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Mock Generate

Checked
manastalukdar·vlatest·testing·MIT

Quickly creates realistic mock data sets (text, numbers, dates) based on user prompts for testing and prototyping.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add manastalukdar/ai-devstudio --skill mock-generate -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

manastalukdar

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned manastalukdar/claude-devstudio at e457c1d3b5.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
e457c1d3b5
Manifest
skills/mock-generate/SKILL.md
Scanned
1 file, 995 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

The mock-generate skill allows you to generate mock data for various purposes, including testing and development. It can produce realistic-looking datasets based on specified schemas or patterns. This helps in creating test environments without relying on real production data and speeds up the prototyping process.

When to use it

  • Testing applications: Generate sample user records, product listings, or transaction data for application testing.
  • Populating development databases: Quickly create a database with realistic-looking data for developers to work with.
  • Prototyping UI/UX designs: Populate mock interfaces with placeholder content to visualize and refine the user experience.
  • Creating training datasets: Generate synthetic data for machine learning model training, especially when real data is scarce or sensitive.

Key capabilities

  • Schema-based generation: Creates data based on provided schemas.
  • Pattern definition: Allows users to define custom patterns for data generation.
  • Data type support: Handles various data types like strings, numbers, dates, and booleans.
  • Realistic data simulation: Aims to produce mock data that resembles real-world scenarios.

Example prompts

  • "Generate 10 mock user records with a schema including name, email, and age."
  • "Create a dataset of 5 product listings with random descriptions and prices."
  • "Give me some sample transaction data for the last month, using this format: [date], [amount], [description]."

Tips & gotchas

The quality of generated mock data depends heavily on the accuracy and completeness of the provided schema or patterns. Ensure your schemas are well-defined to avoid generating unrealistic or unusable data.