Engineering teams are actively building internal collections of skills for agents: code review, troubleshooting, design preparation, onboarding, security practices.
And it looks great until you hit the question: how do you distribute those skills across dozens of teams and multiple harnesses? For Claude you need to put skills into
.claude, for Cursor into .cursor, for Gemini into .gemini, etc. And things become even messier when you need to roll out updates.To solve this problem big companies mostly build their own in-house solutions. Smaller companies usually just copy files from some shared repository and manage this complexity manually.
I don’t like reinventing the wheel, so when my team faced the same problem, we started looking for an existing solution we could reuse. And the only actively maintained tool we managed to find was apm by Microsoft.
APM is a package manager for prompts, skills, and MCPs. In other words, it’s maven or gomod for agents.
APM package structure:
my-package/
├── apm.yml
└── .apm/
├── instructions/
│ └── my.instructions.md
├── skills/
│ └── my-skill/
│ └── SKILL.md
├── agents/
└── prompts/
To install the package in a target repo you need to define apm.yaml with a list of required dependencies:
name: my-projecty
version: 1.0.0
targets:
- claude
- copilot
dependencies:
apm:
- <git-address>/my-package
- <git-address>/another-package
mcp: []
After that you just run:
apm install
and the required skills will be installed into the corresponding harness folders (
.claude, .copilot, etc.). The tool works with Github and on-prem git installations like Gitlab.
APM is not perfect. It had some unpleasant (but not critical) issues, and sometimes you can really feel that it was heavily vibe-coded in Python .
But despite all that, the tool actually works: you have a spec to define your skills\prompts packages, distribute and update them with simple
apm update. And on top of apm dependencies format it’s pretty easy to vibe-code your own internal skills marketplace.#ai #engineering #agents
