trace

Orchestrate parallel tracer workers to generate, rank, and falsify causal hypotheses.

2|1|Updated Jul 9, 2026
One-click install
npx skills add https://github.com/yy1588133/oh-my-snow --skill trace-yy1588133
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: trace
Source: https://github.com/yy1588133/oh-my-snow/tree/main/assets/skills/oms/trace
Command: npx skills add https://github.com/yy1588133/oh-my-snow --skill trace-yy1588133

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the challenge of complex problem diagnosis by orchestrating evidence-driven causal investigations, facilitating parallel analysis of hypotheses, and ensuring comprehensive and accurate problem resolution.

Core Features & Use Cases

  • Evidence-Driven Investigations: Facilitates the investigation of complex problems by generating multiple hypotheses and collecting evidence to support or refute them.
  • Parallel Tracer Workers: Utilizes parallel tracer workers to analyze hypotheses concurrently, improving efficiency and thoroughness.
  • Strength Hierarchy: Ranks evidence by a 6-tier strength hierarchy, ensuring that the most reliable evidence is considered first.
  • Falsification and Rebuttal: Forges a process of falsification and rebuttal to rigorously test hypotheses and arrive at a comprehensive understanding of the problem.
  • Use Case: Ideal for troubleshooting system issues, analyzing performance bottlenecks, or diagnosing errors in complex software systems.

Quick Start

Use the trace skill to investigate the root cause of the observed performance degradation in the 'system-performance-trace.md' document.

Frequently Asked Questions about trace

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I diagnose the root cause of complex software system issues?

Root cause diagnosis of complex software issues is handled by generating multiple hypotheses and collecting evidence to support or refute them. Parallel tracer workers analyze these hypotheses concurrently to ensure comprehensive problem resolution.

What is the best way to investigate performance bottlenecks using evidence-driven analysis?

Evidence-driven analysis for performance bottlenecks utilizes parallel tracer workers to concurrently evaluate hypotheses. It ranks evidence by a 6-tier strength hierarchy and facilitates falsification and rebuttal to rigorously test findings.

How do I troubleshoot system errors when multiple potential causes exist?

Troubleshooting system errors with multiple potential causes is managed by generating multiple hypotheses and analyzing them concurrently with parallel tracer workers. A structured falsification and rebuttal process rigorously tests each hypothesis to isolate the true fault.

Can I use parallel processing to test hypotheses during problem diagnosis?

Parallel processing can be used during problem diagnosis to analyze multiple hypotheses concurrently. This method improves efficiency and thoroughness by rapidly collecting and ranking evidence across parallel tracer workers.

Does this approach require structured data to handle error diagnosis?

This approach requires structured data analysis and robust error handling to effectively manage error diagnosis. It processes structured inputs to orchestrate parallel tracer workers and accurately rank evidence.

When should I not use parallel tracer workers for causal investigations?

Parallel tracer workers for causal investigations should not be used when lacking structured data for analysis or when robust error handling is unavailable. It is designed for complex problem diagnosis rather than simple, single-cause issues.