plugin.

{
  "plugins": {
    "official-aimlapihello-aiml-llm-reasoning": {
      "enabled": true,
      "auto_update": true
    }
  }
}

Tags(AI)

#llm#reasoning#automation#api#integration
Safety Score: 4/5

Flags: network-access, file-write, external-api