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Complexity Guardrails

Checked
noartem·vlatest·other

Complexity Guardrails helps manage AI responses by setting limits on length and detail, ensuring concise & focused outputs for better clarity.

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 noartem/skills --skill complexity-guardrails -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

noartem

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 noartem/skills at 0002f35143.

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

Commit
0002f35143
Manifest
skills/complexity-guardrails/SKILL.md
Scanned
1 file, 28 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 noartem-complexity-guardrails skill helps AI agents avoid generating overly complex or verbose responses. It focuses on keeping outputs concise, clear, and directly relevant to the user's query by enforcing length limits and discouraging unnecessary elaboration. This ensures that interactions remain efficient and easy to understand.

When to use it

  • Generating summaries: When you need a brief overview of a lengthy document or article.
  • Creating concise instructions: For providing step-by-step guides where brevity is crucial.
  • Responding to simple questions: To prevent the AI from rambling and delivering only essential information.
  • Chatbots needing efficiency: To ensure quick response times and avoid overwhelming users with excessive text.

Key capabilities

  • Length constraints on generated output.
  • Discourages verbose language.
  • Focuses on direct relevance to user prompts.

Example prompts

  • "Summarize this article in under 100 words."
  • "Give me the steps for baking a cake, briefly."
  • "What is the capital of France? Answer concisely."

Tips & gotchas

The skill's effectiveness depends on clear instructions regarding desired output length. While it aims to reduce complexity, it might occasionally require more context in prompts to achieve optimal results.