Name: testland/faker-data
Rating: 73.6 (1 reviews)
Author: testland

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.

Quality

92%

Does it follow best practices?

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Securityby

Passed

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Quality

Content

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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 strong, code-forward reference with executable examples in three languages and useful anti-pattern and limitation tables. It is slightly verbose in its explanatory prose and is a single monolithic file where a couple of sections could be split into references.

Suggestions

Tighten the Overview and 'When to use' bullets to assume Claude's knowledge of Faker and skip justifying why synthetic data beats 'foo'/'bar' literals.

Move the provider catalogue and the composing-factories table into reference files under references/ and link to them, reducing the inline footprint.

Add an explicit end-to-end 'author a deterministic factory' checklist with a verify step (re-run the test with the pinned seed to confirm reproduction) to make the workflow sequence and its feedback loop explicit.

Dimension	Reasoning	Score
Conciseness	Mostly lean with code-forward sections, but the Overview and 'When to use' bullets explain what Faker is and why default 'foo'/'bar' patterns miss bugs — concepts Claude already knows and could be trimmed.	4 / 5
Actionability	Copy-paste ready executable code for Python, JS, and Ruby across install, authoring, seeding, and locale, covering the common cases with concrete provider/module names.	5 / 5
Workflow Clarity	The seeding section gives a clear sequence ('Always seed', place in beforeEach/setup, persist the seed per failing test) with an implicit reproduction feedback loop, but the skill is organized as a topic reference rather than a single end-to-end validated workflow.	4 / 5
Progressive Disclosure	Well-organized into clearly headed sections with one-level-deep pointers to peer skills; no bundle files exist, and some inline content (full provider catalogue, composing-factories table) could live in reference files, but the structure stays navigable.	4 / 5
	Total	17 / 20 Passed

Description

100%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 specific, trigger-rich, and complete, with an explicit 'Use when' clause and explicit boundary guidance to peer skills. It is on the longer side but every clause carries concrete information rather than fluff.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions — 'Fixes test data that breaks tests', 'Authors test-data factories with Faker', install per language, provider catalogue, locale selection, seed-based determinism — giving comprehensive coverage rather than generic verbs.	5 / 5
Completeness	Explicitly answers both: what ('Fixes test data... Authors test-data factories with Faker...') and when ('Use when fixtures need realistic values, a stable shape, or a fixed seed.'), with concrete trigger phrases.	5 / 5
Trigger Term Quality	Covers natural user phrasing with synonyms and concrete tokens — 'test data', 'fixtures', 'factory values', 'Faker', 'realistic values', 'stable shape', 'fixed seed', 'reproducible runs' — the kinds of terms a user would actually say.	5 / 5
Distinctiveness Conflict Risk	Carves a clear niche (test-data factories with Faker) and explicitly redirects overlapping cases to pii-masking-pipeline-builder and synthetic-data-toolkit, minimizing conflict risk.	5 / 5
	Total	20 / 20 Passed

Validation

93%

Warnings & errors only

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

Validation — 15 / 16 Passed

Validation for skill structure

Criteria	Description	Result
referenced_paths_exist	Referenced path issues: 3 missing	Warning

	Total	15 / 16 Passed

Reviewed

20 days ago

Table of Contents

Discovery Implementation Validation