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    Sentry Error Fetcher

    1

    Fetch unresolved Sentry errors directly into your workflow for faster production debugging.

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    Sentry Error Fetcher

    Sentry Error Fetcher

    Example session with this skill installed

    Check the latest 3 production errors for the "web-frontend" project.

    • Read your context and instructions
    • Compiled the sentry error fetcher

    I have fetched the 3 most recent unresolved errors from the web-frontend project in Sentry.

    Recent Sentry Errors: web-frontend

    IDIssue TypeError MessageEventsUsersLast Seen
    459021TypeError'undefined' is not an object (evaluating 'e.profile.id')1.2k8402 mins ago
    459018RangeErrorMaximum call stack size exceeded45211015 mins ago
    458992NetworkErrorFailed to fetch: https://api.service.com/v1/auth89891 hour ago

    Next steps

    1. Inspect the stack trace for the TypeError as it is affecting the most users.
    2. Verify if the NetworkError is related to the recent API gateway deployment.
    3. Run the script again with a higher limit if you need a broader overview of current stability.

    Connects securely to your tools. The creator never sees your data.

    About this skill

    Context switching between your IDE and the Sentry dashboard slows down debugging. This skill pulls production crash data directly into your terminal or chat, allowing for immediate root-cause analysis.

    What it does

    • Error retrieval pulls the most recent unresolved issues from a specific Sentry project.
    • Issue filtering focuses exclusively on unresolved events to reduce noise from archived or fixed bugs.
    • Customizable depth allows you to specify the number of issues to fetch to match your current debugging scope.
    • Diagnostic context provides the essential metadata needed to understand why a production application is failing.

    How it works

    1. Set the required SENTRY_ORG_SLUG and SENTRY_AUTH_TOKEN environment variables.
    2. Provide the project_slug of the service you want to audit.
    3. Define an optional limit to control the volume of errors returned.
    4. Review the structured output of error types, messages, and frequency.

    Frameworks & tools

    This skill uses Python for the execution script and integrates with the Sentry API. It requires an active Sentry account with event:read or project:read scopes.

    Why this beats prompting it yourself

    Manual prompts often lack the specific API logic needed to authenticate and traverse Sentry's organization and project hierarchies. This skill handles the authentication handshake and data parsing natively, ensuring you get accurate production data without copy-pasting stack traces.

    Use cases

    • Identify the top three most frequent crashes in a backend service.
    • Verify if a specific bug is still occurring in production after a deployment.
    • Get a quick snapshot of application health during an on-call rotation.

    Known limitations

    Requires pre-configured environment variables and an auth token with specific scopes. It only fetches unresolved issues.

    Find more skills like this on the Claude Skills Marketplace, the Codex Skills Marketplace and the Gemini Skills Marketplace, or browse Claude Skills for Testing & Debugging.

    Requires SENTRY_ORG_SLUG and SENTRY_AUTH_TOKEN with read scopes. Only retrieves unresolved issues.

    How to use

    1. 1

      Connect your agent to the Agensi MCP

      Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup

    2. 2

      Sign in to Agensi

      Your agent shows a sign-in link. Sign in and click Allow.

    3. 3

      Ask for what you need

      Your agent loads this skill and uses it on your task. It always asks before buying a skill.

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    Accounts
    Sentry

    Fetches unresolved issues only — it does not resolve, assign, or comment on them. If you want the agent to act on the error (create a Jira ticket, post to Slack), that's a separate skill or a second call. This one gets you the error data.

    Permissions

    Terminal / Shell
    Read Files
    Network Access
    Environment Variables

    Allowed Hosts

    sentry.io
    Listed2 days ago
    Updatedtoday

    What's inside

    Frequently Asked Questions

    This skill eliminates the need to manually toggle between your development environment and the Sentry dashboard by pulling production crash data and error metadata directly into your AI agent's workflow.

    You will need an active Sentry account, your Organization Slug, and an Auth Token with 'event:read' or 'project:read' permissions to authenticate the API requests.

    The skill is designed to be framework-agnostic and works with any AI agent or platform that supports Python-based execution and environment variable configuration.

    To keep your workspace clean and actionable, this skill exclusively fetches 'unresolved' issues, ensuring you only see active production bugs that require immediate attention.

    Yes, you can use the 'limit' parameter to define exactly how many recent errors you want to retrieve, whether you need a single specific crash or a broad overview of recent service health.

    Upon purchase, you receive the production-ready Python script and the configuration schema required to integrate Sentry API functionality directly into your agent's toolkit.

    New in Testing & Debugging