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Guide for implementing HolmesGPT - an AI agent for troubleshooting cloud-native environments. Use when investigating Kubernetes issues, analyzing alerts from Prometheus/AlertManager/PagerDuty, performing root cause analysis, configuring HolmesGPT installations (CLI/Helm/Docker), setting up AI providers (OpenAI/Anthropic/Azure), creating custom toolsets, or integrating with observability platforms (Grafana, Loki, Tempo, DataDog).

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SKILL.md

name holmesgpt-skill
description Guide for implementing HolmesGPT - an AI agent for troubleshooting cloud-native environments. Use when investigating Kubernetes issues, analyzing alerts from Prometheus/AlertManager/PagerDuty, performing root cause analysis, configuring HolmesGPT installations (CLI/Helm/Docker), setting up AI providers (OpenAI/Anthropic/Azure), creating custom toolsets, or integrating with observability platforms (Grafana, Loki, Tempo, DataDog).

HolmesGPT Skill

AI-powered troubleshooting for Kubernetes and cloud-native environments.

Overview

HolmesGPT is a CNCF Sandbox project that connects AI models with live observability data to investigate infrastructure problems, find root causes, and suggest remediations. It operates with read-only access and respects RBAC permissions, making it safe for production environments.

Quick Reference

Topic Reference
Installation references/installation.md
Configuration references/configuration.md
Data Sources references/data-sources.md
Commands references/commands.md
Troubleshooting references/troubleshooting.md
HTTP API references/http-api.md
Integrations references/integrations.md

Key Features

  • Root Cause Analysis: Investigates alerts and cluster issues
  • Multi-Source Integration: 30+ toolsets (K8s, Prometheus, Grafana)
  • Alert Integration: AlertManager, PagerDuty, OpsGenie, Jira, Slack
  • Interactive Mode: Troubleshooting with /run, /show, /clear
  • Custom Toolsets: Extend with proprietary tools via YAML configuration
  • CI/CD Integration: Automated deployment failure investigation

Installation Quick Start

CLI (Homebrew)

brew tap robusta-dev/homebrew-holmesgpt
brew install holmesgpt
export ANTHROPIC_API_KEY="your-key"  # or OPENAI_API_KEY
holmes ask "what pods are unhealthy?"

Kubernetes (Helm)

helm repo add robusta https://robusta-charts.storage.googleapis.com
helm repo update
helm install holmesgpt robusta/holmes -f values.yaml

Docker

docker run -it --net=host \
  -e OPENAI_API_KEY="your-key" \
  -v ~/.kube/config:/root/.kube/config \
  us-central1-docker.pkg.dev/genuine-flight-317411/devel/holmes \
  ask "what pods are crashing?"

Essential Commands

# Basic investigation
holmes ask "what pods are unhealthy and why?"
holmes ask "why is my deployment failing?"

# Interactive mode
holmes ask "investigate issue" --interactive

# Alert investigation
holmes investigate alertmanager --alertmanager-url http://localhost:9093
holmes investigate pagerduty --pagerduty-api-key <KEY> --update

# With file context
holmes ask "summarize the key points" -f ./logs.txt

# CI/CD integration
holmes ask "why did deployment fail?" --destination slack --slack-token <TOKEN>

Supported AI Providers

Provider Environment Variable Models
Anthropic ANTHROPIC_API_KEY Sonnet 4, Opus 4.5
OpenAI OPENAI_API_KEY GPT-4.1, GPT-4o
Azure OpenAI AZURE_API_KEY GPT-4.1
AWS Bedrock AWS credentials Claude 3.5 Sonnet
Google Gemini GEMINI_API_KEY Gemini 1.5 Pro
Vertex AI VERTEXAI_PROJECT Gemini 1.5 Pro
Ollama Local install Llama 3.1, Mistral

Basic Helm Values Structure

# values.yaml for Kubernetes deployment
image:
  repository: robustadev/holmes
  tag: latest

env:
  - name: ANTHROPIC_API_KEY
    valueFrom:
      secretKeyRef:
        name: holmesgpt-secrets
        key: anthropic-api-key

# Model configuration
modelList:
  sonnet:
    api_key: "{{ env.ANTHROPIC_API_KEY }}"
    model: anthropic/claude-sonnet-4-20250514
    temperature: 0

# Toolsets to enable
toolsets:
  kubernetes/core:
    enabled: true
  kubernetes/logs:
    enabled: true
  prometheus/metrics:
    enabled: true

# Resources
resources:
  requests:
    memory: "1024Mi"
    cpu: "100m"
  limits:
    memory: "1024Mi"

# RBAC (read-only by default)
createServiceAccount: true

Interactive Mode Commands

Command Description
/clear Reset context when changing topics
/run Execute custom commands and share output with AI
/show Display complete tool outputs
/context Review accumulated investigation information

Custom Toolset Example

# custom-toolset.yaml
toolsets:
  my-custom-tool:
    description: "Custom diagnostic tool"
    tools:
      - name: check_service_health
        description: "Check health of a specific service"
        command: |
          curl -s http://{{ service_name }}.{{ namespace }}.svc.cluster.local/health
        parameters:
          - name: service_name
            description: "Name of the service"
          - name: namespace
            description: "Kubernetes namespace"

Use with: holmes ask "check health" -t custom-toolset.yaml

Kubernetes Annotations for Integration

# Add to Services/Deployments for HolmesGPT context
metadata:
  annotations:
    holmesgpt.dev/runbook: |
      This service handles payment processing.
      Common issues: database connectivity, API rate limits.
      Check: kubectl logs -l app=payment-service

Environment Variables Reference

Variable Description Default
HOLMES_CONFIG_PATH Config file path ~/.holmes/config.yaml
HOLMES_LOG_LEVEL Log verbosity INFO
PROMETHEUS_URL Prometheus server URL -
GITHUB_TOKEN GitHub API token -
DATADOG_API_KEY DataDog API key -
CONFLUENCE_BASE_URL Confluence URL -

Best Practices

  1. Use Specific Queries: Include namespace, deployment name, symptoms
  2. Start with Claude Sonnet 4.0/4.5: Best accuracy for complex investigations
  3. Enable Relevant Toolsets: Only enable what you need to reduce noise
  4. Use Interactive Mode: For complex multi-step investigations
  5. Set Up Runbooks: Provide context for known alert types
  6. CI/CD Integration: Automate deployment failure analysis

Security Considerations

  • HolmesGPT uses read-only access (get, list, watch only)
  • Respects existing RBAC permissions
  • Never modifies, creates, or deletes resources
  • API keys stored in Kubernetes Secrets
  • Data not used for model training

Official Resources