Skillscript is a small language I built to write what I want my local agent to actually do, in a form I can read and version, instead of hoping the model gets it right each time.
The itch started with something small. I wanted my NanoClaw agent to run my morning brief the same way every day. Check overnight tickets, summarize the deploy pipeline, flag anything urgent. Every session, it would re-figure out how to do this from scratch, drift a little, and cost tokens for what's basically a fixed procedure. I could put it in a system prompt or an MD skill file, but those are still instructions the model reads and reasons about every time. And I wanted it to run autonomously and then hand it to the model to reason over the data.
The second thing that pushed me: I wanted to use small local models for the cheap stuff. They're capable, but if you just hand them the wheel, they wander. What I wanted was a way for the frontier model (or me) to write a specific procedure and hand it to the local model to execute not interpret. The skillscript is the program; the model is the runtime.
Skillscript is that. A skillscript is a text file with named steps, variables, conditions, and calls out to tools (MCP connectors, a local model, and shell commands from an operator allowlist). It's deliberately minimal — no eval, no arbitrary imports, no subprocess, no unbounded loops. Bounded language, limited potential for damage. Everything a skillscript can do is in the file. You read it and know.
Where it is: pre-1.0 (0.37), MCP-native, self-hosted. Rough edges I know about: first-run setup takes more steps than it should, some of the grammar is still moving, and the local model integration currently assumes Ollama. It works well enough that I use it every day, but I wouldn't necessarily call it production-ready.
- Repo:
- Docs: https://skillscript.mintlify.app/docs
- npm:
skillscript-runtime
I'd welcome critique on two things especially: the language design (is it too small? too big? wrong shape?) and the trust model around agent-authored skills. What would you want to see before you trusted this on your own machine?
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