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Sentry

Official
openai·vlatest·ai-ml

Automatically detects and flags potential vulnerabilities in AI model code using Trail of Bits' expertise and OpenAI integration.

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 openai/plugins --skill sentry --full-depth -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

openai

Matched to the vendor's own GitHub organisation.

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 1 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 1 host that is neither well-known infrastructure nor declared in the skill's frontmatter: sentry.io.

    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

    sentry.ioundeclared

    What it found

    • plugins/sentry/skills/sentry/scripts/sentry_api.py:12contacts sentry.io
      DEFAULT_BASE_URL = "https://sentry.io"
    • plugins/sentry/skills/sentry/scripts/sentry_api.py:176contacts sentry.io
      help="Sentry base URL (default: https://sentry.io)",

  • 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 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 openai/plugins at 5fd93af4cd.

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

Commit
5fd93af4cd
Manifest
plugins/sentry/skills/sentry/SKILL.md
Scanned
4 files, 380 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 openai-sentry skill integrates OpenAI models with Sentry, a popular error tracking and performance monitoring platform. It allows you to automatically send errors and exceptions encountered by your AI applications directly to Sentry for analysis and debugging. This provides valuable context around model behavior and helps identify the root causes of issues in real-time.

When to use it

  • Debugging AI application errors: Automatically capture and report errors occurring within a chatbot or other AI-powered application.
  • Monitoring LLM performance: Track unexpected outputs or failures in language models, providing insights into model drift or training data problems.
  • Improving AI agent reliability: Proactively identify and address issues impacting the stability and accuracy of your AI agents.
  • Analyzing edge cases: Gain visibility into rare or unusual scenarios that trigger errors within your AI systems.

Key capabilities

  • Sentry integration for error reporting
  • Automatic error capture from OpenAI models
  • Contextual information included in Sentry events (e.g., prompt, response)

Example prompts

  • "Integrate my OpenAI API calls with Sentry."
  • "Configure the skill to send errors from my chatbot to my Sentry project."
  • "Show me how to set up openai-sentry for error tracking."

Tips & gotchas

  • Requires an active Sentry account and API key.
  • Ensure your OpenAI model calls are properly instrumented to provide sufficient context for debugging in Sentry.