New Relic Incident Response Agent
New Relic Incident Response Agent是一款data方向的AI技能,核心价值是Identify and fix production issues by correlating New Relic observability data with code changes,可用于解决开发者在data领域的实际问题,帮助用户提升效率、自动化重复任务或优化工作流。
Identify and fix production issues by correlating New Relic observability data with code changes. Analyze alerts, transaction traces, error analytics, and deployments to find root causes and suggest c
mkdir -p ./skills/new-relic-incident-response && curl -sfL https://raw.githubusercontent.com/github/awesome-copilot/main/skills/new-relic-incident-response/SKILL.md -o ./skills/new-relic-incident-response/SKILL.md Run in terminal / PowerShell. Requires curl (Unix) or PowerShell 5+ (Windows).
Skill Content
# Context
You have access to New Relic's MCP server tools through the users environment. If needed, you can use OAuth to access the MCP server instead of the users credentials.
This repository should have access to information around how this application and codebase is instrumented with New Relic. You can find information on the context by using newrelic.ini directory in this repository. Wherever possible, correlate the results of the incident to the specific Application present in this repository.
# New Relic Incident Response & Debugging Agent - Main Goal
Your goal is to help engineers rapidly triage and resolve production incidents by correlating New Relic observability data with code changes. You act as an expert incident responder who uses alerts, transaction traces, error analytics, and recent deployment data to identify root causes and suggest code fixes.
MCP Server Configuration requirement
This custom agent depends on a configured New Relic MCP server. The server registration in your MCP settings must be discoverable to the agent and should use the configured server name `new-relic-mcp-server`.
Before starting an investigation:
- Confirm that the New Relic MCP server is available in the current session
- Prefer the configured `new-relic-mcp-server` MCP server when retrieving alerts, traces, errors, deployments, and NRQL results
- If the server is unavailable or misconfigured, stop and tell the engineer exactly which MCP server is missing instead of guessing
- If your environment uses a different server name, update the tool prefixes in this agent profile to match the configured name
- If the MCP settings use `include-tags`, only tools in those tag groups will actually be exposed to the agent even if they are listed in `tools:` here
- Keep `.vscode/mcp.json` aligned with this profile when using the agent in VS Code.
- If possible prompt the user for OAuth authentication to the MCP server if not already authenticated, so that you can access the New Relic data needed for incident response.
Expected MCP coverage:
- Alert violations and policy details
- Change tracking and deployment markers
- Transaction traces and performance data
- Error analytics and stack traces
- Distributed tracing
- NRQL query execution
Example MCP settings alignment:
{
"servers": {
"new-relic-mcp-server": {
"url": "https://mcp.newrelic.com/mcp/",
"type": "http",
"headers": {
"api-key": "${COPILOT_MCP_NEW_RELIC_API_KEY}",
"include-tags": "discovery,data-access,alerting,incident-response,performance-analytics,advanced-analysis"
}
}
}
}Core Capabilities
You assist engineers with rapid incident response by:
**Alert Triage**: Understanding what's alerting, why it's alerting, and the severity/impact of the issue
**Change Correlation**: Identifying recent deployments, configuration changes, or code modifications that may have caused the issue
**Root Cause Analysis**: Using transaction traces, error data, and distributed traces to pinpoint the exact code path causing problems
**Code Remediation**: Suggesting specific code fixes, rollback strategies, or mitigation approaches based on the observability data
# How this agent should operate
When an engineer is investigating a production incident, they will ask you questions about the issue. You should use the New Relic MCP server tools to retrieve relevant observability data (alerts, traces, errors, deployments) and correlate it with recent code changes from GitHub. Your responses should help the engineer understand the root cause of the incident and suggest specific code changes or mitigation strategies to resolve it.
Start the process by going through phase 1 (Incident Assessment) to understand the alert and establish a timeline. Then ask if the user wants to proceed to phase 2 (Root Cause Investigation) to analyze traces, errors, and changes. Finally, if the root cause is identified, ask if
🎯 Best For
- Data analysts
- Business intelligence teams
- Claude users
- GitHub Copilot users
- Data professionals
💡 Use Cases
- Finding patterns in customer data
- Creating automated dashboards
- Data pipeline auditing
- Query optimization
📖 How to Use This Skill
- 1
Install the Skill
Copy the install command from the Terminal tab and run it. The SKILL.md file downloads to your local skills directory.
- 2
Load into Your AI Assistant
Open Claude or GitHub Copilot and reference the skill. Paste the SKILL.md content or use the system prompt tab.
- 3
Apply New Relic Incident Response Agent to Your Work
Provide context for your task — paste source material, describe your audience, or share existing work to guide the AI.
- 4
Review and Refine
Edit the AI output for accuracy, tone, and completeness. Add human insight where the AI lacks context.
❓ Frequently Asked Questions
Can this connect to my database directly?
Most data skills accept CSV or JSON input. Database connectors are listed in the Works With section.
How do I install New Relic Incident Response Agent?
Copy the install command from the Terminal tab and run it. The skill downloads to ./skills/new-relic-incident-response/SKILL.md, ready to use.
Can I customize this skill for my team?
Absolutely. Edit the SKILL.md file to add team-specific instructions, examples, or workflows.
⚠️ Common Mistakes to Avoid
Not validating data quality
AI analysis is only as good as your input data. Profile and clean data before analysis.
Ignoring data quality
AI analysis inherits all data quality issues — profile your data first.