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testland/seed-data-curator

Builds a reproducible E2E seed dataset for the project's test environments - picks a representative user / org / data-product cross-section, generates the rows via the project's chosen factory library (FactoryBot / mimesis / Bogus / Faker + factory_boy), persists the dataset as a checked-in fixture (SQL dump / JSON / per-engine seed file), and wires it into the test bootstrap. Use when starting E2E coverage on a project that has no seed strategy, or when an existing seed has drifted.

79

Quality

99%

Does it follow best practices?

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SecuritybySnyk

Passed

No findings from the security scan

Overview
Quality
Evals
Security
Files

Quality

Content

100%

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

A well-structured, executable workflow that offloads environment-specific wiring to a verified one-level reference, includes validation with retry loops, and stays lean without overexplaining known concepts.

DimensionReasoningScore

Conciseness

Dense and value-dense with no concept-overexplaining (no 'what is a seed' preamble); every section — coverage matrix, format table, executable script, validation, anti-patterns — earns its tokens, matching the lean score-3 anchor.

3 / 3

Actionability

Provides copy-paste-ready executable code (FactoryBot seed.rb, rake/grep shell commands) with concrete file paths; the deferred Python equivalent is explicitly justified flexibility rather than pseudocode.

3 / 3

Workflow Clarity

Five explicitly numbered, sequenced steps with a validation checkpoint ('assert the row count') and a fix-and-retry loop ('If the count is off, fix... and re-run'), matching the score-3 anchor for clear sequence with feedback loops.

3 / 3

Progressive Disclosure

Core workflow lives inline while verbose CI/ephemeral-env orchestration is split into a real, clearly-signaled one-level-deep reference (references/seed-scripts.md, verified to exist) with no nested indirection.

3 / 3

Total

12

/

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 in third-person voice that covers what the skill does, when to invoke it, and the distinct niche it occupies, with concrete named factory libraries and persistence formats.

DimensionReasoningScore

Specificity

Lists multiple concrete actions — 'picks a representative... cross-section', 'generates the rows via... factory library', 'persists the dataset as a checked-in fixture', and 'wires it into the test bootstrap' — matching the score-3 anchor for multiple specific concrete actions.

3 / 3

Completeness

Explicitly answers both what (builds/persists/wires a reproducible seed dataset) and when via the 'Use when starting E2E coverage... or when an existing seed has drifted' clause.

3 / 3

Trigger Term Quality

Uses natural domain phrasing a testing engineer would actually say ('no seed strategy', 'seed has drifted', 'E2E seed dataset', 'test bootstrap') with good coverage of variations rather than abstract jargon.

3 / 3

Distinctiveness Conflict Risk

Carves a clear niche (reproducible E2E seed datasets via specific factory libraries) with distinct triggers unlikely to fire for general test or data-generation skills.

3 / 3

Total

12

/

12

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: 6 missing

Warning

Total

15

/

16

Passed

Reviewed

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