holmesgpt-skill
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).
When & Why to Use This Skill
HolmesGPT is an AI-powered agent designed for troubleshooting cloud-native and Kubernetes environments. It streamlines incident response by connecting AI models with live observability data from tools like Prometheus, Grafana, and PagerDuty. By performing automated root cause analysis and suggesting remediations with read-only access, it helps SREs and DevOps teams resolve infrastructure issues faster and more accurately while maintaining strict security standards.
Use Cases
- Investigating Kubernetes pod failures, crashing deployments, or unhealthy services using natural language queries.
- Analyzing alerts from Prometheus, AlertManager, or PagerDuty to identify the underlying root cause of infrastructure anomalies.
- Automating the investigation of CI/CD deployment failures to reduce Mean Time to Recovery (MTTR) in production pipelines.
- Correlating logs and metrics across multiple observability platforms like Datadog, Grafana, and Loki during active incidents.
- Creating custom diagnostic toolsets to extend AI troubleshooting capabilities to proprietary internal infrastructure and runbooks.
| 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
- Use Specific Queries: Include namespace, deployment name, symptoms
- Start with Claude Sonnet 4.0/4.5: Best accuracy for complex investigations
- Enable Relevant Toolsets: Only enable what you need to reduce noise
- Use Interactive Mode: For complex multi-step investigations
- Set Up Runbooks: Provide context for known alert types
- CI/CD Integration: Automate deployment failure analysis
Security Considerations
- HolmesGPT uses read-only access (
get,list,watchonly) - Respects existing RBAC permissions
- Never modifies, creates, or deletes resources
- API keys stored in Kubernetes Secrets
- Data not used for model training
Official Resources
- Documentation: https://holmesgpt.dev/
- GitHub: https://github.com/robusta-dev/holmesgpt
- Helm Chart: https://github.com/robusta-dev/holmesgpt/tree/master/helm/holmes
- Slack Community: Cloud Native Slack