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testland/synthetic-data-tool-selector

Chooses between the four mainstream synthetic test-data generators - Faker (JavaScript), FactoryBot (Ruby), mimesis (Python), Bogus (.NET) - picks the right tool by language and use case (raw value generation vs. typed factory orchestration), shows side-by-side equivalents for the same fixture across all four, and emits the language-appropriate code. Use when starting test-data work on a project and the team wants the "which tool should I use" decision documented.

75

Quality

94%

Does it follow best practices?

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SecuritybySnyk

Passed

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Overview
Quality
Evals
Security
Files

Quality

Content

85%

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 dispatcher skill with clear decision tables, executable inline code, and clean one-level-deep progressive disclosure. Its only weakness is mild redundancy between the body's Overview/When-to-use sections and the description.

Suggestions

Trim the Overview paragraph and 'When to use' list to avoid restating the description's 'Use when' trigger and tool roster, since the description already covers that.

Drop explanatory asides like 'The pattern is identical across libraries; only the API style differs' and 'you shouldn't be generating synthetic data in prod anyway' — the dispatch tables and code carry the point.

Consider collapsing the 'Related skills' list, which largely duplicates the deferral list already given in 'When to use' and the 'When NOT to use' table.

DimensionReasoningScore

Conciseness

The body is mostly information-dense (dispatch tree, job table, concrete reset calls) and assumes Claude's competence, but the Overview paragraph and 'When to use' list restate content already in the description, and minor asides like 'The pattern is identical across libraries…' could be trimmed.

2 / 3

Actionability

Provides an executable Python/Faker fixture inline and concrete per-language reset calls (e.g. 'faker.seed(42)', 'Faker::Config.random = Random.new(42)', 'new Faker<T>().UseSeed(42)'), with the full four-language executable code delegated to a verified reference file — copy-paste ready.

3 / 3

Workflow Clarity

The decision flow (language? → job? → tool) is clearly sequenced via the ASCII dispatch tree and the dispatch-by-job table, making the single selection action unambiguous; no validation checkpoints are needed for a non-destructive selector.

3 / 3

Progressive Disclosure

Overview and dispatch logic stay inline while the detailed four-language fixture code and reset snippets are pushed to references/language-variants.md (verified to exist, one level deep, no chained references), with well-signaled inline links and a References section.

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 strong, specific description that clearly states what the skill does, when to use it, names the four candidate tools, and frames a distinct dispatcher niche. It is concise, uses third-person voice, and includes an explicit 'Use when' trigger.

DimensionReasoningScore

Specificity

Lists multiple concrete actions — 'Chooses between the four mainstream synthetic test-data generators', 'picks the right tool by language and use case', 'shows side-by-side equivalents', 'emits the language-appropriate code' — matching the anchor for multiple specific concrete actions.

3 / 3

Completeness

Explicitly answers both what ('chooses/picks/shows/emits') and when ('Use when starting test-data work on a project and the team wants the "which tool should I use" decision documented'), with an explicit trigger clause.

3 / 3

Trigger Term Quality

Covers natural terms users would say — 'synthetic test-data generators', 'Faker', 'FactoryBot', 'mimesis', 'Bogus', 'which tool should I use', 'test-data' — with good breadth of common variations.

3 / 3

Distinctiveness Conflict Risk

Framed as a selector/dispatcher for the 'which tool should I use' decision, a distinct niche from the per-tool sibling skills; third-person voice and distinct trigger make conflict with sibling skills unlikely despite naming the four tools.

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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