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Llm Artifacts Detection

Checked
existential-birds·vlatest·ai-ml·Apache-2.0

Provides LLMs guidance and assistance for building AI and machine learning applications.

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 existential-birds/beagle --skill llm-artifacts-detection -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

existential-birds

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 existential-birds/beagle at d1a74899fb.

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

Commit
d1a74899fb
Manifest
plugins/beagle-core/skills/llm-artifacts-detection/SKILL.md
Scanned
5 files, 1,098 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 enables AI agents to identify and extract structured artifacts—such as code blocks, JSON objects, or XML tags—from unstructured text generated by Large Language Models. It ensures that complex outputs are parsed accurately for downstream processing or integration into applications.

When to use it

  • You need to reliably pull specific data formats (like JSON) out of verbose conversational responses.
  • Your workflow requires automating the extraction of code snippets from natural language explanations.
  • You are building a pipeline where an agent must validate that generated content meets strict structural requirements before execution.

Key capabilities

  • Detects and isolates structured data formats embedded within free-form text.
  • Supports identification of various artifact types including code, JSON, and XML.
  • Enhances the reliability of parsing LLM outputs for programmatic use.

Example prompts

  • "Extract the JSON configuration object from the following system log analysis."
  • "Identify any Python code blocks in this explanation of machine learning concepts."
  • "Parse the XML schema definition hidden within this natural language description."

Tips & gotchas

Ensure your input text clearly demarcates artifacts (e.g., using markdown backticks or explicit tags) to maximize detection accuracy. This skill is most effective when combined with validation steps to confirm the extracted structure matches your expected schema before processing.