AI agents & skills

LiteORM is built for how software gets written now: through AI coding assistants. Rather than leave your assistant to guess the API from training data — and write subtly wrong code — LiteORM ships Agent Skills: task-scoped instruction files that hand the assistant the exact, current API for one job, so it gets it right the first time. If your day-to-day coding involves an AI assistant, setting this up is the highest-leverage five minutes you’ll spend getting started.

There are three distinct pieces, for three different audiences:

• Agent Skills (skills/) — for an assistant writing application code with LiteORM. This page.
• AGENTS.md — for an agent (or human) contributing to the LiteORM repository itself.
• AI in the studio — for the people using your database: natural-language → SQL inside the embedded admin GUI. See the studio guide.

A skill is a folder under skills/ containing a single SKILL.md: YAML frontmatter (a name and a one-sentence description that states when to use it) followed by a tight, actionable body — the real API as tables and short snippets, plus the pitfalls that matter. Each skill covers one job (writing queries, defining models, migrating a schema, porting from gorm, …), so the assistant loads only what the current task needs.

The description is the trigger: a capable assistant reads the available skills’ descriptions and pulls in the relevant SKILL.md on demand, the way it would consult documentation — except this documentation is written for a machine and pinned to the version of LiteORM you’re using.

Copy the skills/ folder from the LiteORM repository into your AI assistant’s skills (or instructions) directory — the whole folder, or just the individual skill folders you want. Assistants that support the Agent Skills convention (a SKILL.md with name/description frontmatter) then load the right skill automatically based on the task you describe.

If your assistant doesn’t auto-load skills, the files are still useful by hand: paste the relevant SKILL.md into the conversation when you start a task — “writing a migration”, “defining a model with relations”, “porting a gorm struct” — and the assistant has the precise surface in front of it instead of a half-remembered one.

Either way, point your assistant at skills/using-liteorm first: it covers choosing the query vs orm front-end, opening a backend, first CRUD, and the shared Session/transaction model — the orientation every other skill builds on.

Skill	Use it when you’re…
using-liteorm	Starting out: choosing query vs orm, opening a backend, first CRUD, the shared Session/transaction model.
query-builder	Writing explicit, typed queries: predicates, joins, unions, subqueries, EXISTS, Iter streaming, Pluck, aggregates, Raw, the query.Repo.
orm-models	Defining declarative models: structs + tags, AutoMigrate, the orm.Repo, associations (Load/Attach), hooks, soft delete.
migrations	Evolving a schema: additive AutoMigrate, reviewable GenerateMigration, the migrate runner, WritePair.
codegen	Generating typed columns/models/queries: liteorm gen, the sqlc plugin, the gorm porter.
sqlite-search	Adding SQLite vector, full-text (FTS5), or hybrid (RRF) search.
postgres-advanced	Using Postgres LISTEN/NOTIFY or the typed JSONB / array operators.
porting-from-gorm	Migrating a gorm codebase: native gorm-tag reading and rewriting to native orm tags, and what differs.
logging	Tracing executed SQL while developing, via slog or the colored handler.
studio	Mounting the embedded database studio, registering models, locking it down.
pitfalls	Avoiding the gotchas that trip people up.

Skills ship alongside the code and are updated with it, so they stay in lockstep with the API — a skill won’t recommend a method that no longer exists or miss one that was just added.

If your agent is working inside the LiteORM repository — adding a feature, fixing a bug — point it at AGENTS.md instead. That’s the contributor onboarding: the architecture in a paragraph, the repository layout, the fragile invariants not to break (the driver-free core, late placeholder rendering, feature-gated dialect operators), the conventions, and the common just tasks. It’s written to make an agent productive in a few minutes without re-deriving how the project is structured.

• Getting started — install and your first query/model.
• Studio — the embedded admin GUI and its built-in natural-language-to-SQL.
• skills/ — the skill files themselves; AGENTS.md — contributor onboarding.