Axiom Performance Profiling
Analyzes axiom execution to identify bottlenecks and optimize performance through detailed resource usage profiling.
Install on your platform
We auto-selected Claude Code based on this skill’s supported platforms.
Run in terminal (recommended)
claude mcp add axiom-performance-profiling npx -- -y @trustedskills/axiom-performance-profiling
Or manually add to ~/.claude/settings.json
{
"mcpServers": {
"axiom-performance-profiling": {
"command": "npx",
"args": [
"-y",
"@trustedskills/axiom-performance-profiling"
]
}
}
}Requires Claude Code (claude CLI). Run claude --version to verify your install.
About This Skill
The axiom-performance-profiling skill connects AI agents to Axiom, a developer experience platform, enabling the logging and analysis of application performance metrics. It allows agents to capture telemetry data directly from their execution environment for deeper observability.
When to use it
- Investigating latency spikes or throughput issues in production applications.
- Correlating user-reported bugs with specific system resource utilization events.
- Automating the collection of diagnostic traces during automated testing cycles.
- Integrating performance data into existing incident response workflows.
Key capabilities
- Direct integration with the Axiom platform for centralized log management.
- Capability to ingest and structure performance telemetry from various sources.
- Support for querying historical performance data to identify trends.
Example prompts
- "Query Axiom logs for any errors occurring during the last deployment of the payment service."
- "Summarize the average response time trends for the API endpoints over the past week."
- "Correlate high CPU usage events with specific user session IDs in the Axiom dashboard."
Tips & gotchas
Ensure your AI agent has appropriate read permissions configured within your Axiom workspace before attempting to query logs. Performance profiling is most effective when combined with other observability tools that provide context beyond raw metrics, such as distributed tracing or application logs.
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