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Spectral Locale Debugging

Checked
plurigrid·vlatest·testing·MIT

Pinpoints performance bottlenecks in Plurigrid simulations by analyzing spectral data and identifying localized anomalies.

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 plurigrid/asi --skill spectral-locale-debugging -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

plurigrid

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 plurigrid/asi at 293ce9f76e.

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

Commit
293ce9f76e
Manifest
skills/spectral-locale-debugging/SKILL.md
Scanned
3 files, 93 lines
Copies in repo
2 — all of them scanned
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 spectral-locale-debugging skill provides localized debugging capabilities for AI agents. It allows developers to inspect and modify agent behavior within specific geographic regions or contexts, facilitating targeted testing and refinement of location-aware features. This enables precise control over how an agent responds based on its perceived locale.

When to use it

  • Testing Location-Specific Responses: Verify that an agent provides the correct information (e.g., local business hours, weather) for different cities or countries.
  • Debugging Geofencing Issues: Identify and resolve problems with geofenced actions or triggers within a simulated environment.
  • Validating Localization Logic: Ensure that localized content and language translations are functioning as expected in various regions.
  • Simulating Regional Variations: Test how an agent adapts its behavior based on different cultural norms or regulatory requirements associated with specific locales.

Key capabilities

  • Locale Simulation: Ability to set a virtual locale for the AI agent.
  • Localized Debugging: Inspect and modify agent actions within the simulated locale.
  • Contextual Testing: Test location-dependent features in a controlled environment.

Example prompts

  • "Simulate debugging in London, UK."
  • "Set the locale to Tokyo, Japan, and run this test case."
  • "Inspect the agent's response when it believes it is in Paris, France."

Tips & gotchas

The skill requires a foundational understanding of AI agent architecture and localization principles. Be aware that simulated locales might not perfectly replicate real-world conditions, so validation against live data is still necessary.