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t1mericksont1merickson/skills/synthetic-fixtures/SKILL.md

synthetic-fixtures

Skill1 starsChanged 2 months agoMITView on GitHub

Create deterministic synthetic test media or data when a task needs a durable fixture that exercises a named property without using private user files. Do not use for ordinary mocks or stubs, sample interface copy, generic seed data, anonymizing an existing private dataset, or creating production content rather than test evidence.

The file

We split this skill into the rules it gives an agent

3 rules. About 0 are common, 1 is distinctive, and 2 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈1

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 2

    Very specific

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

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Verify properties programmatically: image mode/profile/EXIF/orientation, audio codec/rate/channels/loudness, video codec/rate/timestamps, …

    line 15
  2. Very specific:

    Use a project fixtures/ directory and descriptive names such as gradient-p3-16bit.png or silence-mono-48k-3s.wav.

    line 19
  3. Distinctive:

    When synthetic media cannot represent the required real-world messiness, ask for approved material or use clearly licensed public material.

    line 28

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “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

Synthetic fixtures

Synthetic is the first choice: it controls the property under test, reproduces exactly, and keeps private material out of commits and screenshots.

Contract

  • Name the property and failure the fixture is meant to exercise before generating it.
  • Seed every stochastic generator and pin material tool versions when output bytes matter.
  • Store the generator beside the fixture or generate during tests. Record the command, seed, dimensions/duration/schema, SHA-256, and expected assertions.
  • Verify properties programmatically: image mode/profile/EXIF/orientation, audio codec/rate/channels/loudness, video codec/rate/timestamps, or data encoding/schema. A file that merely opens is not a valid fixture.
  • Include edge fixtures deliberately: empty, one-unit, maximum practical size, malformed, unusual Unicode, mismatched metadata, and boundary values relevant to the code.
  • Strip accidental metadata and inspect generated files before committing them.

Use a project fixtures/ directory and descriptive names such as gradient-p3-16bit.png or silence-mono-48k-3s.wav.

Recipes

  • Images: Pillow, ImageMagick, or SVG for exact test cards, alpha ramps, color casts, masks, resolution wedges, and seeded noise.
  • Audio: SoX or FFmpeg for tones, sweeps, silence, clipped samples, seeded noise, channel layouts, and sample-rate mismatches.
  • Video: FFmpeg test sources with burned-in frame numbers for sync, rate, color, and timestamp tests.
  • Structured data: small seeded generators covering empty values, normalization forms, emoji, long names, case collisions, and invalid records.

When synthetic media cannot represent the required real-world messiness, ask for approved material or use clearly licensed public material. Store source URL, author, license, retrieval date, and any transformations. Do not assume that visible-on-the-web means reusable.

Pair every durable fixture with a verifier that fails when the property drifts. See file-batch-safety before generating or renaming a large fixture corpus.

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