An expert system designed to 10x your productivity by optimizing prompts, designing automated workflows, and selecting the right AI tools.
The fastest way to install a skill directly from the registry.
npx clawhub@latest install ai-workflow-optimizer
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
Copy this prompt to OpenClaw to install it automatically.
Help me install ai-workflow-optimizer using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Get the raw skill files in a ZIP archive.
The AI Workflow Optimizer is a specialized skill focused on bridging the gap between basic AI usage and professional-grade automation. It provides users with a structured approach to interacting with large language models like ChatGPT, Claude, and Cursor, ensuring that every interaction yields high-quality, actionable results.
By leveraging advanced frameworks like CRISPE, this skill helps users move beyond vague requests to highly specific, context-aware instructions. Whether you are looking to fix AI hallucinations, manage long-context documents, or build a multi-stage content creation pipeline, this addition to your Openclaw Skills library provides the technical blueprint for success.
To integrate this skill into your environment, ensure you have the Openclaw Skills runner installed and use the following configuration:
# Clone or download the skill file
# Add the ai-workflow-optimizer to your skill directory
# Example invocation
openclaw run ai-workflow-optimizer --task "optimize my coding prompt"
The skill organizes its optimization logic through structured frameworks and comparative data tables:
| Data Type | Description | Key Components |
|---|---|---|
| CRISPE Metadata | Framework for prompt engineering | Capacity, Request, Input, Specifics, Purpose, Example |
| Tool Taxonomy | Database of AI tool strengths | Context window size, IDE integration, visual capabilities |
| Workflow Logic | Sequence of operations | Input -> Analysis -> Generation -> Refinement -> Output |
| Troubleshooting | Error-Solution mapping | Temperature control, RAG implementation, Context summarization |
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