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Model Debugging

Listed
pollinations·vlatest·testing·MIT

Helps with data modeling, debugging as part of testing, debugging, and quality assurance workflows.

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 pollinations/pollinations --skill model-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

pollinations

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 failed 2 of 6 checks. A failure is not proof of bad intent; it means something in the code needs a human to look at it before you run it.

  • No undeclared network calls: failed

    Contacts 2 hosts that are neither well-known infrastructure nor declared in the skill's frontmatter: api.cloudflare.com, gen.pollinations.ai.

    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.

    Hosts contacted

    api.cloudflare.comundeclaredgen.pollinations.aiundeclared

    What it found (3 of 8)

    • .claude/skills/model-debugging/SKILL.md:306contacts api.cloudflare.com
      curl -s "https://api.cloudflare.com/client/v4/accounts/$ACCOUNT_ID/workers/observability/telemetry/keys" \
    • .claude/skills/model-debugging/SKILL.md:320contacts api.cloudflare.com
      curl -s "https://api.cloudflare.com/client/v4/accounts/$ACCOUNT_ID/workers/observability/telemetry/query" \
    • .claude/skills/model-debugging/SKILL.md:347contacts api.cloudflare.com
      curl -s "https://api.cloudflare.com/client/v4/accounts/$ACCOUNT_ID/workers/observability/telemetry/query" \

  • No credential access: failed

    Touches credential material in 2 places.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

    What it found

    • .claude/skills/model-debugging/SKILL.md:89SSH private keys or config
      ssh -i ~/.ssh/id_rsa_ovh [email protected] "sudo journalctl -u image-pollinations -f"
    • .claude/skills/model-debugging/SKILL.md:92SSH private keys or config
      ssh -i ~/.ssh/id_rsa_ovh [email protected] "sudo journalctl -u image-pollinations --since '3 minutes ago' --no-pager" > legacy-image-logs.txt

  • 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 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 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 pollinations/pollinations at b5bae6b280.

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

Commit
b5bae6b280
Manifest
.claude/skills/model-debugging/SKILL.md
Scanned
6 files, 728 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 perform systematic debugging of models, helping identify errors, logical flaws, or performance issues within generated outputs. It acts as a critical testing mechanism to validate model behavior against expected standards before deployment.

When to use it

  • When an AI agent produces inconsistent results or fails specific logic checks during development.
  • To isolate root causes of hallucinations or factual inaccuracies in complex reasoning tasks.
  • Before finalizing a model for production to ensure reliability and safety compliance.
  • During iterative refinement cycles to verify that code changes or prompt adjustments resolved previous bugs.

Key capabilities

  • Systematic error detection within model outputs.
  • Logical flaw identification in reasoning chains.
  • Performance validation against predefined criteria.
  • Support for iterative testing workflows.

Example prompts

  • "Run a debugging session on this model's response to identify any logical inconsistencies."
  • "Debug the following output to find why the calculation is incorrect."
  • "Test this model's behavior under edge cases and report any failures found."

Tips & gotchas

Ensure you provide clear context about the expected output format so the debugging process has a baseline for comparison. This skill is most effective when used in conjunction with other testing skills to build comprehensive validation suites.