Over 160 production-ready skill documents (SKILL.md) for LLM coding agents like Claude Code, Cursor, and Codex.Each skill contains expert-level configs, CLI commands, troubleshooting guides, and ready-to-run scripts spanning six domains: DevOps (CI/CD, Kubernetes, observability), Security (vulnerability scanning, secrets, hardening), Infrastructure (AWS/Azure/GCP, networking, databases, local inference stacks), LLM engineering (agent evals, RAG infrastructure, inference scaling), Compliance (SOC2, HIPAA, GDPR, ISO 27001), and IT operations. Skills install in under a minute via the `npx skills add` CLI or a simple git clone, and the agent discovers, matches, and activates them on demand.
- Ships with notable niche skills absent from other repos: MCP server security, eBPF kernel-level observability, OpenTofu migration, agent-eval CI/CD gates, and multi-tenant LLM hosting with KEDA autoscaling.
- Ships with notable niche skills absent from other repos: MCP server security, eBPF kernel-level observability, OpenTofu migration, agent-eval CI/CD gates, and multi-tenant LLM hosting with KEDA autoscaling.
Google explores the transition from traditional deterministic automation to agentic AI within Site Reliability Engineering. As system complexity grows due to microservices, cloud scale, and increased code generation, Google is implementing SRE AI across the entire software development lifecycle to enhance reliability. The approach includes using agents for automated runbook improvement, advanced anomaly detection, incident management orchestration, and autonomous investigation utilizing observability data.
- Moving from deterministic automation to agentic AI models
- Integration of AI in reliability design and documentation
- Using anomaly detection rather than static thresholds for alerting
- Orchestrating incident response via communication monitoring and automated summaries
- Leveraging historical data through AI Insights for risk management
- Adhering to principles of transparency, security, and agent identity
- Moving from deterministic automation to agentic AI models
- Integration of AI in reliability design and documentation
- Using anomaly detection rather than static thresholds for alerting
- Orchestrating incident response via communication monitoring and automated summaries
- Leveraging historical data through AI Insights for risk management
- Adhering to principles of transparency, security, and agent identity
AWS has released the general availability of its DevOps Agent, a generative AI assistant designed to automate incident investigation and operational tasks. Built on Amazon Bedrock AgentCore, the tool integrates with observability platforms, code repositories, and CI/CD pipelines to autonomously triage issues and correlate telemetry data. New capabilities include support for investigating applications in Azure and on-premises environments, custom agent skills, and personalized reporting.
Key highlights:
* Autonomous incident investigation triggered by webhooks from sources like CloudWatch or PagerDuty.
* Integration with major tools including Datadog, Grafana, Splunk, GitHub, and GitLab.
* Reported performance improvements of up to 75% lower MTTR during preview.
* Pricing model based on cumulative time spent on operational tasks per second.
Key highlights:
* Autonomous incident investigation triggered by webhooks from sources like CloudWatch or PagerDuty.
* Integration with major tools including Datadog, Grafana, Splunk, GitHub, and GitLab.
* Reported performance improvements of up to 75% lower MTTR during preview.
* Pricing model based on cumulative time spent on operational tasks per second.
This article examines the development of Microsoft’s Azure SRE Agent, designed to mitigate operational toil in mission-critical environments. By utilizing an "agentic workflow" of specialized AI agents, Microsoft has integrated automation across the entire software development lifecycle. This human-AI partnership has autonomously resolved over 35,000 incidents and saved more than 50,000 developer hours, accelerating root cause analysis and mitigation while maintaining rigorous governance and human oversight.
This article details how HPE is addressing operational fatigue and burnout in IT teams through the introduction of agentic AI operations. HPE's new system utilizes skills-based AI agents that work alongside human operators to reduce alert noise, improve response times, and cut root cause analysis time by at least half, according to early adopters.
The focus is on augmenting human capabilities rather than replacing them, with a strong emphasis on auditability, transparency, and human oversight in AI-driven actions. The system aims to break down data silos and provide proactive insights to prevent issues before they escalate.
The focus is on augmenting human capabilities rather than replacing them, with a strong emphasis on auditability, transparency, and human oversight in AI-driven actions. The system aims to break down data silos and provide proactive insights to prevent issues before they escalate.
This article discusses how AI is changing infrastructure as code (IaC) and the challenges it presents. Spacelift's co-founder, Marcin Wyszynski, explains that while AI tools can democratize infrastructure provisioning, the lack of understanding of the generated code poses risks. He draws a parallel to learning a foreign language – AI can produce the code, but teams need to comprehend it to avoid potentially disastrous infrastructure changes.
Spacelift's solution, Intent, focuses on deterministic guardrails and integration with tools like Open Policy Agent to ensure safe and controlled AI-driven infrastructure management. The core challenge is balancing speed and control in a rapidly evolving landscape.
Spacelift's solution, Intent, focuses on deterministic guardrails and integration with tools like Open Policy Agent to ensure safe and controlled AI-driven infrastructure management. The core challenge is balancing speed and control in a rapidly evolving landscape.
This article explores the emerging category of AI-powered operations agents, comparing AI DevOps engineers and AI SRE agents, how cloud providers are responding, and what engineers should consider when evaluating these tools.
>When deployed strategically, agents can empower SREs to offload low-risk, toilsome tasks so they can focus on the most critical matters.
Agents in practice include:
* **Contextual Information:** Providing SREs with details from previously resolved incidents involving the same service, including responder notes.
* **Root Cause Analysis:** Suggesting potential origins of an issue and identifying recent configuration changes that might be responsible.
* **Automated Remediation:** Handling low-risk, well-defined issues without human intervention, with SRE review of after-action reports.
* **Diagnostic Suggestions:** Nudging SREs towards running specific diagnostics for partially understood incidents and supplying them automatically.
* **Runbook Generation:** Automatically creating and updating runbooks based on successful remediation steps, preventing recurring issues.
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Agents in practice include:
* **Contextual Information:** Providing SREs with details from previously resolved incidents involving the same service, including responder notes.
* **Root Cause Analysis:** Suggesting potential origins of an issue and identifying recent configuration changes that might be responsible.
* **Automated Remediation:** Handling low-risk, well-defined issues without human intervention, with SRE review of after-action reports.
* **Diagnostic Suggestions:** Nudging SREs towards running specific diagnostics for partially understood incidents and supplying them automatically.
* **Runbook Generation:** Automatically creating and updating runbooks based on successful remediation steps, preventing recurring issues.
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TraceRoot.AI is an AI-native observability platform that helps developers fix production bugs faster by analyzing structured logs and traces. It offers SDK integration, AI agents for root cause analysis, and a platform for comprehensive visualizations.
TraceRoot accelerates the debugging process with AI-powered insights. It integrates seamlessly into your development workflow, providing real-time trace and log analysis, code context understanding, and intelligent assistance. It offers both a cloud and self-hosted version, with SDKs available for Python and JavaScript/TypeScript.
