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testland/faker-data

Authors test-data factories using Faker: the Python `faker` library, the `@faker-js/faker` JS port, and the `faker-ruby` gem. Owns the library mechanics end to end: install per language, the provider catalogue (person / internet / location / date / finance / lorem), locale selection and multi-locale mode, and seed-based determinism for reproducible runs. Scope is generating fresh values for tests that start from nothing, not replacing values inside an existing dataset that already holds real records - a production dump in staging goes to pii-masking-pipeline-builder in qa-test-data-privacy (its faker-masking-operators reference), which owns referential integrity and re-identification. Prefer this skill when the codebase already uses the Faker family or when cross-language consistency across Python, JS, and Ruby matters; use synthetic-data-toolkit's mimesis reference only when deeper Python locale coverage is the priority. Use when authoring fixtures or factories that need realistic-looking field values.

89

Quality

89%

Does it follow best practices?

Impact

Average score across 10 eval scenarios

SecuritybySnyk

Passed

No findings from the security scan

Overview
Quality
Evals
Security
Files

Quality

Content

82%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, highly actionable reference with executable examples across three languages and useful anti-patterns. Mostly lean, though repeated citation lines and a slightly explanatory Overview could be trimmed.

Suggestions

Remove or consolidate the repeated '(Per [faker-py][py].)' citation lines to reduce token overhead; the link table already establishes the references.

Tighten the Overview to drop the general explanation of what Faker is, since Claude already knows this; keep only the scoped library table.

Add a short validation note in the Seeding section (e.g., assert reproducibility by re-running with the same seed and comparing output) to give an explicit checkpoint.

DimensionReasoningScore

Conciseness

Mostly tight code blocks and tables, but the repeated '(Per [faker-py][py].)' citation lines and a lightly explanatory Overview add a few unnecessary tokens.

4 / 5

Actionability

Provides executable, copy-paste-ready install commands and code examples across Python, JS/TS, and Ruby covering the common cases of generation, seeding, and locale selection.

5 / 5

Workflow Clarity

Sections are clearly sequenced (Install → Authoring → Seeding → Locale → Composing) with concrete seeding guidance, though no explicit validation/feedback checkpoints are present.

4 / 5

Progressive Disclosure

Well-organized single-file overview with clear sections and external doc links, but with no bundle files present all content is inline rather than split across one-level-deep references.

4 / 5

Total

17

/

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.

A strong, specific description with concrete capabilities, an explicit 'Use when' trigger, and explicit boundary routing to sibling skills. Minor room to add natural synonyms and file extensions in the trigger phrasing.

DimensionReasoningScore

Specificity

Lists multiple concrete actions—install per language, the provider catalogue (person/internet/location/date/finance/lorem), locale and multi-locale mode, and seed-based determinism—giving comprehensive coverage of Faker mechanics.

5 / 5

Completeness

Explicitly states both what ('Authors test-data factories using Faker...') and when ('Use when authoring fixtures or factories that need realistic-looking field values.') with concrete trigger phrases.

5 / 5

Trigger Term Quality

Includes natural terms like 'fixtures', 'factories', 'test data', and 'Faker', but lacks file-extension synonyms and a few common natural phrasings.

4 / 5

Distinctiveness Conflict Risk

Defines a clear niche and actively reduces conflict risk by routing production-dump staging to pii-masking-pipeline-builder and locale-rich Python needs to synthetic-data-toolkit.

5 / 5

Total

19

/

20

Passed

Validation

93%

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

Validation15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

referenced_paths_exist

Referenced path issues: 3 missing

Warning

Total

15

/

16

Passed

Reviewed

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