Skill UI

Automates Sentry monitoring workflows through Composio’s Rube MCP by orchestrating issue investigation, project issue management, alert rule configuration, release tracking, and organizational monitoring.

Master advanced error capture and context enrichment using Sentry SDKs for both TypeScript and Python. This skill teaches how to reliably capture exceptions, track non-exception events (messages), and enrich reports with structured data like user context, tags, breadcrumbs, and custom fingerprints. It is essential for dramatically improving the ability to diagnose complex production issues quickly.

A comprehensive guide to production-ready error tracking using Sentry SDK in TypeScript and Python. Learn how to implement structured error context via scopes, manage user journeys with breadcrumbs, filter out noise and PII using beforeSend/beforeBreadcrumb hooks, and use custom fingerprinting for accurate issue grouping. Essential for robust observability.

This robust tool provides comprehensive tracking and analysis for your cryptocurrency holdings. It calculates real-time portfolio valuations using external APIs (like CoinGecko), analyzes asset allocation, tracks Profit & Loss (P&L), and issues concentration risk alerts. Ideal for users who need to check total portfolio value, view detailed holdings, analyze diversification, or export structured data for tax reporting and record-keeping.

Defines and monitors SLAs, SLIs, and SLOs so teams can set performance targets, track metrics like error budgets and availability, and react before issues impact customers.

Trackio is an essential library for logging, visualizing, and analyzing machine learning training experiments. It provides multi-interface support: a Python API for real-time metric logging and alert firing during training; a Command Line Interface (CLI) for retrieving historical metrics and alerts; and synchronization capabilities to Hugging Face Spaces for persistent dashboards. This tool is crucial for debugging, diagnosing training issues (like loss spikes), and automating ML workflow iteration.

This advanced prompt simulates a Senior Project Manager and DevOps specialist. It takes comprehensive feature artifacts (PRD, technical breakdowns, testing plans) and generates a structured, automated GitHub project plan. It enforces Agile best practices (Epic > Feature > Story > Enabler > Task) and includes templates for issue creation, dependency management, risk assessment, and Definition of Done, ensuring robust project tracking within GitHub.

This skill provides comprehensive capabilities for managing the entire issue lifecycle on GitHub. Users can create structured bug reports, feature requests, and tasks, and also read, list, and update existing issues. It supports setting labels, assigning owners, managing milestones, and tracking complex dependencies. Ideal for engineering teams needing robust project backlog management.

This comprehensive automation toolkit manages core Bitbucket functionalities, including repositories, workspaces, pull requests, and issues. It enables programmatic control over development workflows—from creating and reviewing pull requests to listing repos, tracking issues, and managing workspace members. Essential for integrating CI/CD pipelines and streamlining the entire software development lifecycle.

Bulk migrate repo labels and Project V2 metadata into GitHub org issue fields, converting label-based tags into typed fields, optionally removing old labels, and syncing project field values for cleaner structured issue tracking.

A comprehensive automation toolkit for managing Sentry error tracking and monitoring operations. Users can programmatically investigate issues, configure project-level alert rules, manage release versions, and monitor organizational structure. Essential for DevOps and development teams needing deep integration and programmatic control over error data.

This guide provides a systematic approach to deep debugging complex HubSpot API issues. It includes layer-by-layer network testing (DNS, TCP, TLS), correlation ID tracking, and timing analysis to isolate intermittent or hard-to-reproduce errors. Use this when standard troubleshooting fails or when compiling detailed evidence for technical support.