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app-bug-forensics

app-bug-forensics

Use when diagnosing or fixing user-reported application bugs. Guides evidence-backed root-cause analysis from symptoms, screenshots, logs, provider failures, background tasks, storage, or desktop lifecycle behavior through a scoped patch and regression tests.

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name: app-bug-forensics description: Use when diagnosing or fixing user-reported application bugs. Guides evidence-backed root-cause analysis from symptoms, screenshots, logs, provider failures, background tasks, storage, or desktop lifecycle behavior through a scoped patch and regression tests. license: MIT

App Bug Forensics

Use this skill when a user reports a bug, screenshot, intermittent failure, or confusing app behavior. The goal is root cause first, patch second.

Workflow

  1. Preserve the symptom.

    • Identify the exact screen, action sequence, visible message, and affected data scope.
    • Inspect screenshots and logs when available.
    • Do not assume the visible error string names the real failing component.
  2. Trace the path.

    • Follow the request from UI event to state store, API client, backend route, persistence, sidecar/process boundary, and external provider when relevant.
    • Search for all places that can set the visible error state.
    • Check whether the failing request still belongs to the current selection.
    • For AI failures, identify the exact provider, model, transport, profile, credential source, timeout, and whether the request reached the provider.
  3. Classify likely root cause.

    • Stale async request or route watcher race.
    • Frontend/backend version mismatch or old sidecar process.
    • Missing optional capability being treated as fatal.
    • Provider protocol, model, key, base URL, or auth mismatch.
    • Default AI profile being added when the user selected another profile.
    • Long-running provider request blocked behind an unclosable dialog.
    • Provider request timed out locally after remote token usage already began.
    • Background job result, status, reconnect, or cancellation state was lost.
    • Saved credential/profile source disappeared or was overwritten by another profile.
    • Data migration/path/access issue.
    • UI overlay, selection, scrolling, or layout state bug.
    • Result view was hidden or discarded by navigation instead of explicit clearing.
    • Installer or process lifecycle half-update.
  4. Reproduce minimally.

    • Prefer a focused test or local run over broad speculation.
    • If the issue is intermittent, add timing, request tokens, or controlled mocked delays to reproduce the race.
    • If external provider behavior is suspected, separate mock branch tests from one real minimal connectivity test.
    • For charged or slow provider paths, do not auto-retry the full generation while investigating. Probe status and run only minimal real requests.
  5. Fix the cause.

    • Ignore stale results instead of showing stale errors.
    • Translate current real errors into user-facing copy.
    • Keep optional feature failures local to their own UI area.
    • If a request may already be running remotely, reconnect to local job state rather than resending the provider call.
    • Preserve completed AI results until the user explicitly clears or replaces them.
    • Preserve user data; do not "fix" by deleting local state unless the user explicitly asks.
  6. Lock the regression.

    • Add the narrowest meaningful test for the bug shape.
    • Include a manual acceptance path when the bug depends on installer, provider, OS, or real browser behavior.
    • Report what was reproduced, what was fixed, and what remains only manually verified.

Reference Use

Read references/app-quality-principles.md when the bug involves user-visible errors, provider tests, storage, startup, or UI state.

Read references/living-to-tell-casebook.md when the bug resembles raw Not Found, Failed to fetch, provider 403 HTML, sidecar leftovers, stale article/reference selection, startup black windows, data location confusion, or duplicate anchors/excerpts, AI result hiding, provider timeout after token use, profile key isolation, or background job recovery.

Red Lines

  • Do not hide a current valid failure by catching and ignoring every exception.
  • Do not claim the issue is fixed because a mock test passes.
  • Do not use fake data, fake provider success, or broad fallback behavior to make a test pass while the real user workflow remains broken.
  • Do not expose API keys, auth files, private text, or raw provider HTML in the final report.
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