llmbooster

What This Skill Does

LLMBooster is a sophisticated 4-step thinking framework designed to significantly enhance the output quality of LLMs within the OpenClaw environment. Unlike traditional automated tools that require external processing, LLMBooster operates internally by enforcing a structured cognitive flow. It guides the model through a sequence of Plan, Draft, Self-Critique, and Refine stages, effectively mitigating common issues such as hasty conclusions, missed technical details, and superficial reasoning. The skill is highly configurable, allowing users to adjust the 'thinking depth' based on whether they need a quick brainstorming session or a rigorous, multi-stage technical analysis.

Installation

To integrate this skill into your OpenClaw environment, execute the following command in your terminal:

clawhub install openclaw/skills/skills/danlct27/llmbooster

Once installed, you can manage the skill's state and configuration using the /booster command prefix. Ensure that the directory structure is maintained as per the repository instructions to allow the CLI handler to function correctly.

Use Cases

LLMBooster is best utilized for tasks that require high accuracy and depth. It excels in complex code reviews, drafting professional technical documentation, conducting comparative analysis, and solving multi-step logical problems. It is particularly effective when working with lower-parameter models that may benefit from a forced 'step-by-step' approach to prevent hallucinations or logic gaps.

Example Prompts

1. "/booster please perform a detailed analysis of this Python script and identify potential memory leaks."
2. "I need a high-quality summary of these research papers, use booster to ensure all key findings are captured accurately."
3. "Use booster for this task: compare the performance implications of PostgreSQL versus MongoDB for a high-write financial application."

Tips & Limitations

To maximize the utility of this skill, set the depth level according to your requirements. Using a depth of 4 provides the highest quality but increases latency significantly. Conversely, for simple queries, a depth of 1 or 2 is usually sufficient to maintain efficiency without unnecessary overhead. Keep in mind that while LLMBooster forces logical progression, it remains dependent on the underlying model's capability; it acts as a structured reasoning scaffold rather than a substitute for model intelligence.