“debugging”

Stack-specific debugging (Python/async/pdb, logs, stack traces) when root cause is unclear.

MUST USE for any real runtime debugging across ANY language or binary — crashes, silent failures, wrong responses, stuck processes, memory leaks, async…

Systematically debug an error or unexpected behavior.

Systematic debugging workflow for isolating and fixing bugs

Use when the user says 'diagnose', 'debug this', reports something broken/throwing/failing/slow, when encountering a bug/test failure/unexpected behavior and…

Debug issues systematically.

Compatibility route to the author's complete Superpowers systematic-debugging skill for failures and root-cause investigation.

Fast hypothesis-driven debugging.

Systematic debugging workflow.

Structured debugging protocol.

Systematic debugging that identifies root causes rather than treating symptoms.

Systematic debugging framework ensuring root cause investigation before fixes.

Use when Malvex needs debugger-backed runtime evidence such as breakpoints, arguments, registers, threads, heap state, or decrypted configuration.

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

Use when something is failing, regressing, or behaving unexpectedly and the goal is root-cause analysis, narrowest fix, and verification.

Debug issues using BDD-style triage: reproduce the bug in a failing test first, then fix, then verify.

Debug errors systematically.

Use for runtime bugs: crashes, wrong output, intermittent or silent failures, flaky tests, code that works locally but not in prod.

When to use this skill for systematic debugging including root cause analysis, error tracing, performance profiling, and memory leak detection

Systematic debugging workflow using Approved Logs pattern.

Use for any bug, test failure or unexpected behavior, before proposing a fix - reproduce, find the root cause, one hypothesis at a time.

Systematic debugging with diagnostic paths covering data processing, rendering, state machine, asset loading, form/input, and build issues

When there is a bug, error, failed test, regression, performance drop, unexpected result, or anomalous data, establish a tight feedback loop and find the root…

Find the root cause of a defect or a symptom by first building a feedback loop that shows the failure on demand, recording the behaviour that must not change…