We Already Have Our Own Skills
Your own skills and MD files make the AI better. For one person, on one machine, in one project. Why that is not enough for a team, and what it takes instead.
When we present ai-boost, one sentence comes up sooner or later: “We already have our own skills and MD files.” It usually comes from developers who are already far along with AI. They maintain a CLAUDE.md, have written their own skills and made their AI noticeably better with them.
The objection is fair. Whoever built this has seen the problem: without project knowledge, the AI writes generic code. The question is how far this solution carries.
What skills and MD files do
An instruction file gives the AI rules and conventions, a skill bundles a recurring task. For one person, on one machine, in one project, that works well. The limits show as soon as more comes in: more people, more projects, more time.
Where they hit their limits
They are not company-wide. An MD file lives in one repository, a skill on one machine. Rules meant for the whole company get copied into every project. A few months later, there are as many versions as projects.
They load everything. Every session loads the instruction file in full, no matter the task. The more it contains, the more the context window fills up with rules that do not apply right now.
Available is not used. Whether the AI calls a skill or a tool is its own decision. You cannot rely on it.
In a measurement with 45 test runs, the AI called available tools on its own in only 16 cases. That is why ai-boost supplies the context with every prompt, without the AI having to ask.
Every machine has its own version. Files get copied, adjusted and go stale. Nobody knows which version applies, and none of it is enforced.
They do not know the tickets. What lives in Jira, Confluence or the commit history only gets in if someone maintains it by hand.
Nobody sees whether they work. Whether a rule was followed, whether the code fits the architecture and what the session cost stays invisible.
They are tied to a provider. Many formats are bound to one provider’s tooling. Switch, and you start over.
From personal setup to one standard
The answer is not to get rid of skills. The answer is to move the knowledge in them to where it applies to everyone. That takes four things.
Central, not scattered. Rules and ways of working are maintained once and apply company-wide, across all projects and on every machine.
Fit to the task. Instead of loading everything, every prompt gets the context the task needs.
Enforced, not recommended. A standard only works if deviations are noticed and rejected.
Measurable. Whether rules take effect and what a session costs has to be visible.
How ai-boost does it
ai-boost maintains tools, models per phase, mandatory steps and working rules centrally in the portal and distributes them to every machine. The rules apply company-wide, across all projects. No developer has to wire anything up.
With every prompt, ai-boost supplies the right context from code, conventions, commit history, tickets and documentation. In the session, deviations from the assigned model are rejected. Every session with code changes is assessed, and usage and cost are visible per developer, repository and project. Through the open standard MCP, every AI agent works from the same context.
The knowledge that lives in individual files today belongs in one standard that applies to everyone.
Your own skills are a good start. One standard for everyone is the goal.
The question is not whether your team has good skills. It is whether everyone on the team benefits from them, in every project.