Infrahub Skills is an AI skills package for infrastructure engineers and platform teams using Infrahub. It gives their AI coding assistant built-in Infrahub expertise. Teams describe what they want in plain language and the skills produce valid, best-practice schemas, generators, transforms, checks, and more. Whether you’re prototyping a new use case, extending an existing implementation, or onboarding a new engineer, Skills help you get more out of Infrahub, faster.
Bring Infrahub expertise to how you already work
Infrahub is an AI-ready data management platform for network and infrastructure automation at scale. Its extensible, schema-first architecture lets teams model any domain, version every change, and drive reliable automation across production environments. With that flexibility comes a set of conventions and best practices.
Teams typically pick up expertise in using a platform over time — through documentation, example projects, and conversations with more experienced colleagues. Gathering expertise this way works, and it’s how platforms and systems are learned. But the process of gathering expertise in this way can be time-consuming and sequential, and the expertise gained on one project doesn’t automatically carry over to the next engineer who joins the team.
AI accelerates the ability for engineers to learn and use a platform. With AI skills, we can now embed platform expertise directly into the AI coding assistants engineers already use — so that anyone building with Infrahub gets the benefit of conventions and best practices they haven’t yet learned, applied automatically to their own work and explained along the way.
What it looks like to build with Skills
Skills are useful whether you’re just getting started, deep in a build, or running Infrahub in production.
Prototyping a new use case: Describe what you want to model in plain language and have an AI agent build it in Infrahub. No need to master the schema format first. What used to take weeks of ramp-up to build a POC can happen in hours (or less!).
Actively building: Skills shorten the loop between ideation and having a working implementation. Infrahub conventions are applied by an AI agent at the point of generation, so output is built correctly the first time. This minimizes the rework loop. Six skills cover the core build workflow:
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Schema Manager turns a plain-language description of your domain into a valid schema, with naming conventions, relationships, and generics applied automatically.
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Object Manager populates your schema with data files that load in the right order.
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Generator Manager builds generators that create infrastructure objects from design definitions.
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Transform Manager creates Python or Jinja2 transforms that turn Infrahub data into device configs, reports, and other formats.
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Check Manager writes Python validation checks that run in proposed change pipelines.
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Menu Manager shapes the web UI to match how your team thinks about your data.
Extending a production implementation: Skills help you keep iterating. They help you add new features and build integrations with external systems like spreadsheets, CMDBs, NetBox, and Nautobot which is historically one of the most time-consuming parts of any Infrahub project. Additionally, when new engineers join the team, skills allow Infrahub expertise to travel with the repo rather than being trapped in Slack history. Two skills are built specifically the ongoing management of a production implementation:
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Data Analyzer queries a running Infrahub instance in plain language to provide cross-node correlation, drift detection, blast-radius analysis, data quality audits. The data analyzer allows AI skills to do this leveraging an MCP server, so they don’t have to hand roll GraphQL queries to do so.
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Repo Auditor surfaces where a project has drifted from best practices over time and reports what needs to be adjusted.
Two ways to work
Not every task needs the same treatment. Skills supports different modes, matched to task complexity:
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Direct mode: Using direct mode, you interact with your AI agent directly and it uses skills on the back end. Use this mode for well-scoped, single-skill tasks. Describe what you need and the AI matches it to the right skill and produces it. This mode is best used for adding an attribute, creating a check, or populating a batch of objects.
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Spec-Driven Development (SDD): SDD is not unique to Infrahub skills. It’s becoming a widely used method of invocation to produce high quality outputs from LLMs. SDD leverages a socratic method to build specifications that the LLM then goes and deploys. Use this mode for complex, multi-part builds. The AI reasons through requirements with you first, validates the approach against Infrahub conventions, and lays out a task plan for review. Nothing is generated until you approve the plan.
Across both modes, the AI explains the reasoning behind every recommendation. The result is that users build real Infrahub expertise as they work.
How to use it
Infrahub Skills are ingested as context to whichever AI coding assistant you use when they are relevant to the task at hand. You simply need to install them to make them available either globally or into the project directory you are working with. After they are installed, you can describe a task in plain language and let the AI match it to the right skill automatically — or invoke one directly if you know which you want.
Skills follows the open Agent Skills format, so they work with Claude Code, GitHub Copilot, Cursor, Windsurf, Amp, Cline, and Codex — any AI tool that reads custom context files from the project directory.
Get started
Head over to the Infrahub Skills documentation for installation options for your AI tool of choice, a walkthrough of each skill, and more on how to get the most out of them.
Community contributions are welcome — new rules, examples, and improvements can be submitted via PR on the GitHub repo.
