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Write Example

Official
tldraw·vlatest·backend

Generates clear, concise examples of concepts or instructions based on provided context and desired output format.

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 tldraw/tldraw --skill write-example -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

tldraw

Matched to the vendor's own GitHub organisation.

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 tldraw/tldraw at fdd9b4dee0.

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

Commit
fdd9b4dee0
Manifest
skills/write-example/SKILL.md
Scanned
1 file, 190 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

This skill allows AI agents to generate example text based on a given prompt. It can produce various types of examples, such as code snippets, writing samples, or data structures. The generated output is intended to illustrate concepts and provide practical demonstrations for users.

When to use it

  • Generate sample code in a specific programming language to demonstrate functionality.
  • Create example text for a marketing campaign showcasing different tones of voice.
  • Produce structured data examples (like JSON) to illustrate the format required by an API.
  • Illustrate how to use a particular feature or tool with concrete usage scenarios.

Key capabilities

  • Text generation based on prompts
  • Support for various output formats
  • Ability to generate code snippets
  • Creation of writing samples

Example prompts

  • "Write an example Python function that calculates the factorial of a number."
  • "Generate three examples of marketing copy for a new coffee shop, one each in a playful, professional, and urgent tone."
  • "Show me an example JSON object representing a user profile with fields for name, email, and age."

Tips & gotchas

The quality of the generated examples depends heavily on the clarity and specificity of the prompt. Providing detailed instructions or context will yield more relevant and useful results.