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Tldr Code

Checked
parcadei·vlatest·other·MIT

Tldr Code summarizes code snippets into concise, understandable TL;DRs, saving developers time and improving comprehension.

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 parcadei/continuous-claude-v3 --skill tldr-code -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

parcadei

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 parcadei/continuous-claude-v3 at d07ff4b06b.

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

Commit
d07ff4b06b
Manifest
.claude/skills/tldr-code/SKILL.md
Scanned
2 files, 405 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 tldr-code skill provides concise summaries of code snippets. It takes a block of code as input and returns a simplified explanation, making complex logic easier to understand. This is useful for quickly grasping the purpose of unfamiliar code or explaining existing code to others.

When to use it

  • Understanding unfamiliar code: Quickly get the gist of a function or algorithm without diving into every line.
  • Code review assistance: Generate summaries to help reviewers grasp the intent and functionality of code changes.
  • Documentation generation: Create brief explanations for code snippets in documentation.
  • Learning new programming concepts: Use it to clarify how specific code examples work.

Key capabilities

  • Summarizes code snippets
  • Provides simplified explanations
  • Supports various programming languages (implied)

Example prompts

  • "Can you give me a TL;DR of this Python function: def my_function(x): ...?"
  • "Explain what this JavaScript code does in simple terms."
  • "Summarize the purpose of this block of C++ code."

Tips & gotchas

The quality of the summary depends on the complexity and clarity of the original code. Very convoluted or poorly written code may result in less accurate summaries.