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testland/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.

72

Quality

91%

Does it follow best practices?

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SecuritybySnyk

Passed

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Overview
Quality
Evals
Security
Files

Quality

Content

86%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.

The body is a well-structured routing skill: dense dispatch tables and executable snippets inline, with full per-tool workflows correctly offloaded to verified reference files. It is lean and actionable, with only minor prose that could be tightened.

DimensionReasoningScore

Conciseness

The body is mostly dense, high-value material (dispatch trees, job-to-tool tables, executable code, per-language reset tables), with only minor over-explanation such as "The pattern is identical across libraries; only the API style differs" that could be trimmed.

4 / 5

Actionability

Provides copy-paste ready, executable code for FactoryBot, mimesis, and Bogus, plus concrete per-language seed-reset calls and explicit job-to-tool mappings covering the common cases.

5 / 5

Workflow Clarity

Routing is unambiguous via the dispatch-by-language tree and dispatch-by-job table, with clear when-to-use / when-not-to-use sections; no destructive or batch operation is performed inline so the validation cap does not apply, but there are no explicit multi-step checkpoints either.

4 / 5

Progressive Disclosure

Clear overview pointing to one-level-deep references (factory-bot.md, mimesis.md, bogus.md, language-variants.md — all verified present in references/), with well-signaled inline links and a consolidated References section for easy navigation.

5 / 5

Total

18

/

20

Passed

Description

92%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.

The description is strong across the board: it names concrete actions, includes an explicit use-when trigger, and clearly distinguishes itself from faker-data and sibling skills. Third-person voice is consistent throughout.

DimensionReasoningScore

Specificity

Lists multiple concrete actions — "Picks the right generator by language and job", "shows side-by-side equivalents of the same fixture across all four ecosystems", "carries each tool's full workflow in references/" — with comprehensive coverage of the toolkit's capabilities.

5 / 5

Completeness

Explicitly answers both what (umbrella routing across the four generators) and when ("use this skill when the project needs typed factory orchestration, .NET fixtures, or a documented 'which tool should I use' decision") with concrete trigger phrases.

5 / 5

Trigger Term Quality

Strong natural keyword coverage via the actual tool names (FactoryBot, Mimesis, Bogus, Faker) and phrases like "typed factory orchestration" and ".NET fixtures", but a few natural synonyms (e.g. "test fixtures", "fake data factories") are missing.

4 / 5

Distinctiveness Conflict Risk

Clear niche (typed factory orchestration / .NET / polyglot equivalents) with an explicit carve-out ("faker-data stays the default for plain field values") that minimizes overlap with the sibling default skill.

5 / 5

Total

19

/

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.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Reviewed

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