Orchestrate parallel hypothesis evaluation and rank evidence for observed results.
npx skills add https://github.com/Jurel89/copilot-omni --skill trace-jurel89
Please help me install this Agent Skill. Skill: trace Source: https://github.com/Jurel89/copilot-omni/tree/main/skills/trace Command: npx skills add https://github.com/Jurel89/copilot-omni --skill trace-jurel89
This Skill provides an evidence-driven tracing lane to help solve complex problems by orchestrating competing tracer hypotheses in parallel.
To use the trace skill, provide an observation and run /copilot-omni:trace <observation to trace>.
High-intent search queries and answers about installing and using this skill.
Evidence-driven tracing is a method that concurrently evaluates multiple competing hypotheses for a given observation, collecting and ranking evidence to explain the root cause of complex problems like runtime bugs or performance issues.
You can troubleshoot ambiguous runtime bugs by running the trace skill with your observation, which orchestrates parallel hypothesis testing to gather and rank evidence for and against each potential causal explanation.
Yes, parallel tracing is suitable for performance analysis and causal investigation, as it concurrently explores multiple explanations for observed results and ranks the collected evidence to determine the underlying cause.
No external dependencies are required to use this approach, as the skill operates independently using its internal scripts and references to orchestrate the parallel evaluation of competing tracer hypotheses.
The best way to analyze a performance issue is to provide the observation to the trace skill, which concurrently explores multiple explanations and ranks the gathered evidence to reveal why the observed results occurred.
You should avoid using parallel hypothesis testing for simple, non-causal problems with obvious solutions, as this approach is specifically designed for ambiguous, evidence-heavy questions requiring complex problem-solving.
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