golden-file-conventions
Reference catalog for snapshot / golden file management - naming conventions, directory layout, when to add / update / remove a baseline, sanitization (timestamps, IDs, PII), per-OS / per-runtime variant strategy, and review workflow for snapshot diffs in PRs. Use when designing a snapshot-testing convention or auditing an existing one for drift.
Install with skills.sh (any agent)
npx skills add testland/qa --skill golden-file-conventionsgolden-file-conventions
Terminology note: "golden file" / "golden master" are practitioner-emergent terms popularized by the Working Effectively with Legacy Code tradition. ISTQB has no canonical entry - the closest formal term is "snapshot test." This catalog uses both interchangeably; assume "golden file" and "snapshot" mean the same thing in the rest of the body.
A reference catalog for how to manage snapshot / golden files. It supplies the conventions that active snapshot-management workflows follow when updating / pruning golden files.
When to use
Naming conventions
Per-test snapshot file
Most snapshot frameworks (Jest, Vitest, pytest-snapshot, RSpec Snapshot) use a path adjacent to the test file:
src/
components/
Button.tsx
Button.test.tsx
__snapshots__/
Button.test.tsx.snapConvention: one snapshot file per test file, named <test-file-name>.snap. Do not split snapshots across multiple files per test.
Per-test name within a snapshot file
Inside a .snap file, each snapshot is keyed by <describe> > <it> chain:
exports[`Button renders with primary variant 1`] = `<button class="primary">...</button>`;The trailing 1 is the snapshot index when one test takes multiple snapshots - keep these to a minimum (≤3 per test); beyond that, split the test.
Per-OS / per-browser variants (visual snapshots)
For visual / screenshot-based snapshots, the name carries the platform suffix (per playwright-snapshots, in the qa-visual-regression plugin):
Button-primary-1-chromium-linux.png
Button-primary-1-firefox-linux.png
Button-primary-1-webkit-darwin.pngOS / browser suffixes are load-bearing - anti-aliasing and font metrics differ. Don't strip them.
Directory layout
| Layout | When to use |
|---|---|
Adjacent (__snapshots__/ next to test) | Default. Reviewer sees the diff in the same PR view as the test. |
Centralized (tests/__fixtures__/) | Cross-test fixtures (golden inputs reused by many tests). |
External (s3://snapshots-bucket/) | Visual snapshots that are large; CI uploads / downloads. Common with Percy, Chromatic, Playwright + S3. |
Default to adjacent. Centralized only when fixtures are reused. External only when artifact size makes adjacent impractical.
When to add a baseline
Add a snapshot when:
Don't add a snapshot for:
Sanitization (the load-bearing rule)
A snapshot that contains volatile values (timestamps, UUIDs, random IDs, current dates) breaks every run. Sanitize before snapshotting:
| Volatile field | Sanitization pattern |
|---|---|
| Timestamps | Replace with a fixed string [TIMESTAMP] or freeze the clock (vi.useFakeTimers()). |
| UUIDs | Replace with [UUID] or seed a deterministic generator. |
| Auto-increment IDs | Replace with [ID] or use a sequence-controlled fixture. |
File paths (/var/folders/...) | Replace with [PATH] or normalize via project root. |
| Memory addresses (object refs) | Avoid in serialized output; use a custom serializer. |
| User-data tokens | Strip before snapshotting; tokens shouldn't be in the test surface anyway. |
Most frameworks support custom serializers / matchers - use them. Jest's expect.any(Date) matcher pattern is canonical:
expect(result).toMatchSnapshot({
createdAt: expect.any(Date),
uuid: expect.any(String),
});The serializer normalizes volatile fields before comparison, so the snapshot shows Any<Date> rather than a specific timestamp.
Update vs. fix decision tree
When a snapshot diff appears in a PR:
Is the diff explained by code changes in the same PR?
├── No → REGRESSION; fix the code, do not update the snapshot.
└── Yes → Did the diff align with the intent (described in the PR title)?
├── No → REGRESSION (cascade from an unrelated change); investigate before updating.
└── Yes → Is the diff isolated to the components the PR is supposed to change?
├── No → INVESTIGATE: a CSS / token / shared-component change affected unrelated snapshots.
└── Yes → UPDATE: run `--update-snapshots` and commit.The most common review failure is rubber-stamping snapshot updates - accepting a 47-component diff because the PR title says "Refactor Button". A snapshot-diff classifier can implement this decision tree.
Severity tiering
Every snapshot has an implicit severity:
| Tier | Behavior | Examples |
|---|---|---|
| Critical | Blocks merge on diff; requires explicit reviewer acceptance. | Production-shipped pages; payment flows; auth. |
| Standard | Blocks merge on diff; author can self-approve with a clear PR description. | Internal admin tooling; non-shipping experiments. |
| Advisory | Surfaces diff but doesn't block. | Unstable areas under active redesign; new baselines during ramp-up. |
Promote Advisory → Standard after ~2 weeks of stability. Promote Standard → Critical for security-sensitive surfaces.
Pruning rules
Remove a snapshot when:
The "test deleted but snapshot remained" cleanup can be automated.
Anti-patterns
| Anti-pattern | Why it fails | Fix |
|---|---|---|
| Updating snapshots in a separate "snapshot refresh" PR | Reviewer can't see the code change that justifies the diff. | Always update snapshots in the same PR as the source change. |
--update-snapshots in PR CI as the default | Snapshots become tautologies; never catch a regression. | Update snapshots only in interactive runs; PR CI fails on diff. |
| Snapshotting raw HTML for components | Brittle to attribute-order changes from tooling upgrades. | Snapshot the React / Vue / Svelte component tree (e.g. react-test-renderer), not raw HTML; OR use a normalizer. |
| One mega-snapshot per page | A 5kb diff is uninterpretable; reviewers approve to move on. | Per-component snapshots; smaller surface = faster review. |
| Storing snapshots externally without checksums | A drift in S3 vs. the test code makes "what changed?" hard. | Include checksums in the test code; verify on each run. |
| Snapshots of error messages with stack traces | Stack traces include line numbers that drift with every refactor. | Snapshot the error type + message only; strip the trace. |
| Cross-OS shared snapshots | Anti-aliasing / font / line-ending differences flake the test. | Per-OS snapshot suffixes (see naming above). |
Review workflow
References
Related skills
boundary-value-generator
Generates boundary-value test cases from typed input specifications - for each input field, produces the canonical 6-point set (one below, at, and above the lower bound; one below, at, and above the upper bound) plus equivalence-class representatives. Emits cases as parameterized test inputs (pytest @parametrize / Jest test.each / xUnit InlineData / etc.). Use when a function or endpoint has numeric / string-length / collection-size constraints and the team needs systematic edge-case coverage.
faker-data
Fixes test data that breaks tests - factory values in a shape the code under test rejects (a phone number that is not E.164), fixtures that only pass when the whole suite runs in order, and random values that make an assertion pass or fail depending on the run. Authors test-data factories with Faker: the Python `faker` library, the `@faker-js/faker` JS port, and the `faker-ruby` gem - install per language, the provider catalogue (person / internet / location / date / finance / lorem), locale selection and multi-locale mode, and seed-based determinism for reproducible runs. Scope is generating fresh values for tests that start from nothing, not replacing values inside a dataset that already holds real records - that goes to pii-masking-pipeline-builder. Use when fixtures need realistic values, a stable shape, or a fixed seed.
malicious-payload-bank
Reference catalog of curated adversarial input payloads keyed by attack class - SQL injection, XSS, SSRF, path traversal, command injection, XXE, prototype pollution, regex DoS, Unicode confusables, header injection - plus per-context guidance for which payloads apply (URL parameter / form input / JSON body / file upload). Use when authoring negative-test cases for input validation, fuzz targets, or a security-focused test suite that needs to exercise the OWASP Top 10 attack surface.
msw-handlers
Authors Mock Service Worker (MSW) request handlers for both browser and Node.js test environments using the `http.get` / `http.post` / `HttpResponse.json` API, wires them via `setupWorker` (browser) or `setupServer` (Node), and manages the test lifecycle (`server.listen` / `resetHandlers` / `close`). Use when the project uses JavaScript / TypeScript and needs to mock fetch / XHR at the network layer for both Vitest / Jest unit tests and Cypress / Playwright integration tests.
negative-test-generator
Covers the refusal paths a handler already implements but nothing tests - a batch endpoint that must apply all rows or none, optimistic-concurrency version conflicts between two editors, or a delete that deliberately separates who you are from what you may do from the state the record is in. For each happy-path test, produces companions exercising input validation rejection, missing required fields, type mismatches, authorization failures, rate-limit errors, and adversarial payloads from the malicious-payload-bank, emitted as parameterized tests in the project's runner format. Use when code has deliberate error paths and the suite only proves the success case.
pairwise-test-case-generator
Generates parameterized test inputs combining boundary-value, equivalence-class, and pairwise-combinatorial cases from a typed multi-input specification - produces the cross-product of cases up to a configurable strength (1-wise / 2-wise / N-wise) using all-pairs reduction so the test surface stays tractable. Emits cases in the project's test-runner-native parametrize format. Use when a function or endpoint takes 3+ inputs whose interactions matter and full Cartesian product would explode.
seed-data-curator
Builds a reproducible E2E seed dataset for the project's test environments - picks a representative user / org / data-product cross-section, generates the rows via the project's chosen factory library (FactoryBot / mimesis / Bogus / Faker + factory_boy), persists the dataset as a checked-in fixture (SQL dump / JSON / per-engine seed file), and wires it into the test bootstrap. Use when starting E2E coverage on a project that has no seed strategy, or when an existing seed has drifted.
synthetic-data-toolkit
Umbrella for the synthetic test data generators beyond plain Faker - FactoryBot (Ruby factories with traits, associations, and build / create / build_stubbed strategies), Mimesis (fast type-hinted Python generator with the Schema/Field bulk pattern and 46 locales), and Bogus (.NET typed `Faker<T>` builders with `.RuleFor` / `StrictMode` / `UseSeed`). Picks the right generator by language and job, shows side-by-side equivalents of the same fixture across all four ecosystems, and carries each tool's full workflow in references/ (factory-bot.md, mimesis.md, bogus.md). faker-data stays the default for plain field values in Python / JS / Ruby; use this skill when the project needs typed factory orchestration, .NET fixtures, or a documented "which tool should I use" decision.
synthetic-pii-generator
Generates realistic-but-fake personally identifiable information (PII) - emails, phone numbers, SSNs / national IDs, addresses, names, credit-card numbers (test BIN ranges), date-of-birth - for non-production environments. Wraps Faker / mimesis with PII-aware constraints so generated values match real format expectations (Luhn-valid card numbers, region-valid phone formats, ITIN/SSN format) without ever generating real-person data. Use when seeding test environments, building demo data, or replacing real PII in copied datasets.
test-data-patterns
Pure reference catalog of the cross-language object-construction patterns for test data - Test Data Builder (Pryce/Freeman), Factory (with traits and associations), Object Mother, Fixture composition (per-test / per-describe / shared), Snapshot (defers to `golden-file-conventions` for the operational details), and Production-Data Anonymisation. Distinct from the per-language tool skills (`faker-data` and the `synthetic-data-toolkit` umbrella covering FactoryBot / mimesis / Bogus) which document tool-specific configuration; this catalog is the architecture-tier reference for choosing **which pattern** before reaching for the tool. Use when choosing a test-data construction pattern for a new suite, or auditing an existing suite whose fixtures have drifted into shared mutable state.
wiremock-stubs
Authors WireMock stub mappings for HTTP service mocking - `stubFor` with verb/path/header matchers + `willReturn` response shaping, lifecycle via `WireMockServer` (start / stop) or JUnit `WireMockExtension`, request verification via `verify()`, and dynamic-port allocation for parallel tests. Also carries the Mountebank multi-protocol workflow (TCP / SMTP / LDAP / gRPC imposters, record-playback proxying) in references/mountebank.md. Use when the project is JVM-based and tests need to mock HTTP dependencies (third-party APIs, internal microservices) at the network layer, or when mocking must go beyond HTTP.