Command Optimization
Refines complex user commands into efficient, executable steps, minimizing errors and maximizing task completion success.
Install on your platform
We auto-selected Claude Code based on this skill’s supported platforms.
Run in terminal (recommended)
claude mcp add command-optimization npx -- -y @trustedskills/command-optimization
Or manually add to ~/.claude/settings.json
{
"mcpServers": {
"command-optimization": {
"command": "npx",
"args": [
"-y",
"@trustedskills/command-optimization"
]
}
}
}Requires Claude Code (claude CLI). Run claude --version to verify your install.
About This Skill
What it does
This skill optimizes commands provided to an AI agent, aiming to improve efficiency and accuracy. It refines input by removing unnecessary words or phrases and restructuring sentences for clarity. The goal is to ensure the AI understands the intended task precisely, leading to better results with fewer resources.
When to use it
- Complex requests: Use when you're unsure if your command is being fully understood by the agent due to its complexity.
- Resource-intensive tasks: Optimize commands for tasks that consume significant processing power or time.
- Iterative refinement: Employ this skill during a process of refining prompts and observing AI output.
- Long, verbose instructions: When providing lengthy instructions, use it to ensure the core intent is clear.
Key capabilities
- Removes unnecessary words and phrases
- Restructures sentences for clarity
- Improves command efficiency
- Enhances accuracy of AI understanding
Example prompts
- "Optimize this command: 'Please find all documents related to project alpha and summarize the key findings.'"
- "Can you refine this prompt for better results? 'Write a short story about a cat who goes on an adventure.'"
- "Improve the following instruction: 'I want you to create a presentation that includes data from last quarter, with charts and graphs.'"
Tips & gotchas
The effectiveness of command optimization depends on the initial clarity of the prompt. While it can improve existing commands, poorly worded or ambiguous requests may still yield suboptimal results after refinement.
Tags
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