seed-data
Skill0 starsChanged 6 days agoMITView on GitHub
Use only when user explicitly requests seed/test data generation for specified schema/models. Generates idempotent seed scripts with realistic test data.
3 other projects use a version of this.
Adopted by
Most recently taken up by
- manastalukdar/ai-devstudiotheir own version, first seen Oct 13, 2026
- VoxTechnologies/transmute-frameworktheir own version, first seen Jul 26, 2026
- Jinbae10/skillstheir own version, first seen Jul 7, 2026
Other versions3 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 3
- VoxTechnologies/transmute-framework★ 5same name
- manastalukdar/ai-devstudio★ 1same name
- Jinbae10/skills★ 0same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository174 other files it gives its agents.
- AGENTS.mdAGENTS.md
- CLAUDE.mdCLAUDE.md
- ad-ci-readinessSkill
- ad-pre-pushSkill
- accessibilitySkill
- agent-evalSkill
- agent-harness-constructionSkill
- agentic-engineeringSkill
- agent-introspection-debuggingSkill
- agent-sortSkill
- ai-first-engineeringSkill
- ai-regression-testingSkill
- api-connector-builderSkill
- architecture-decision-recordsSkill
- article-writingSkill
- automation-audit-opsSkill
- autonomous-agent-harnessSkill
- autonomous-loopsSkill
- backend-patternsSkill
- benchmarkSkill
- blueprintSkill
- brand-voiceSkill
- browser-qaSkill
- bun-runtimeSkill
- canary-watchSkill
- cavemanSkill
- ckSkill
- claude-devfleetSkill
- click-path-auditSkill
- codebase-onboardingSkill
- code-tourSkill
- coding-standardsSkill
- content-engineSkill
- content-hash-cache-patternSkill
- context-budgetSkill
- continuous-agent-loopSkill
- continuous-learningSkill
- continuous-learning-v2Skill
- cost-aware-llm-pipelineSkill
- councilSkill
- crosspostSkill
- ad-harnessSkill
- ad-state-migrateSkill
- ad-tui-devSkill
- agent-daemon-memorySkill
- big-feature-flowSkill
- bootstrap-daemonSkill
- bug-flowSkill
- engine-upgradeSkill
- feature-flowSkill
- gepa-evolve-inlineSkill
- harness-troubleshootSkill
- orchestrate-teamSkill
- release-flowSkill
- session-closeSkill
- skill-authorSkill
- skill-installerSkill
- dashboard-builderSkill
- database-migrationsSkill
- deep-researchSkill
- The first 60 shown.
The file
We split this skill into the rules it gives an agent
6 rules. 0 are common, 6 are distinctive, and 0 are very specific to this project.
0
Common
Most of the 3 edited versions we read keep these.
6
Distinctive
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 19
Only invoke this skill when ALL of these are true:
- Distinctive: line 26
NOT for simple CRUD code or component generation
- Distinctive: line 74
Use varied, production-like data
- Distinctive: line 99
Use concise but realistic content
- Distinctive: line 101
Ask clarifying questions FIRST if unclear:
- Distinctive: line 122
Create seed data without schema context
“Common” is measured: kept by at least 6 in 10 of the 3 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Seed Data Generator
Purpose
Generate idempotent database seed scripts with realistic test data.
When to Use (STRICT CRITERIA)
Only invoke this skill when ALL of these are true:
- User explicitly requests seed/test data generation with phrases like:
- "generate seed data"
- "create test data script"
- "populate database with mock data"
- "need sample data for testing"
- User specifies schema/models that need data
- NOT for simple CRUD code or component generation
When NOT to Use
- Regular feature development
- Component/API code generation
- Bug fixes or refactoring
- User asks general "how to" questions
- Unclear requirements
Usage Pattern
User triggers with explicit command: "Generate a seed script for [Product, User, Order] models with 50 realistic entries each"
Output Format
1. Idempotent Script Template (TypeScript)
// scripts/seed-[entity].ts
import { PrismaClient } from '@prisma/client'
// or: import payload from 'payload'
// or: WordPress REST API client
const prisma = new PrismaClient()
async function seed[Entity]() {
console.log('Seeding [entity] data...')
const data = [
// Realistic data array (50-100 items)
]
for (const item of data) {
await prisma.[entity].upsert({
where: { id: item.id }, // or slug/email
create: item,
update: item,
})
}
console.log(`Seeded ${data.length} [entity] records`)
}
seed[Entity]()
.catch(console.error)
.finally(() => prisma.$disconnect())2. Realistic Data Generation
- Use varied, production-like data
- Include edge cases (long strings, special chars, nulls)
- Realistic dates (past year range)
- Proper relationships (foreign keys)
3. Make it Idempotent
Always use:
upsert(Prisma)findOneAndUpdatewithupsert: true(Mongoose)INSERT ... ON CONFLICT DO UPDATE(raw SQL)wp.posts().id(123).update()with existence check (WordPress)
4. Include Run Instructions
// package.json
{
"scripts": {
"seed": "tsx scripts/seed-all.ts",
"seed:products": "tsx scripts/seed-products.ts"
}
}Token Efficiency Rules
- Generate data inline (no external files)
- Use concise but realistic content
- Batch related entities in one script
- Ask clarifying questions FIRST if unclear:
- "How many records per entity?"
- "Which entities need seeding?"
- "What ORM/database?"
Example Invocation
User says: "I need test data for my e-commerce app - products, categories, and users"
Claude asks: "I can generate seed scripts for those. Quick questions:
- How many of each? (default: 50 products, 10 categories, 20 users)
- Are you using Prisma, Payload, or WordPress?
- Any specific requirements (price ranges, user roles, etc.)?"
Then generates: Complete seed scripts after confirmation.
Anti-Patterns (DO NOT DO)
- Generate seed data when user just asks for a component
- Auto-suggest seeding for every database model
- Create seed data without schema context
- Generate when user says "create a Product model" (wait for explicit seed request)
Success Criteria
- User runs script multiple times safely
- Data looks production-ready
- Script handles existing data gracefully
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
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- It worked
- Partly
- It didn't
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