the skills ecosystem: how AI capabilities become composable tools

by Ray Svitla

AI agents are only as useful as the tools they can use. this sounds obvious but has weird implications.

a language model that can only talk is a chatbot. a language model that can execute code, read files, control browsers, and interact with APIs — that’s an agent.

the difference isn’t the model. it’s the skills.

what skills actually are

in the AI context, a “skill” is just a function an agent can call. could be simple: “read this file.” could be complex: “book a meeting that works for three people across two timezones considering their calendar conflicts and preferences.”

the Model Context Protocol (MCP) standardizes this. instead of every AI assistant inventing its own plugin system, MCP provides a common interface: servers expose tools, clients consume them.

think of it like USB. doesn’t matter if you’re plugging in a keyboard or a hard drive — the port works the same way.

MCP servers are containers of related skills. one server might handle filesystem operations. another handles Linear integration. another controls your smart home.

agents discover what skills are available, understand how to use them (via JSON schemas), and compose them to accomplish tasks.

discovery: how agents find capabilities

early AI assistants had fixed toolsets. the model knew about a few built-in functions and that was it.

MCP changes this. skills are discoverable at runtime.

an agent connects to available MCP servers, requests their tool manifests, and suddenly knows how to do things it was never explicitly trained for.

this is powerful and also slightly terrifying. you install a new MCP server and every agent in your system immediately gains those capabilities.

no retraining. no configuration. just instant expansion of the possibility space.

the downside: now you need to think about tool discovery as a security boundary. which agents should have access to which skills? how do you prevent an agent from using tools it shouldn’t?

composition: the emergent complexity

here’s where it gets interesting. agents don’t just use individual skills. they compose them.

example task: “analyze my project’s test coverage and create Linear tickets for any files below 80%”

skills needed: → run test coverage tool → parse coverage report → filter results → create Linear tickets → format ticket descriptions

no single skill does this. but an agent can chain them together.

this is the promise of the skills ecosystem: complex capabilities emerge from composing simple primitives.

the catch: composition creates new failure modes. each skill in the chain can fail. errors need to propagate correctly. state needs to be managed. retries need to make sense.

when you compose five skills, you don’t get five times the functionality. you get five times the potential points of failure.

trust: the unsolved problem

who publishes MCP servers? anyone. who verifies they do what they claim? nobody.

right now, the MCP ecosystem operates on trust. you find a server on github, read the code (maybe), and decide to run it.

this works fine for individual tinkerers. it breaks down at scale.

imagine a skills marketplace: thousands of available MCP servers, all claiming to do useful things. how do you know which ones are safe? which ones work as advertised? which ones will still exist next month?

npm had this problem. the solution involved package registries, verification systems, security audits, and still occasional supply-chain attacks.

MCP will need similar infrastructure. probably: → package registry with reviews → sandboxing for untrusted skills → permission systems (this skill can read but not write) → usage analytics (how many people actually use this)

we’re not there yet. right now it’s the wild west.

namespacing: avoiding skill collision

what happens when two MCP servers both provide a “search” skill?

currently: undefined behavior, probably chaos.

eventually: namespacing. skills become filesystem/read and web/search and email/search instead of just read and search.

agents need to understand not just “I need to search” but “I need to search in this specific domain with these specific semantics.”

this is a UX problem disguised as a technical problem. humans understand context naturally. agents need explicit disambiguation.

versioning: the compatibility nightmare

skills evolve. a server updates its API. suddenly agents that depended on the old interface break.

software engineers solved this decades ago with semantic versioning. but that assumes everyone follows the rules and handles breaking changes responsibly.

in practice: skills will break. agents will need fallback strategies. prompts will need to handle “this tool is unavailable, try this alternative.”

robust agent systems will need to treat skill availability as uncertain. plan A fails? try plan B. both fail? ask the human.

the curation problem

more skills ≠ better agents.

actually, too many skills makes agents worse. they waste tokens considering irrelevant tools. they choose the wrong tool for the job. they get confused by similar-sounding capabilities.

humans curate their toolboxes. you don’t carry every tool you own when you go to fix something. you bring the relevant ones.

agents need similar curation. which skills should be available for this task? which can be safely ignored?

right now, this is mostly manual. you configure which MCP servers connect to which agents. crude but functional.

eventually: dynamic skill loading. the agent sees the task, determines which capabilities it might need, loads just those skills, completes the task.

but we need better ways to describe what skills are relevant for what contexts.

local vs remote skills

some skills run locally (read a file, execute code). some require network calls (search the web, create Linear tickets).

local skills are fast and reliable. remote skills are slow and can fail.

agents need to understand this difference. if a task can be accomplished with local skills, prefer that. only reach out to remote skills when necessary.

also: privacy. local skills can’t leak data to third parties. remote skills might.

a well-designed skills ecosystem needs clear boundaries about what stays local and what goes remote.

the plugin paradox

wordpress proved that plugin ecosystems can be incredibly successful. they also proved they can be incredibly messy.

too few plugins: the system is limited. too many plugins: the system is incomprehensible, slow, and fragile.

MCP will hit the same paradox. there’s a sweet spot between “too limited” and “too chaotic” and finding it requires both good tooling and good community norms.

my guess: we’ll see curated skill packs. “install the developer toolkit” gives you 20 carefully chosen skills that work well together. “install the content creator pack” gives you a different set.

pre-composed collections instead of à la carte selection.

interoperability: when skills need to talk to each other

most skills are independent. “read file” doesn’t need to know about “create Linear ticket.”

but some skills need to be aware of each other. a “search database” skill and a “update database” skill should share connection pooling.

MCP doesn’t currently have a great story for this. servers are independent. they don’t coordinate.

this creates inefficiencies. multiple servers might each open their own database connection instead of sharing one.

eventually we’ll need server-to-server communication or a shared runtime that handles resource pooling.

the future: skills as infrastructure

right now, skills feel like add-ons. optional capabilities you can install if you want.

soon, they’ll feel like infrastructure. the base layer that determines what your agents can do.

choosing which skills to support becomes a strategic decision. similar to choosing which APIs to integrate or which cloud services to depend on.

and just like cloud services, you’ll have: → first-party skills (official, well-supported) → third-party skills (community-built, varying quality) → custom skills (built in-house for specific needs)

managing this ecosystem becomes a real job, not a side project.

what skills would you build if you could expose any capability to AI agents? and what skills worry you — what capabilities should probably stay out of the skills ecosystem entirely?

Ray Svitla stay evolving 🐌