Trace observed outcomes by comparing competing hypotheses with ranked evidence.
npx skills add https://github.com/marcmunoz-uno/jailbreak --skill trace-marcmunoz-uno
Please help me install this Agent Skill. Skill: trace Source: https://github.com/marcmunoz-uno/jailbreak/tree/main/skills/trace Command: npx skills add https://github.com/marcmunoz-uno/jailbreak --skill trace-marcmunoz-uno
It helps you understand why an observed outcome happened by turning ambiguous “why” questions into an evidence-driven comparison of competing explanations.
Use /oh-my-claudecode:trace with a description of the observed behavior and what you want explained, then ask it to produce ranked hypotheses with the critical unknown and the next probe to collapse uncertainty.
High-intent search queries and answers about installing and using this skill.
Root cause analysis for runtime bugs uses evidence-driven tracing to compare competing hypotheses with ranked evidence. This method separates the observed behavior from explanatory guesses, actively falsifies the leading hypothesis, and recommends a discriminating next probe to collapse uncertainty.
Debugging a performance regression is best handled by performance forensics that turn ambiguous latency behavior into parallel hypothesis lanes. These lanes gather distinct evidence streams for and against each explanation, run a rebuttal round, and output a single leading explanation with explicit uncertainty tracking.
Team mode orchestration for root cause analysis deploys parallel tracer workers to gather distinct evidence streams for competing hypotheses. This structure prevents converging on a single guess too early by running a rebuttal and convergence step before recommending the most discriminating next probe.
Hypothesis testing for system orchestration is needed when you observe ambiguous routing or configuration behavior and must explain why it happened. It applies falsification-driven rigor to actively disconfirm your top explanation, track evidence gaps, and identify the critical unknowns driving the observed result.
Evidence-driven tracing explains experimental findings and config routing behavior by comparing competing hypotheses with ranked evidence. It requires Claude built-in team mode orchestration to separate observations from explanations, track uncertainty explicitly, and recommend a discriminating next probe.
Falsification-driven tracing for debugging requires explicit uncertainty tracking and a structured rebuttal round, meaning it demands Claude built-in team mode orchestration. Without parallel tracer lanes to gather distinct evidence, you risk converging on the same guess too early without collapsing the critical unknown.
INNERJOINTTrace causal observations to rank competing explanations for system behavior.
EvolutionAPIGenerate ranked hypotheses with evidence for ambiguous engineering observations.
Leap0920Rank competing causal hypotheses using evidence and falsification for ambiguous investigations.
CarbeneAIGuide root-cause debugging through investigation, analysis, hypothesis, and implementation phases.
