Performance Benchmark Setter
Automatically establishes baseline performance metrics for AI models based on historical data and industry standards.
Install on your platform
We auto-selected Claude Code based on this skill’s supported platforms.
Run in terminal (recommended)
claude mcp add performance-benchmark-setter npx -- -y @trustedskills/performance-benchmark-setter
Or manually add to ~/.claude/settings.json
{
"mcpServers": {
"performance-benchmark-setter": {
"command": "npx",
"args": [
"-y",
"@trustedskills/performance-benchmark-setter"
]
}
}
}Requires Claude Code (claude CLI). Run claude --version to verify your install.
About This Skill
What it does
This skill allows AI agents to establish performance benchmarks for various tasks. It can define metrics, set target values, and track progress against those targets. The agent uses these benchmarks to optimize its output and ensure consistent quality over time.
When to use it
- Content Creation: Set a benchmark for article word count or video length to maintain consistency across multiple pieces of content.
- Code Generation: Establish a performance target (e.g., code execution speed) to guide the AI agent's coding process.
- Data Analysis: Define accuracy thresholds when analyzing data sets, ensuring reliable results.
- Customer Service Chatbots: Measure and improve response time or customer satisfaction scores over time.
Key capabilities
- Metric definition
- Target value setting
- Progress tracking
- Performance optimization
Example prompts
- "Set a benchmark for generating blog posts, aiming for 1500 words per post."
- "Establish a performance target of 95% accuracy when classifying customer support tickets."
- "Track the average response time for chatbot interactions and set a goal to reduce it by 20%."
Tips & gotchas
The skill's effectiveness relies on clearly defined metrics. Ensure you provide specific, measurable criteria for the AI agent to track.
Tags
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