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Agent skill

Generate Sample Data

by bitovi in bitovi/ai-enablement-prompts

Generate mock/sample data from Zod schemas for testing, development, and mocks.

MITAuto-check passedTesting & QA

Install Generate Sample Data

skills CLI
$ npx skills add bitovi/ai-enablement-prompts --skill generate-sample-data -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install bitovi/ai-enablement-prompts generate-sample-data --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/bitovi/ai-enablement-prompts.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/react-mock/skills/generate-sample-data .claude/skills/generate-sample-data && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
generate-sample-data
GitHub stars
121
Token cost
~916 tokens
SKILL.md length
211 words
Files
1
Skills in repo
40
Repo updated
First seen
Licence
MIT

At a glance

Generate mock/sample data from Zod schemas for testing, development, and mocks.

  • Works in 7 steps: Types derive from Zod schemas → JSON serializable only (ISO 8601… → Consistent naming: create[Entity]Sample… → …
  • Creating sample data generators
  • SKILL.md covers When to Use, Technology Stack, File Structure and Standard Function Signature, plus 8 more sections
  • Calls npm

What it does

Generate Sample Data is an agent skill from bitovi/ai-enablement-prompts. Generate mock/sample data from Zod schemas for testing, development, and mocks. Use when creating sample data generators, setting up test fixtures, populating mock APIs, or generating realistic fake data for development.

Its SKILL.md is about 920 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Testing & QA, covering Test data and fixtures and Forms and validation. It works with Zod. The repository describes itself as: Prompts Bitovi uses for software development. The licence is MIT.

When your agent uses it

  • Creating sample data generators
  • Setting up test fixtures
  • Populating mock APIs
  • Generating realistic fake data for development

Example prompts

  • “/generate-sample-data”

Requirements

  • Node.js

Workflow steps

7 steps, taken from the first numbered list in SKILL.md.

  1. Types derive from Zod schemas
  2. JSON serializable only (ISO 8601 strings, not Date objects)
  3. Consistent naming: create[Entity]Sample / create[Entity]Samples
  4. Seed for determinism
  5. Overrides for flexibility
  6. Export from model index
  7. 15-20+ items for lists/tables

What it can do on your machine

Read from SKILL.md and the folder at commit df229b1. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • npm

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use npm, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Generate Sample Data loads about 916 tokens when it runs. Until then it costs about 60 tokens; SKILL.md has 211 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~60
When it runs · the whole SKILL.md, loaded when a task matches
~916

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from bitovi/ai-enablement-prompts at commit df229b1, republished under its MIT licence (© bitovi). 211 words, ~916 tokens.

Download SKILL.mdSave it as .claude/skills/generate-sample-data/SKILL.md (or your agent's skills folder).
name
generate-sample-data
description
Generate mock/sample data from Zod schemas for testing, development, and mocks. Use when creating sample data generators, setting up test fixtures, populating mock APIs, or generating realistic fake data for development.

Skill: Generate Sample Data from Zod Schemas

Generate type-safe, realistic sample data for testing, development, and mock APIs using Zod schema definitions.

When to Use

  • Creating sample data generators for domain models
  • Populating MSW mock API handlers
  • Generating test fixtures
  • Creating Storybook stories
  • Seeding development databases
  • Building realistic demo data

Technology Stack

PackagePurpose
@anatine/zod-mockGenerates mock data from Zod schemas via generateMock()
@faker-js/fakerProvides realistic fake data
ZodSource of truth for type definitions and validation

File Structure

model/
├── Job.ts              # Zod schema + type
├── Job.sample.ts       # Sample generator ← THIS FILE
├── User.ts / User.sample.ts
└── index.ts            # Re-export samples

Standard Function Signature

typescript
createEntitySample(options?: { seed?: number; overrides?: Partial<Entity> }): Entity

Two Generation Patterns

Pattern 1: Simple Schemas (Primitives Only)

Use generateMock directly:

typescript
import { generateMock } from '@anatine/zod-mock';
import { EntitySchema } from './Entity';

export function createEntitySample(options?: { seed?: number; overrides?: Partial<Entity> }): Entity {
  const mock = generateMock(EntitySchema, { seed: options?.seed });
  return { ...mock, ...options?.overrides };
}
Pattern 2: Composed Schemas (Nested Objects/Arrays)

Compose child samples:

typescript
export function createJobSample(options?: { seed?: number; overrides?: Partial<Job> }): Job {
  const mock = generateMock(JobBaseSchema, {
    seed: options?.seed,
    stringMap: {
      name: () => faker.company.name(),
      email: () => faker.internet.email(),
    },
  });
  return { ...mock, ...options?.overrides };
}

Enhancing with Faker

Use Faker for realistic names, emails, phones. Create seeded Faker instances for determinism:

typescript
import { faker } from '@faker-js/faker';

// Seeded for determinism
if (options?.seed !== undefined) {
  faker.seed(options.seed);
}

Using in Mock APIs (MSW)

Generate 15-20+ items with variety:

typescript
const jobs = Array.from({ length: 20 }, (_, i) =>
  createJobSample({ seed: i, overrides: { status: i % 3 === 0 ? 'COMPLETED' : 'ACTIVE' } })
);

Creating Array Samples

typescript
export function createJobSamples(count: number, baseSeed: number = 0): Job[] {
  return Array.from({ length: count }, (_, i) =>
    createJobSample({ seed: baseSeed + i })
  );
}

CRITICAL: Deterministic Dates with Seeds

NEVER use Date.now() or new Date() when a seed is provided!

typescript
const baseTimestamp = seed !== undefined
  ? new Date(2026, 1, 20, 10, 0, 0).getTime() + (seed * 1000)
  : Date.now();

Testing Sample Generators

Every generator should have tests for:

  • Validity (validates against schema)
  • Determinism (same seed = same output)
  • Overrides work correctly

Key Principles

  1. Types derive from Zod schemas
  2. JSON serializable only (ISO 8601 strings, not Date objects)
  3. Consistent naming: create[Entity]Sample / create[Entity]Samples
  4. Seed for determinism
  5. Overrides for flexibility
  6. Export from model index
  7. 15-20+ items for lists/tables

Installation

bash
npm install -D @anatine/zod-mock @faker-js/faker

© bitovi, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in plugins/react-mock/skills/generate-sample-data of bitovi/ai-enablement-prompts.

Open the folder on GitHubat commit df229b1

Compare with similar skills

Generate Sample Data next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Generate Sample Data compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Generate Sample Data this skillbitovi/ai-enablement-prompts121—~916Automated safety check: PassMIT
Data Strategyidavidov13/agentic-playwright223—~3.4kAutomated safety check: PassMIT
Typescript Idiomsirahardianto/awesome-agv157—~6.1kAutomated safety check: PassMIT
API Testingfugazi/test-automation-skills-agents247—~1.5kAutomated safety check: PassMIT
Common Tasksidavidov13/agentic-playwright223—~2.5kAutomated safety check: PassMIT
Faasjs API Jobsfaasjs/faasjs121—~653Automated safety check: PassMIT

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Works with

Categories

Questions about Generate Sample Data

What does Generate Sample Data do?

Generate mock/sample data from Zod schemas for testing, development, and mocks. Generate Sample Data is an agent skill from bitovi/ai-enablement-prompts. Generate mock/sample data from Zod schemas for testing, development, and mocks.

When should I use Generate Sample Data?

Generate Sample Data fits situations like: creating sample data generators; setting up test fixtures; populating mock APIs; generating realistic fake data for development.

How do I install Generate Sample Data in Claude Code?

Run `npx skills add bitovi/ai-enablement-prompts --skill generate-sample-data -a claude-code`. Or copy the skill folder (plugins/react-mock/skills/generate-sample-data in bitovi/ai-enablement-prompts) into .claude/skills/generate-sample-data in your project. Claude Code loads it when a task matches its description.

How do I install Generate Sample Data in Codex?

Run `npx skills add bitovi/ai-enablement-prompts --skill generate-sample-data -a codex`. Or copy the skill folder (plugins/react-mock/skills/generate-sample-data in bitovi/ai-enablement-prompts) into .agents/skills/generate-sample-data in your project. Codex loads it when a task matches its description.

Can I use Generate Sample Data in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add bitovi/ai-enablement-prompts --skill generate-sample-data -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/generate-sample-data, .gemini/skills/generate-sample-data, .github/skills/generate-sample-data and .opencode/skills/generate-sample-data in your project.

What does Generate Sample Data need to run?

Going by SKILL.md and its folder, Generate Sample Data needs the command-line tools its instructions call (npm). Our summary lists: Node.js.

Does Generate Sample Data access the network?

SKILL.md contains no URLs. Its commands use npm, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Generate Sample Data safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Generate Sample Data use?

Generate Sample Data is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Generate Sample Data use?

About 916 tokens (SKILL.md is roughly 3.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Generate Sample Data?

Skills that share tags, products or a category with Generate Sample Data: Data Strategy (idavidov13/agentic-playwright, 223 stars), Typescript Idioms (irahardianto/awesome-agv, 157 stars), API Testing (fugazi/test-automation-skills-agents, 247 stars) and Common Tasks (idavidov13/agentic-playwright, 223 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Generate Sample Data?

bitovi (a GitHub organization) maintains it in bitovi/ai-enablement-prompts, which has 121 GitHub stars. The repository holds 40 skills in this directory. The repository was last updated on September 11, 2026.

Source: bitovi/ai-enablement-prompts on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.