Optimize Sentry performance monitoring configuration. Use when tuning sample rates, reducing overhead, or improving performance data quality. Trigger with phrases like "sentry performance optimize", "tune sentry tracing", "sentry overhead", "improve sentry performance".
Installation
Details
Usage
After installing, this skill will be available to your AI coding assistant.
Verify installation:
skills listSkill Instructions
name: sentry-performance-tuning description: | Optimize Sentry performance monitoring configuration. Use when tuning sample rates, reducing overhead, or improving performance data quality. Trigger with phrases like "sentry performance optimize", "tune sentry tracing", "sentry overhead", "improve sentry performance". allowed-tools: Read, Write, Edit, Grep version: 1.0.0 license: MIT author: Jeremy Longshore jeremy@intentsolutions.io
Sentry Performance Tuning
Prerequisites
- Performance monitoring enabled
- Transaction volume metrics available
- Critical paths identified
- Performance baseline established
Instructions
- Implement dynamic sampling with tracesSampler for endpoint-specific rates
- Configure environment-based sample rates (higher in dev, lower in prod)
- Remove unused integrations to reduce SDK overhead
- Limit breadcrumbs to reduce memory usage
- Use parameterized transaction names to avoid cardinality explosion
- Create spans only for meaningful slow operations
- Configure profile sampling sparingly for performance-critical endpoints
- Measure SDK initialization time and ongoing overhead
- Implement high-volume optimization with aggressive filtering
- Monitor SDK performance metrics and adjust configuration
Output
- Optimized sample rates configured
- SDK overhead minimized
- Transaction naming standardized
- Resource usage reduced
Error Handling
See {baseDir}/references/errors.md for comprehensive error handling.
Examples
See {baseDir}/references/examples.md for detailed examples.
Resources
More by jeremylongshore
View allRabbitmq Queue Setup - Auto-activating skill for Backend Development. Triggers on: rabbitmq queue setup, rabbitmq queue setup Part of the Backend Development skill category.
evaluating-machine-learning-models: This skill allows Claude to evaluate machine learning models using a comprehensive suite of metrics. It should be used when the user requests model performance analysis, validation, or testing. Claude can use this skill to assess model accuracy, precision, recall, F1-score, and other relevant metrics. Trigger this skill when the user mentions "evaluate model", "model performance", "testing metrics", "validation results", or requests a comprehensive "model evaluation".
building-neural-networks: This skill allows Claude to construct and configure neural network architectures using the neural-network-builder plugin. It should be used when the user requests the creation of a new neural network, modification of an existing one, or assistance with defining the layers, parameters, and training process. The skill is triggered by requests involving terms like "build a neural network," "define network architecture," "configure layers," or specific mentions of neural network types (e.g., "CNN," "RNN," "transformer").
Oauth Callback Handler - Auto-activating skill for API Integration. Triggers on: oauth callback handler, oauth callback handler Part of the API Integration skill category.
