Anthropic Releases Agent Skills to Public Domain

Overview

Anthropic just re-released Claude Skills as Agent Skills, a general purpose specification and framework for AI agents to employ skills, just as Claude Code and Codex do today.

What Are Agent Skills?

Agent Skills are structured packages containing instructions, scripts, and resources that enable AI agents to perform tasks more accurately and efficiently. They provide procedural knowledge and specific context, allowing agents to extend their capabilities dynamically. Agents can use skills to gain domain expertise, acquire new capabilities like creating presentations or analyzing datasets, and execute repeatable workflows consistently. Originally developed by Anthropic and released as an open standard, Agent Skills promote interoperability and reusability across different AI agent products.

Agent Skills have been broadly applied, but primarily to business and software development workflows. Unfortunately, I have found few robust and thoughtful applications for IT operations. I think this about to change, or it will, if I have any say in the matter. 🙂

Agents for Operations Teams and AIOps?

AIOps (AI for IT operations) applies AI and machine learning to automate and enhance IT operations, enabling faster detection, diagnosis, and resolution of issues. The application of AI to operations is a woefully underserved and underrepresented area. Right now the focus is on AI-assisted software development with tools like Cursor, Windsurf, Claude Code, Codex, Gemini, and many others¹. Tools like Claude Code are clearly optimized for code development. In fact, when you start Claude Code or Codex up they have many default commands like “/review”, “/diff”, and “/security-review”.

Agent Skills could be transformative for operations teams. Rather than building custom agents from scratch, operators can leverage general-purpose agents like Claude Code and equip them with domain-specific skills. Early experiments, such as the k8s-troubleshooter skill for Kubernetes triage, demonstrate how Agent Skills can turn a general-purpose agent into a domain expert capable of performing root cause analysis on production incidents.

This pattern suggests a future where operators maintain libraries of reusable skills for their infrastructure, ride the innovation curve of general-purpose agents, and focus on encoding domain knowledge rather than agent logic. The result: faster incident response, consistent troubleshooting workflows, and reduced cognitive load during outages.

What do I mean by operations-centric?

AI Agents as Operations-Centric vs. Developer-Centric

Coding agents such as Claude Code fundamentally are driven by the software engineer who is in the pilot seat. While operators do operate in an imperative mode, they also are frequently operating in a reactive mode to issues that arise in production. In that case, infrastructure and applications are driving an OODA loop. In the new world, rather than an Operator-in-the-OODA-Loop, we have an Agent-in-the-OODA-Loop and then an operator supervising that AI agent.

What this means in practice is that agents for operations need to be proactively notified of issues in production such that they can take immediate actions to triage the issue, deliver an initial investigative report, notify the operations team, and standby for further directives. They are effectively driving the OO in the OODA loop, then coordinating with the operations team on the D, and then either the agent or the operators run the A.

If you’re going to apply Agent Skills to operations, you also need to be deliberate about guardrails. In my view, the “safe default” is read-only skills that focus on evidence capture, triage loops, and reporting, running with least privilege (and ideally through tightly-scoped tooling) so the agent can’t mutate production by accident. That lines up nicely with the operator-in-the-loop reality: let the agent drive the OO in the OODA loop—observe and orient quickly, produce a crisp report with hypotheses and next-step commands—and then have a human explicitly decide when (or if) to execute changes. Over time we can relax those constraints in controlled ways, but starting with strong boundaries is how you get the benefits without turning an outage into a bigger outage.

In addition to responding to production issues, there is also the issue of predictive analytics, an area that has had quite a bit of attention in the past, but is likely to have a bit of a renaissance in the Autonomous Era as agents can be employed to predict faults, help with capacity planning and management and take other proactive measures.

Key Benefits for Agents and AIOps

• Faster time-to-value — drop in a skill and immediately get repeatable workflows without building a bespoke agent.
• Faster, more consistent triage — codify runbooks + evidence capture so incidents start with the same high-quality baseline every time.
• Safer automation — skills can be explicitly scoped (read-only by default, approvals/guardrails) to reduce “agent goes rogue” risk in production.
• Higher-quality outcomes — inject domain context/procedures so agents make fewer wrong assumptions and produce more actionable recommendations.
• Reusable operational knowledge — turn tribal knowledge into versioned, shareable skill packages across teams and environments.
• Auditable operations — standard workflows make it easier to review what was run, why, and what evidence was collected (postmortems/compliance).
• Interoperability and portability — open skills can move between agent products, reducing vendor/tool lock-in.

Conclusion

Anthropic’s release of Agent Skills as an open standard marks an inflection point for agentic AI:

• Don’t build agents, build skills. General-purpose agents like Claude Code are already well-tuned for reasoning and workflow management. Focus your efforts on encoding domain expertise.
• Operations is the next frontier. While AI-assisted development gets the headlines, AIOps remains underserved. Agent Skills provide a path to bring the same capabilities to infrastructure and production systems.
• The OODA loop is changing. Operations-centric agents need event-driven triggers, not just prompts. Expect to see agents that observe, orient, and report—then wait for human decision-making before acting.
• Interoperability matters. An open standard means skills built today will work across tomorrow’s agents—whether Claude Code, Codex, Goose, or others yet to come.

Getting Started

Explore the Agent Skills platform and resources at agentskills.io.

Resources

• Introducing Agent Skills - Anthropic’s announcement post
• Agent Skills Documentation - Official overview, structure, and pre-built skills
• Anthropic Skills GitHub Repo - Official example skills repository
• Agent Skills Quickstart - Tutorial for getting started
• Skill Authoring Best Practices - Writing effective skills
• Agent Skills in the SDK - SDK integration guide
• Claude Agent Skills GitHub Examples - Community implementations
• Troubleshooting Kubernetes with AI Agents - Real-world k8s AIOps example using Agent Skills

• Tags:
• Skills
• Agents
• Kubernetes
• Triage
• Ooda-Loop
• Anthropic