monitoring-database-transactions
About
This skill enables Claude to monitor database transactions for performance issues like long-running queries and lock contention using the `/txn-monitor` command. It triggers when users request transaction monitoring, lock detection, or rollback rate analysis. Use it to get real-time alerts and insights into database health and anomalies.
Documentation
Overview
This skill empowers Claude to proactively monitor database transactions, identify performance bottlenecks like long-running queries and lock contention, and alert on anomalies such as high rollback rates. It provides insights into database health and helps prevent performance degradation.
How It Works
- Activation: The user's request triggers the
database-transaction-monitorplugin. - Transaction Monitoring: The plugin executes the
/txn-monitorcommand to initiate transaction monitoring. - Alerting: The plugin analyzes transaction data and generates alerts based on predefined thresholds for long-running transactions, lock wait times, and rollback rates.
When to Use This Skill
This skill activates when you need to:
- Detect and kill long-running transactions blocking other queries.
- Monitor lock wait times and identify deadlock patterns.
- Track transaction rollback rates for error analysis.
Examples
Example 1: Detecting Long-Running Transactions
User request: "Find any long-running database transactions."
The skill will:
- Activate the
database-transaction-monitorplugin. - Execute the
/txn-monitorcommand to identify transactions exceeding a predefined duration threshold.
Example 2: Analyzing Lock Contention
User request: "Analyze database lock contention."
The skill will:
- Activate the
database-transaction-monitorplugin. - Execute the
/txn-monitorcommand to monitor lock wait times and identify deadlock patterns.
Best Practices
- Threshold Configuration: Configure appropriate thresholds for long-running transactions and lock wait times to minimize false positives.
- Alerting Integration: Integrate transaction alerts with existing monitoring systems for timely notification and response.
- Regular Review: Regularly review transaction monitoring data to identify trends and proactively address potential performance issues.
Integration
This skill can be integrated with other monitoring and alerting tools to provide a comprehensive view of database health. It complements tools for query optimization and database schema design.
Quick Install
/plugin add https://github.com/jeremylongshore/claude-code-plugins-plus/tree/main/database-transaction-monitorCopy and paste this command in Claude Code to install this skill
GitHub 仓库
Related Skills
sglang
MetaSGLang is a high-performance LLM serving framework that specializes in fast, structured generation for JSON, regex, and agentic workflows using its RadixAttention prefix caching. It delivers significantly faster inference, especially for tasks with repeated prefixes, making it ideal for complex, structured outputs and multi-turn conversations. Choose SGLang over alternatives like vLLM when you need constrained decoding or are building applications with extensive prefix sharing.
evaluating-llms-harness
TestingThis Claude Skill runs the lm-evaluation-harness to benchmark LLMs across 60+ standardized academic tasks like MMLU and GSM8K. It's designed for developers to compare model quality, track training progress, or report academic results. The tool supports various backends including HuggingFace and vLLM models.
llamaguard
OtherLlamaGuard is Meta's 7-8B parameter model for moderating LLM inputs and outputs across six safety categories like violence and hate speech. It offers 94-95% accuracy and can be deployed using vLLM, Hugging Face, or Amazon SageMaker. Use this skill to easily integrate content filtering and safety guardrails into your AI applications.
langchain
MetaLangChain is a framework for building LLM applications using agents, chains, and RAG pipelines. It supports multiple LLM providers, offers 500+ integrations, and includes features like tool calling and memory management. Use it for rapid prototyping and deploying production systems like chatbots, autonomous agents, and question-answering services.
