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
bogus-data
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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.
e2e-test-narrative-builder
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factory-bot-data
Authors Ruby FactoryBot factories with traits, associations, sequences, and the three build strategies (build / create / build_stubbed); integrates with RSpec / Minitest test suites; pairs with Faker for randomized field values. Use when the project is Ruby / Rails and needs structured fixture creation with referential integrity.
faker-data
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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.
mimesis-data
Authors Python test fixtures using mimesis - a fast, type-hinted, locale-aware test-data generator with 46 locales - covering Person / Address / Internet / Datetime providers and the Schema/Field pattern for typed-dict generation. Pairs with factory_boy when referential integrity is needed. Use when the project is Python and the team values speed, type hints, or strong locale coverage over Faker's larger ecosystem.
mountebank-imposters
Authors Mountebank imposters (multi-protocol mock servers - HTTP, HTTPS, TCP, SMTP, LDAP, gRPC, WebSockets, GraphQL, and more) by POSTing JSON definitions to the Mountebank control API on port 2525, configures stubs with predicates and responses, and uses record-playback proxy mode to capture upstream traffic. Use when the project needs a multi-protocol mock server beyond HTTP-only tools like WireMock or MSW.
msw-handlers
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negative-test-generator
Generates negative / error-path test cases that mirror happy-path tests - 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. Emits cases as parameterized tests in the project's runner format. Use when a feature has happy-path coverage but the rejection / error / unauthorized paths are untested.
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-tool-selector
Chooses between the four mainstream synthetic test-data generators - Faker (JavaScript), FactoryBot (Ruby), mimesis (Python), Bogus (.NET) - picks the right tool by language and use case (raw value generation vs. typed factory orchestration), shows side-by-side equivalents for the same fixture across all four, and emits the language-appropriate code. Use when starting test-data work on a project and the team wants the "which tool should I use" decision documented.
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 per-language data wrappers (`factory-bot-data` Ruby, `faker-data` JS, `mimesis-data` Python, `bogus-data` .NET) 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. Use when the project is JVM-based and tests need to mock HTTP dependencies (third-party APIs, internal microservices) at the network layer.