Rigup.Nix


Self-Contained & Modular AI Agent Config

`rigup` is a Nix-based system for packaging knowledge, instructions and skills for your AI agent with the tools and configuration needed to execute them.

A _riglet_ is _executable knowledge_:

• metadata to indicate to your agent what this riglet is for and when it is useful to consult it
• a set of operations, instructions, processes, a.k.a a new Skill for your agent
• the tools (nix packages) needed to execute these instructions
• the configuration for these tools (if any)

By combining the relevant riglets together, you build your agent's _rig_: the tools it needs to work on your project, and the operational knowledge it needs to use those tools properly and efficiently.

By default, the documentation of a riglet is _lazily_ loaded (like Skills are): at startup your agent is fed a manifest, only showing for each riglet a quick description and a set of use cases. It will then go on reading it when it needs to, or is prompted to. However, `rigup` gives you more control than Skills traditionally do in terms of when your agent should be exposed to a riglet's content. For instance, a fundamental riglet can be set to have an _eager_ disclosure (forcing the agent to see the whole SKILL.md at startup), while another one can have a _shallow-toc_ disclosure (the agent will see a table of contents of the riglet's main SKILL.md).

Besides, while Skills are necessarily fixed files, riglets' documentation files can be produced by any Nix _derivation_. It means the contents can also just be read from a raw file, or they can be defined programmatically, for instance by a bash script. This makes a riglet's doc file contents configurable in the same way this riglet's tools are. `rigup`'s Nix library provides utilities to template your skills via [minijinja](https://github.com/mitsuhiko/minijinja) to facilitate this use case.

`rigup` has a "knowledge-first" d