Analyzes Rocket.Chat messages and generates JSON-based smart replies.
npx skills add https://github.com/rgeissen/uderia --skill profile-perf
Please help me install this Agent Skill. Skill: profile-perf Source: https://github.com/rgeissen/uderia/tree/main/.claude/skills/profile-perf Command: npx skills add https://github.com/rgeissen/uderia --skill profile-perf
Performance testing and optimization framework for Uderia profiles to measure and improve their execution efficiency across different profile types.
Run a quick IDEAT vs OPTIM performance comparison on a sample query to observe token usage and timing.
High-intent search queries and answers about installing and using this skill.
To benchmark Uderia profile execution performance, run a quick IDEAT versus OPTIM profile comparison on a sample query. This analyzes execution traces, collects token usage, and compares metrics to identify optimization opportunities.
Analyzing execution traces for profile optimization involves comparing metrics between different profile runs to validate plan quality. This process identifies bottlenecks, detects inappropriate LLM result formats, and generates actionable optimization reports for performance benchmarking.
To detect recursive executions or issues in Uderia profile runs, analyze execution traces during performance benchmarking. This identifies optimization opportunities and potential problems like inappropriate LLM result formats, generating actionable reports for resolution.
Yes, you can measure token usage across different profile types by comparing IDEAT and OPTIM profile runs. The framework analyzes execution traces to collect token usage data and validate strategic plan quality for optimization.
Validating strategic plans in Uderia profiles identifies bottlenecks, recursive executions, and inappropriate LLM result formats. By analyzing execution traces and comparing metrics, the framework generates actionable optimization reports to resolve these issues.
High token usage during Uderia profile execution is identified by analyzing execution traces and comparing metrics between profile runs. This benchmarking detects optimization opportunities and potential issues like recursive executions, guiding performance improvements.
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