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testland/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.

76

Quality

95%

Does it follow best practices?

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SecuritybySnyk

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Overview
Quality
Evals
Security
Files

Quality

Content

87%

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

A tight, highly actionable authoring reference with executable examples for every usage pattern and a useful anti-patterns table. Its only gap is the absence of explicit validation checkpoints for the bulk-generation workflows it covers.

Suggestions

Add a short validation step to the bulk-generation guidance (e.g. assert row count and key field shape after Schema.create(), or verify to_csv/to_json output before seeding the DB) to lift workflow_clarity.

For the 100k-iteration anti-pattern, show the concrete disk-generation snippet (Schema.to_csv / to_json) inline rather than only naming the methods, so the fix is copy-paste ready.

Consider moving the per-provider method tour into a reference file and keeping SKILL.md focused on the Schema/Field pattern + decision guidance, to deepen progressive disclosure now that the body is ~200 lines.

DimensionReasoningScore

Conciseness

The body is lean — dense executable code blocks plus mimesis-specific prose (e.g. 'Generic aggregates every provider under one instance') that earns its place rather than explaining generic concepts Claude already knows.

3 / 3

Actionability

Provides fully executable, copy-paste-ready code for per-provider usage, Generic, Schema/Field typed-dict generation, locales, seeding, and factory_boy pairing — matching the 'fully executable, copy-paste ready' anchor.

3 / 3

Workflow Clarity

Sections are sequenced (Install → Authoring → Seeding → Pairing) but this is a reference/authoring skill with no explicit validation checkpoints, and the batch-generation operations it describes (e.g. 100k iterations) lack a validate-fix-retry loop, capping it at 2.

2 / 3

Progressive Disclosure

Well-organized into clearly headed sections with one-level-deep references (external mimesis docs/URLs and sibling skill names like faker-data); no nested references, and easy to navigate.

3 / 3

Total

11

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12

Passed

Description

100%

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 precise, third-person description that states concrete capabilities, natural trigger terms, and an explicit use-when clause while distinguishing itself from sibling Faker/factory skills. It matches the top anchor on every dimension.

DimensionReasoningScore

Specificity

Lists multiple concrete actions — 'Authors Python test fixtures', 'covering Person / Address / Internet / Datetime providers and the Schema/Field pattern for typed-dict generation', 'Pairs with factory_boy' — matching the 'multiple specific concrete actions' anchor.

3 / 3

Completeness

Explicitly answers both what ('Authors Python test fixtures using mimesis…') and when ('Use when the project is Python and the team values speed, type hints, or strong locale coverage over Faker's larger ecosystem.'), with an explicit trigger clause.

3 / 3

Trigger Term Quality

Surfaces natural terms a user would say — 'Python test fixtures', 'mimesis', 'factory_boy', 'Faker', 'type hints', 'locale coverage', 'typed-dict' — giving good coverage rather than jargon.

3 / 3

Distinctiveness Conflict Risk

Occupies a clear niche (mimesis specifically) and explicitly contrasts against Faker and factory_boy, making it unlikely to trigger for the wrong skill.

3 / 3

Total

12

/

12

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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