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AkshitIreddyAkshitIreddy/agent-skills/skills/diagnosing-bugs/SKILL.md

diagnosing-bugs

Skill1 starsChanged 2 months agoMITView on GitHub

Diagnose a reported defect or performance regression through reproduction, targeted probes, and causal verification.

193 other projects use a version of this.

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Used with

Projects with it also use setup-ts-deep-modules, loop-me and 10 more.

Other files people use alongside this one.

Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
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The file

We split this skill into the rules it gives an agent

5 rules. 0 are common, 5 are distinctive, and 0 are very specific to this project.

  • 0

    Common

    Most of the 180 edited versions we read keep these.

  • 5

    Distinctive

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Read relevant code and logs to locate the failing path and construct a probe.

    line 16
  2. Distinctive:

    If reproduction is unavailable, state the gap and continue evidence-led source inspection or targeted diagnostics.

    line 26
  3. Distinctive:

    Do not add production instrumentation or access without authorization.

    line 29
  4. Distinctive:

    When fixing is in scope, correct the demonstrated cause.

    line 56
  5. Distinctive:

    Remove task-owned temporary instrumentation and probes when no longer needed.

    line 61

“Common” is measured: kept by at least 6 in 10 of the 180 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Diagnose the reported defect

Adapted from Matt Pocock's diagnosing-bugs skill; see LICENSE.txt.

Establish the actual symptom and a useful feedback loop at the seam where the failure occurs. Prefer an existing test, command, trace, or real interaction that can distinguish the reported bug from nearby failures.

Build evidence without blocking discovery

Read relevant code and logs to locate the failing path and construct a probe. Confirm that the probe can expose the reported symptom before trusting its pass. Reduce irrelevant inputs when that improves diagnosis, without requiring every remaining input to be proven minimal before continuing.

For intermittent failures, record reproduction frequency and conditions. Use bounded repetition, stress, or timing probes suited to the defect. A device or production-only bug may need captured evidence or a longer-running probe; it need not become a deterministic seconds-fast unit test.

If reproduction is unavailable, state the gap and continue evidence-led source inspection or targeted diagnostics. Keep hypotheses tentative. Request only the specific missing artifact, access, or decision that enables further work. Do not add production instrumentation or access without authorization.

Distinguish causes

Rank plausible explanations and define an observable prediction for each. Choose the next probe by how well it separates those explanations. Change one relevant variable at a time when possible; use a debugger, narrow logs, or profiling appropriate to the failure. Performance work needs comparable baseline measurements rather than adjectives.

Communicate findings that affect the user's decisions. A fixed number of hypotheses or a checkpoint before every experiment is unnecessary.

Protect diagnostic data

Inspect and share only the necessary redacted excerpts. Logs, HARs, URLs, dumps, and screenshots can contain credentials and personal data. Keep authentication in the user's normal application flow; never request passwords or tokens in a diagnostic transcript. If human observation is necessary, ask for the relevant action and symptom directly rather than requiring an interactive shell script.

Complete the requested diagnosis or fix

For a diagnosis-only request, return the supported cause or ranked findings, the evidence, and the exact unresolved question. Code changes are outside that deliverable unless the user also requests a fix.

When fixing is in scope, correct the demonstrated cause. Where a durable regression test can exercise the real failure pattern, make it fail before the fix and pass afterward. Otherwise preserve the useful reproduction or explain the coverage gap. Recheck the original scenario and run the affected project checks.

Remove task-owned temporary instrumentation and probes when no longer needed; preserve user work and useful evidence. Report the cause, observed outcome, and remaining uncertainty. A non-reproduction is not proof that a live fault is gone.

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