trace

Transform ambiguous software bugs into ranked, testable hypotheses with evidence.

10|Updated Mar 22, 2026
One-click install
npx skills add https://github.com/Lee-SiHyeon/oh-my-copilot --skill trace-lee-sihyeon
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: trace
Source: https://github.com/Lee-SiHyeon/oh-my-copilot/tree/main/.github/skills/trace
Command: npx skills add https://github.com/Lee-SiHyeon/oh-my-copilot --skill trace-lee-sihyeon

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps you diagnose hard-to-reproduce bugs by turning vague symptoms into competing, testable hypotheses backed by concrete evidence.

Core Features & Use Cases

  • Competitive hypothesis debugging: structures the investigation into ranked candidate root causes that can be disproven.
  • Evidence strength hierarchy: prioritizes direct reproduction and primary sources (code/log/measurements) over weaker inference.
  • Parallel exploration workflow: runs distinct investigation lanes in parallel to quickly narrow the best explanation.
  • Safety-first diagnostic contract: enforces a “Trace Contract” (observation, hypotheses, evidence for/against, best explanation, unknowns, next probe) so conclusions stay evidence-grounded.

Use case examples

  • Intermittent production errors: occasional 500s that only happen under certain conditions.
  • Environment-specific failures: passes locally but fails in CI or staging.
  • Performance regressions: latency increases after a deployment with unclear cause.

Quick Start

Run the trace skill with a clear symptom statement like: trace “After login, intermittently 500 errors happen in production only.”

Frequently Asked Questions about trace

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

FAQPage Schema
How do I find the root cause of intermittent production errors?

To find the root cause of intermittent production errors, transform vague symptoms into competing, testable hypotheses using evidence-driven debugging. Rank candidate root causes by rebuttability, then collect direct code and log evidence to validate the best explanation.

What is the best way to debug CI failures that pass locally but fail in staging?

Debugging environment-specific CI failures requires structuring the investigation into parallel exploration lanes. By maintaining a diagnostic Trace Contract, you systematically collect primary source evidence to disprove hypotheses about environment-dependent failures.

How does hypothesis testing work for performance regression analysis?

Hypothesis testing for performance regression works by generating ranked candidate explanations for latency increases after deployment. It prioritizes direct measurements and primary sources over weaker inference to validate the best explanation.

Can I use evidence-driven debugging for hard-to-reproduce bugs without specific dependencies?

Yes, you can apply evidence-driven debugging for hard-to-reproduce bugs without specific dependencies. It uses repository searches and log analysis to collect L1 and L2 evidence, ensuring conclusions stay grounded without requiring external frameworks.

How do I structure a debugging investigation when the root cause is not obvious?

To structure a debugging investigation when the root cause is not obvious, enforce a Trace Contract that tracks observations, hypotheses, evidence for and against, the best explanation, unknowns, and the next targeted probe.