Skills engineering
- Author and maintain Claude Skills tuned to your domain
- Capture incident response, code review, and architecture playbooks
- Versioning, evaluation, and review processes for Skills as a code asset
AI Skills & Practices
Make AI part of how your team actually works.
We help engineering and operations teams turn institutional knowledge into reusable Skills, playbooks, and prompts — so AI augments daily work, not just side projects.
The teams getting real leverage from AI aren’t the ones with the most access to it. They’re the ones who codified how their best people work, and made that knowledge available to every engineer through Skills, agents, and well-designed prompts.
We help you capture that knowledge, package it as Skills your tooling can invoke, and establish the engineering practices that keep it sharp over time.
AI-native engineering practices
- Claude Code workflows, hooks, and team configuration
- Prompt and context patterns that survive model upgrades
- Internal tooling so AI usage is auditable and improving
How we engage
- Workshop with senior engineers to surface high-value workflows
- Build a starter Skills library and reference agents with your team
- Define ownership, review, and evaluation practices going forward
- Coach engineers so authoring Skills becomes a normal part of delivery
FAQ
Common questions about AI skills and practices
What is a Claude Skill, in practice?
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A Skill is a packaged piece of know-how — instructions, examples, and reusable assets — that Claude or another agent can load on demand. It turns the way your best engineer handles a task into something every engineer can invoke.
How are Skills different from prompts or system prompts?
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A prompt is one-off. A system prompt is global. A Skill is targeted, versioned, and conditional — it loads only when relevant, and it’s owned like code. That’s what makes it scale across a team.
Who in the team should own Skills?
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The senior engineers whose practice you’re encoding. Skills are not a docs project run by enablement — they’re an engineering artefact, reviewed like code. They decay if no one owns them.
How do you keep Skills from going stale as models change?
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Evaluation, mostly. Each Skill ships with a small set of examples and expected outcomes. When the model upgrades, you re-run the evals and only the failing ones need attention. Without evals, Skills rot quietly.
Can Skills work with tools other than Claude?
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The format is Anthropic-native, but the pattern — packaged, loadable expertise — works with any agent stack. We’ve helped teams adapt the approach to in-house frameworks where Claude isn’t the runtime.