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An agent capability library

An agent capability library

Tech Sami Honkonen
Sami Honkonen manages local AI agents by treating their capabilities as a version-controlled, searchable documentation index.
What: Sami Honkonen uses a global 'AGENTS.md' file that functions as a capability index, allowing his AI agents to dynamically query specialized Markdown documentation for tasks like browser automation, SSH access to a Studio machine, or managing on-demand Linux VMs via exe.dev.
Why it matters: This approach moves agent management away from static, monolithic prompts toward a modular, file-based architecture that scales as developers add more infrastructure to their local environments.
Deep dive
  • Developers can define a global AGENTS.md index that acts as a capability registry.
  • Agents read the index to determine if they possess the required skill for a specific task.
  • Docs are kept as plain Markdown files in a Git repository.
  • Adding new capabilities requires only updating the index and writing a corresponding Markdown documentation file.
  • Project-specific agents can symlink to this global library or include local overrides to maintain context.
  • The system forces developers to codify their infrastructure as they build it, creating a self-documenting workflow.
Decoder
  • MCP server: Model Context Protocol, an open standard for connecting AI assistants to systems like local files, databases, or development tools.
  • Symlink: A symbolic link, or a file that points to another file or directory, used here to share a single capability source across multiple agent environments.
Original article

In the last post I described how I set up a local LLM and how I create purpose-built agents:

Whenever I want AI help with something specific, I make a new directory under ~/projects/ on my Air and just start working with pi. Once I’ve done what I want to do, I tell pi to record the process in an AGENTS.md. From that point on, every time I open pi in that directory it reads the file and is immediately ready to continue.

Continuing on that, I’ve started building a general library of capabilities for the agents. Pi reads a global AGENTS.md from ~/.pi/agent/AGENTS.md on startup. Mine is a symlink to a CAPABILITIES.md in a repo called agent-docs. It’s a capability index: a list of things the agent can do, each with a short description of when to reach for it and a pointer to a doc that explains how. Here’s an excerpt:

- **Studio machine** — a powerful always-on Mac you can SSH into.
  Reach for it when you need more horsepower or a stable host for a
  background service. Read @/Users/sami/projects/agent-docs/STUDIO.md.

- **exe.dev VMs** — on-demand Linux VMs with a public HTTPS proxy.
  Reach for it when you need a real Linux box or a public URL.
  Read @/Users/sami/projects/agent-docs/EXE-DEV.md.

- **Browser automation** — drive a real Chrome from the shell.
  Reach for it when you need to scrape, fill a form, or visually
  check a deployed page. Read @/Users/sami/projects/agent-docs/AGENT-BROWSER.md.

The agent doesn’t load all specific instructions upfront. It reads the index, decides whether the task matches a capability, and only then reads the relevant doc. The docs themselves are ordinary markdown: what the thing is, when to use it, how to use it. I write and update them almost exclusively with AI. agent-docs is itself a purpose-built agent for maintaining the library.

I also include a reference to CAPABILITIES.md in the AGENTS.md of individual coding projects, so project agents can reach for the same tools.

The current list has seven entries: the Studio, the local LLM, private git hosting on the Studio, exe.dev VMs, browser automation, a personal MCP server, and this blog. Adding a new one is three steps: write the doc, add a line to the index, commit.

The idea is that this compounds. Every time I set something up, I write a doc for it. The agents inherit the capability. Over time the agent should become genuinely useful across a wide range of tasks, because the infrastructure behind it keeps growing.

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