generating-database-seed-data
tessl i github:jeremylongshore/claude-code-plugins-plus-skills --skill generating-database-seed-dataProcess this skill enables AI assistant to generate realistic test data and database seed scripts for development and testing environments. it uses faker libraries to create realistic data, maintains relational integrity, and allows configurable data volumes. u... Use when working with databases or data models. Trigger with phrases like 'database', 'query', or 'schema'.
Validation
81%| Criteria | Description | Result |
|---|---|---|
allowed_tools_field | 'allowed-tools' contains unusual tool name(s) | Warning |
metadata_version | 'metadata' field is not a dictionary | Warning |
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
Total | 13 / 16 Passed | |
Implementation
13%This skill is a template-style document that describes what a data seeder generator should do rather than providing actionable instructions. It lacks any executable code examples, specific Faker library usage, or concrete SQL/script output. The content is padded with generic boilerplate sections that provide no value to Claude.
Suggestions
Replace abstract descriptions with executable code examples showing actual Faker library usage (e.g., Python with faker, JavaScript with @faker-js/faker) and resulting SQL output
Remove generic boilerplate sections (Prerequisites, Instructions, Output, Error Handling, Resources) that contain no skill-specific information
Add concrete validation steps such as checking foreign key integrity, verifying data type constraints, and testing the generated script before execution
Include a minimal working example with actual table schema input and complete seed script output that can be copy-pasted
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Extremely verbose with extensive padding explaining concepts Claude already knows (what Faker does, what seed scripts are, what foreign keys are). The 'Prerequisites', 'Instructions', 'Output', and 'Error Handling' sections are generic boilerplate that add no value. | 1 / 3 |
Actionability | No executable code, no concrete commands, no actual Faker library usage examples. The 'Examples' section describes what the skill 'will do' abstractly rather than showing actual code or SQL output. Zero copy-paste ready content. | 1 / 3 |
Workflow Clarity | The 'How It Works' section provides a clear 4-step sequence, but lacks any validation checkpoints, error recovery steps, or concrete commands. No feedback loops for verifying generated data integrity or handling constraint violations. | 2 / 3 |
Progressive Disclosure | Monolithic wall of text with no references to external files. Generic sections like 'Resources: Project documentation' provide no actual navigation. Content that could be split (examples, best practices) is all inline with no structure for discovery. | 1 / 3 |
Total | 5 / 12 Passed |
Activation
67%The description provides reasonable coverage of capabilities and includes explicit 'Use when' guidance, but suffers from a truncated description ('u...'), overly generic trigger terms that could conflict with other database skills, and missing natural keywords users would actually say when needing test data generation. The trigger terms focus on general database work rather than the specific test data generation use case.
Suggestions
Add more specific trigger terms that users would naturally say: 'test data', 'seed data', 'mock data', 'sample data', 'faker', 'populate database', 'dummy data'
Fix the truncated description and complete the capability list to improve specificity
Make trigger terms more distinctive to this skill's niche (test data generation) rather than generic database terms to reduce conflict with other database-related skills
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (test data, database seed scripts) and some actions (generate realistic test data, uses faker libraries, maintains relational integrity, configurable data volumes), but the description is truncated ('u...') and doesn't provide a comprehensive list of concrete actions. | 2 / 3 |
Completeness | Explicitly answers both what (generate test data and seed scripts with faker libraries, relational integrity, configurable volumes) and when (Use when working with databases, trigger phrases provided), meeting the criteria for explicit trigger guidance. | 3 / 3 |
Trigger Term Quality | Includes some relevant keywords ('database', 'query', 'schema') but misses natural variations users would say like 'test data', 'seed data', 'faker', 'mock data', 'sample data', or 'populate database'. | 2 / 3 |
Distinctiveness Conflict Risk | The trigger terms 'database', 'query', and 'schema' are quite generic and could easily conflict with other database-related skills (e.g., query optimization, schema design, database administration). The test data generation niche isn't strongly emphasized in the triggers. | 2 / 3 |
Total | 9 / 12 Passed |
Reviewed
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