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testland/synthetic-data-toolkit

Use whenever someone asks for test factories or a FactoryBot setup, even a quick one: factories with traits and associations (Ruby, Rails), Mimesis bulk data (Python), Bogus Faker<T> builders (.NET), or which generator fits. Same fixture in each tool; plain fake values stay with faker-data.

Quality

86%

Does it follow best practices?

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Overview
Quality
Evals
Security
Files

Quality

Content

78%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A well-structured router skill: highly actionable code and dispatch guidance, exemplary progressive disclosure with verified one-level-deep references, and a clear routing workflow. The main weakness is token efficiency — conceptual overview prose and duplicated inline examples could be trimmed since the references already carry the detail.

Suggestions

Tighten the Overview to drop the conceptual explanation of what synthetic-data generation is ("produce realistic field values, optionally compose them into typed object graphs") and keep only the routing intent.

Trim the per-tool code snippets in 'Per-tool workflow overview' to one line each (or drop them), since each reference page already opens with a full working example — this removes the duplication flagged in conciseness.

Say 'the pattern is identical across libraries' once (in the side-by-side section) instead of twice, and let references/language-variants.md carry the restatement.

DimensionReasoningScore

Conciseness

Mostly efficient (dispatch tree, tables, and seeding conventions all earn their tokens), but the Overview explains background concepts Claude already knows, the line "the pattern is identical across libraries; only the API style differs" is stated twice, and the per-tool overview code snippets duplicate content carried in the reference files.

3 / 5

Actionability

Guidance is fully executable: copy-paste-ready Ruby, Python, and C# snippets, a concrete per-language seed-reset table, a decision tree with specific tool destinations, and a dispatch-by-job table. Not 4: the examples are complete and cover the common cases across all four ecosystems.

5 / 5

Workflow Clarity

The dispatch-by-language tree gives a clear, unambiguous routing sequence and the reset table gives concrete follow-through, but no explicit ordering ties the two dispatch mechanisms together, and the 'dispatch by job' table can diverge slightly from the language tree (e.g., bulk generation lists mimesis or Bogus while the tree routes .NET solely to bogus.md).

4 / 5

Progressive Disclosure

The body is a genuine overview with at-a-glance snippets, and each tool's full workflow (install, authoring, seeding, anti-patterns, limitations) lives in a clearly labeled, one-level-deep reference file — all four referenced paths exist and none reference further files. Structure matches the exemplary anchor.

5 / 5

Total

17

/

20

Passed

Description

87%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A strong description: it names concrete capabilities per language, gives an explicit 'Use whenever...' trigger clause, and cleanly fences off the sibling faker-data skill. Keyword coverage is good but misses a few natural synonyms like 'fixtures' and 'fake data'.

DimensionReasoningScore

Specificity

The description lists several concrete, tool-specific capabilities ("factories with traits and associations (Ruby, Rails)", "Mimesis bulk data (Python)", "Bogus Faker<T> builders (.NET)", "which generator fits") but frames them as coverage areas rather than a comprehensive set of actions, leaving minor gaps.

4 / 5

Completeness

It explicitly answers both questions: what ("factories with traits and associations... Mimesis bulk data... Bogus Faker<T> builders") and when ("Use whenever someone asks for test factories or a FactoryBot setup, even a quick one"), with concrete trigger phrases plus an explicit boundary ("plain fake values stay with faker-data").

5 / 5

Trigger Term Quality

Natural user phrasings like "test factories", "FactoryBot setup", "Mimesis", and "Bogus" are present, but common synonyms a user might say — "fixtures", "fake data", "test data" — are missing, which falls short of comprehensive synonym coverage.

4 / 5

Distinctiveness Conflict Risk

It carves out a clear niche — typed factory orchestration and named canonical libraries — and explicitly disambiguates the closest sibling skill ("plain fake values stay with faker-data"), minimizing conflict risk.

5 / 5

Total

18

/

20

Passed

Validation

100%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

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