AI Decision System
Replayable, offline-verifiable records for AI-assisted go/no-go decisions: experiment promotions, typed model answers and human-approved actions.
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一分鐘完成驗證A small, checkable language for how an application should behave: who can act, under what conditions, and what changes. Coding agents draft the model; the deterministic meant CLI checks it.
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A DSL for business intent and observable behavior. People describe what an application should do in business vocabulary; engineers choose how to implement it. Coding agents work from that model and the engineer's technical design. The deterministic toolkit checks the model; application tests establish whether the implementation follows it.檢視完整 README ↗A DSL for business intent and observable behavior. People describe what an application should do in business vocabulary; engineers choose how to implement it. Coding agents work from that model and the engineer's technical design. The deterministic toolkit checks the model; application tests establish whether the implementation follows it.
Coding agents need a source of truth about what a system means — who may do what, under which conditions, with what observable outcomes — that can be checked. A Meant model captures that in eight statement kinds (concept, dimension, touchpoints, shape, exposure, action, invariant, scenario), with total-by-construction expressions and no Turing-completeness in the kernel. No LLM sits anywhere in the checking path: you author, the deterministic toolkit verdicts. The model anchors to behavior, never to source code.
From the conformance corpus (a tiny lending desk):
shape OverdueLoan of Loan
where status = open and due_date < today
wait [ReturnBook] timeout 3 days -> SendReminder
action ReturnBook
"The member or the desk closes a loan."
by member, librarian
requires OpenLoan else NOT_OPEN
by member:
requires actor.id = member.id
in OverdueLoan :
effect
a late return is noted at the desk
effect
status = returned
the member's open loan count decreases by one
verified-by tests/lending/return.test.ts#returns
Shapes are pure predicates over the world — overlapping views, never a state machine you maintain by hand; the wait … timeout fuse is the one construct that reacts to time. Actions declare who may act (by; an unlisted actor is denied by default), preconditions with deny codes, and observable outcomes as deltas and creations that the simulator can execute. Every capitalized token must resolve to a declaration in the file; quoted strings are instance data; lowercase residue sentences are honestly unverified.
Requires Bun ≥ 1.4. Zero runtime dependencies.
bun add -g meant-lang
meant # the onboarding page
Give your coding agent one prompt that names one workflow:
Run `meant` and follow its instructions to build a Meant model of the <workflow> workflow in this app.
The agent reads the "Starting from code" section of meant skill, mines that workflow's code into .meant/model.meant (code-first mode), proposes vocabulary with file:line evidence for your verdict, and runs check, sim and report. You review the model, its falsified claims, and its open questions.
The development extension in editors/zed/ provides .meant recognition, syntax highlighting, declaration outlines, # comments, and two-space indentation settings. It is grammar-only; model diagnostics come from the CLI and agent hook.
zed: install dev extension and select this repository’s editors/zed directory. Zed fetches the pinned grammar from teambrilliant/tree-sitter-meant and compiles it during installation using its WASI SDK. See Zed’s development-extension instructions.fixtures/known-good.meant. Confirm the selected language is Meant Lang, declaration names appear in the outline, comments and keywords are highlighted, and indentation uses two spaces. If installation fails, inspect zed: open log.Grammar changes must pass that repository’s bun run check, be committed in that repository, and have their full commit SHA pinned in the extension before reinstalling. Generated material under editors/zed/grammars/ is ignored.
meant # orientation: what this is, the loop, the verb table
meant check <paths> [--json] # lexer → parser → resolver → laws; exit 1 iff errors
meant check --explain <LAW> # a law's normative text + worked example
meant fmt <paths> [--check] # canonical formatting; --check exits 1 on drift
meant sim <paths> # witnesses, probes, scenario replay
meant report <paths> # opacity queue, residue patterns, discovery candidates, questions
meant skill [install] # the agent editing discipline (stdout, or written to .claude/skills/)
meant spec [<section> | --all] # the normative grammar, sliceable by section
meant hook # PostToolUse feedback from a tool event on stdin
Paths are .meant files, or directories and .md files — models embedded in exact meant fences in Markdown are extracted and checked identically. Application models conventionally live in their own repository at .meant/model.meant, with decisions in .meant/history.md. Pass .meant/model.meant or .meant explicitly: searching . skips dot-directories.
A model may span files: every file in one run whose domain <name> header matches is one domain, resolved and simulated together — so point commands at the model's directory. Clauses may sit in any of its files; a key given two different values is a P/domain-clause-conflict.
Every diagnostic cites its law: V1–V15 are the validator obligations from spec §7, P marks violations of the grammar productions themselves, and L-… are structural smells, always info-grade. --json is the agent interface; law and code strings are stable API.
The binary teaches its own use: bare meant prints the bootstrap, meant skill prints the editing discipline for injection into any agent's context, and meant skill install persists it as a Claude Code skill in the host repo. A PostToolUse hook (meant hook) re-checks any edited model file (with the rest of its domain) and feeds diagnostics straight back into the session — the printed settings snippet wires it up. The model is decided by the human operator: agents draft, the checker verdicts mechanically, the operator verdicts semantically.
spec/meant-grammar-v0.md # the normative grammar — where spec and code disagree, the spec wins
spec/errata.md # append-only change log (E#); E1–E55 folded into the grammar, new entries from E56
src/ # the checker toolchain: TypeScript on Bun, zero runtime deps
fixtures/ # conformance corpus; expectations inline via `# EXPECT: <law>`;
# known-good.meant must stay at zero errors and zero warnings
examples/ # complete neutral examples, including invoicing.meant
From a checkout, bun install (dev tooling only), then invoke the CLI as bun run meant …; bun link puts the checkout's meant on your PATH.
bun test # goldens, lexer round-trip, fixture EXPECT harness
bun run typecheck
Zero runtime dependencies is a constraint, not a preference — any dependency, runtime or dev, needs explicit agreement first.
v0 developer preview: commands and diagnostics may change between releases. The grammar has been through more than 50 recorded errata driven by modeling real systems. The current toolkit validates and explores models. Automatic application conformance and continuous runtime checking remain future work.
MIT. Copyright (c) 2026 Brilliant Consulting SL. The license covers this repository's first-party tooling, editor support, specification, documentation, and examples. Contributions are welcome; commercial use and private modifications are permitted under the license.