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Skill

/debugging

Use when a bug's cause is unknown. Applies a hypothesis-driven method — reproduce, isolate, instrument, prove — instead of speculative edits, and covers profiler, debugger, and log-based investigation. Trigger on "no idea why this is failing", "works locally but not in prod",

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$ npx -y skills add nimadorostkar/Claude-Skills-collection --skill debugging --agent claude-code

SKILL.md

debugging.SKILL.md
name: debugging
description: Use when a bug's cause is unknown. Applies a hypothesis-driven method — reproduce, isolate, instrument, prove — instead of speculative edits, and covers profiler, debugger, and log-based investigation. Trigger on "no idea why this is failing", "works locally but not in prod", "this test fails intermittently", "it got slower and i don't know why". Do not use once the cause is known and needs a tested fix — see the bug-fix-protocol skill.
metadata:
  category: development
  version: 1.0.0
  tags: [debugging, root-cause, diagnostics, profiling]

Debugging

Purpose

Find the actual cause of a defect, not a change that makes the symptom disappear. Debugging is a search problem, and the method is binary search over hypotheses.

When to Use

  • A test fails and the reason is not obvious from the assertion.
  • Production behavior differs from local behavior.
  • An intermittent or timing-dependent failure.
  • A performance regression with no obvious cause.
  • A bug that "came back" after being fixed.

Capabilities

  • Deterministic reproduction, including of flaky failures.
  • Bisection over code history, input space, and configuration.
  • Instrumentation: strategic logging, breakpoints, tracing, core dumps.
  • Concurrency debugging: race detectors, deadlock analysis, lock ordering.
  • Performance debugging: CPU and allocation profiles, flame graphs.

Inputs

  • The failure: error message, stack trace, failing test, or observed behavior.
  • The last known-good state, if any.
  • Environment differences between where it fails and where it does not.

Outputs

  • A reproduction that fails reliably.
  • A stated root cause, with the evidence that proves it.
  • A fix, plus a regression test that fails without the fix.

Workflow

1. **Reproduce** — Reduce to the smallest input and shortest path that still fails. If you cannot reproduce it, you cannot verify a fix. For flaky failures, run in a loop until you have a failure rate. 2. **Read the evidence** — Read the entire stack trace, including the parts you have seen before. Read the actual error, not the one you assume it is. 3. **Form one hypothesis** — State it as a falsifiable claim: "the cache returns a stale value because the invalidation runs before the write commits." 4. **Test the hypothesis** — Add instrumentation that would distinguish true from false. Do not change behavior yet. 5. **Bisect** — If no hypothesis survives, bisect. `git bisect` over commits; comment out halves of the input; disable half the config. 6. **Prove the cause** — You have the cause when you can turn the bug on and off at will. 7. **Fix and regress** — Write the failing test first, then fix, then confirm the test passes and the rest still do.

Best Practices

  • Never change two things at once. You will not know which one mattered.
  • "It works now" without an explanation means the bug is still there.
  • Trust the machine over your memory of what the code does — read the code that is running, on the branch that is deployed.
  • If the bug is in a dependency, prove it with a minimal script before reporting or working around it.
  • Timing-dependent bugs are nearly always missing synchronization or an assumed ordering. Look for shared mutable state first.
  • Delete instrumentation you added, or promote it to permanent, structured logging. Do not leave debug prints behind.

Examples

**Hypothesis log for an intermittent failure:**

Symptom : /checkout returns 500 approximately 1 in 40 requests under load.
Evidence: Stack trace shows NullPointerException in CartCache.get(); no error at low load.

H1: Cache eviction races with read.
    Test: log cache size + key on every get/put; run 500 concurrent requests.
    Result: FALSE — evictions never coincide with the failures.

H2: Cart is written by request thread but read by an async pricing task
    before the write commits.
    Test: log thread id and transaction id at write and at read.
    Result: TRUE — pricing task reads on a different connection, 3-8ms before commit.

Cause: Async task enqueued inside the transaction, executed outside it.
Fix  : Enqueue on transaction commit (after-commit hook).
Test : Regression test asserting the task is not enqueued until commit.

Notes

  • `git bisect run <script>` automates bisection completely when you have a scripted reproduction. It is the single highest-leverage debugging tool most engineers underuse.
  • Heisenbugs that vanish under a debugger are usually timing or optimization-related. Reach for logging and race detectors instead of breakpoints.
  • A bug that reappears was never fixed — the original fix addressed a symptom. Reopen the investigation rather than patching again.
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@nimadorostkar
Ships withclaude-skills-collection

A curated library of 137 production-grade skills for Claude and other AI coding agents. Every skill follows one structure, speaks with one voice, and earns its place by changing what the agent does.

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Questions about debugging.

Which coding agents can use debugging?
debugging installs in Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot, OpenCode and Windsurf with the skills CLI, using each agent's flag shown on this page. Flowy's auto-invocation, which fires a skill when your prompt matches, runs in Claude Code only.