trace

Coordinate competing hypotheses with evidence collection and ranked synthesis.

Updated Feb 4, 2026
One-click install
npx skills add https://github.com/officechbusinessservices-creator/CompliCore --skill trace-officechbusinessservices-creator
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: trace
Source: https://github.com/officechbusinessservices-creator/CompliCore/tree/main/packages/oh-my-claudecode/skills/trace
Command: npx skills add https://github.com/officechbusinessservices-creator/CompliCore --skill trace-officechbusinessservices-creator

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Coordinate evidence-driven tracing to resolve ambiguous results by orchestrating competing hypotheses.

Core Features & Use Cases

  • Orchestrates multiple tracer lanes for competing explanations
  • Enforces tracing discipline: hypotheses, evidence for/against, critical unknowns, discriminating probes
  • Supports Claude built-in team mode coordination with 1+ tracer workers per lane
  • Produces a ranked synthesis and actionable next steps
  • Ideal for debugging, postmortems, and experimental analysis where multiple explanations must be evaluated in parallel

Quick Start

Restate the observation, spawn three tracer lanes, gather evidence for and against each hypothesis, and return a ranked synthesis.

Frequently Asked Questions about trace

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

FAQPage Schema
How do I evaluate competing hypotheses during debugging when results are ambiguous?

Evidence-driven tracing resolves ambiguous debugging results by orchestrating competing hypotheses in parallel. It assigns one tracer per hypothesis lane to gather evidence for, evidence against, and run discriminating probes, producing a ranked synthesis of actionable next steps.

What is the best way to coordinate a postmortem analysis with multiple possible explanations?

Postmortem analysis benefits from evidence-driven tracing by spawning parallel tracer lanes for each explanation. It enforces tracing discipline by structuring evidence collection for and against each hypothesis, ultimately returning a ranked synthesis of the most likely causes.

How does team mode work for running parallel experiments and tracing evidence?

Team mode coordinates parallel experiments by spawning one tracer worker per hypothesis lane. Each worker independently gathers evidence for, evidence against, and executes a discriminating probe before returning results for a final ranked synthesis.

Can I use this approach for experimental analysis where I need to test multiple explanations simultaneously?

Yes, experimental analysis is supported by orchestrating competing explanations across multiple tracer lanes. The system gathers evidence for and against each explanation while running critical unknown probes, yielding a ranked synthesis to guide next steps.

Do I need to manually separate evidence for and against each hypothesis when tracing?

No, evidence-driven tracing enforces this discipline automatically by structuring each lane with explicit evidence-for, evidence-against, and discriminating probe sections. This ensures critical unknowns are addressed before producing the final ranked synthesis.