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/qa-investigation

@bfc4714

Investigate a specific test failure to its root cause and document the why. Detects whether a failing test is flaky (intermittent) or a deterministic bug during reproduction. Use when a test fails and you need the real cause, not just to make it green. Execution layer, not strategy review. Keywords: flaky test, intermittent failure, debugging tests, root cause analysis, test failure triage, bug hunt, why does this test fail.

Use this Skill: https://skilld.dev/gh/fugazi/test-automation-skills-agents/qa-investigation

This session only. Nothing lands on disk.

SKILL.md

β‰ˆ112 tokens always: the name and description. β‰ˆ1.6k when used: this file. β‰ˆ3.8k more on demand in 3 files.

QA Investigation

A persistent, file-backed investigation journal for a specific failing test. This is the execution layer: it resolves a concrete failure. It does not validate strategy or architecture (grill-me-qa) nor generate QA deliverables (qa-manual-istqb).

The core idea: your context window is volatile RAM; the filesystem is persistent disk. Writing goals, evidence, and decisions to markdown prevents context drift during a long investigation.

When to Use This Skill

  • A test fails intermittently (flaky) or deterministically (bug), and you need the root cause.
  • The investigation spans many tool calls, multiple runs, or more than one session.
  • You want a durable record of what you found, decided, and why.

When NOT to Use This Skill

  • Authoring a test from scratch β€” use the relevant automation/framework skill.
  • Designing a framework or coverage strategy β€” strategy validation (grill-me-qa) or artifact generation (qa-manual-istqb).
  • Simple questions or quick lookups (fewer than ~5 tool calls).
  • General review of non-test production code.

The boundary is not "is it a selector / browser issue / timeout" β€” any of those can be worth investigating. The boundary is whether the request needs a persistent, multi-step root-cause investigation or is a one-shot tactical task. If uncovering the why takes evidence, runs, and iteration, use this skill.

Tool Agnosticism

This method is independent of any test framework β€” web, API, mobile, embedded, unit, load. Terms like "browser", "selector", "network requests", or "CI vs local" are illustrative, not requirements; substitute the equivalent in your stack.

Core Process

The phases are the same whether the failure is flaky or a deterministic bug. The skill discovers the classification during triage β€” it does not assume it up front.

Phase 1: Reproduction & Triage

  • Reproduce reliably; isolate variables (parallelism, repeat count, environment, data/state).
  • Determine: intermittent (flaky), deterministic (bug), or non-reproducible? This is a finding, not an input.
  • Record the classification and the evidence that supports it.
  • Goal: a confirmed reproduction or a documented non-reproducible failure.

Non-reproducible path: if the failure cannot be reproduced after a bounded number of attempts, do not force a label. Record it as non-reproducible with partial evidence, note the suspected nature (infrastructure, app logic, or test-side timing), and escalate or flag for observation. Log the decision and reason to qa_investigation_findings.md. See Flow for detail.

Phase 2: Evidence Collection

  • Capture logs, stack traces, screenshots, traces, retry counts, dependency activity, timings.
  • Multimodal content (images, page/dependency data, PDFs) does not persist in context β€” write it to qa_investigation_findings.md as text immediately.
  • Redact sensitive data (tokens, cookies, credentials, email addresses, PII) before persisting; do not write raw screenshots, traces, logs, or network captures verbatim β€” summarize them in text with sensitive parts masked.
  • Note environment specifics: build/version, platform, device, data conditions, worker count.
  • Goal: enough evidence for a defensible hypothesis.

Phase 3: Hypothesis & Root Cause

  • Form the leading hypothesis (race condition, timing, selector/view issue, app bug, environment, shared state, data flakiness).
  • Test it in a way that can reject it; confirm or reject; record the confirmed cause and the evidence.
  • Goal: a confirmed root cause, not a guess.

Phase 4: Fix & Validation

  • Decide the fix (test-side vs product-side) and, critically, the alternatives you rejected and why.
  • Apply it, then validate stability over repeated runs.
  • Goal: a stable, verified fix with a documented decision.

Phase 5: Prevention

  • Decide how to prevent recurrence: a shared helper, a lint rule, documentation, a regression guard.
  • Record the preventive action(s).
  • Goal: the failure does not come back silently.

File Purposes

Scale the file scope to the investment level (triaged at the start β€” see Flow). Higher value = fuller record; lower value = leaner:

Investment Files in project root How much to write
P1 high-value / blocking All three: plan + findings + progress Full pipeline: goal, phases, decisions, errors, run log
P2 medium plan + findings Phases and the why; progress only if the session runs long
P3 low-value / cosmetic flake findings only Evidence + classification + suspected cause; move on

Each investigation creates the files above in the project root:

File Purpose When to Update
qa_investigation_plan.md Goal, phases, decisions, error log After each phase completes
qa_investigation_findings.md Root cause, evidence, technical decisions After ANY discovery
qa_investigation_progress.md Session log, run/result records Throughout the session

Critical Rules

  1. Create the plan first β€” non-negotiable; the plan is your persistent memory. For a P3 (low-value) case, the findings file is the plan β€” create that first.
  2. 2-Action Rule β€” after every 2 read/search ops, save key findings to qa_investigation_findings.md.
  3. Read before decide β€” re-read the plan before major decisions.
  4. Update after act β€” mark phase status, log errors, note files changed.
  5. Log ALL errors β€” with attempt number and resolution.
  6. Never repeat failures β€” if an action failed, the next must differ.
  7. Classify after reproducing, not before β€” a wrong early label poisons the investigation.

References

  • Flow β€” methodology detail, effort triage, completion criteria, file lifecycle, error protocols, anti-patterns
  • Templates β€” starter templates for the three investigation files
  • Examples β€” flaky, bug, and non-reproducible cases

Source: SKILL.md on GitHub

No alerts1mo3 checks Β· Risk SAFE
  • Gen Agent Trust Hub1mo

    This skill provides a structured methodology for investigating test failures, emphasizing persistence and evidence collection. It includes clear instructions to redact sensitive data like credentials and PII, which is a security best practice. No malicious code or exfiltration patterns were detected.

  • Socket1mo

    No alerts

  • Snyk1mo

    Risk: LOW Β· No issues

Signed by skilld at bfc4714. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub 4 days ago.

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