JetBrains MPS is a development environment for creating domain-specific languages, which are programming languages designed for a particular field or task. It provides editors with features such as completion, semantic checks, and type checking, and can generate code in languages including Java and XML.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/jetbrains/mps/mps-aspect-typesystemnpx skills add JetBrains/MPS --skill mps-aspect-typesystemgit clone --depth 1 https://github.com/JetBrains/MPSWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/jetbrains/mps/mps-aspect-typesystem)<a href="https://agentmods.dev/skills/jetbrains/mps/mps-aspect-typesystem"><img src="https://agentmods.dev/badge/skills/jetbrains/mps/mps-aspect-typesystem.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00133 | $0.02129 |
| Opus 5 | $0.00067 | $0.01064 |
| Sonnet 5 | $0.00027 | $0.00426 |
| Haiku 4.5 | $0.00013 | $0.00213 |
Grade A, and why
mps-aspect-typesystem scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 61 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MPS Typesystem and Checking Aspect
The typesystem aspect gives nodes types and reports semantic errors. It combines two related sub-aspects:
- Typesystem rules — compute types and constraints on them (
InferenceRule,SubtypingRule,InequationReplacementRule,ComparisonRule,SubstituteTypeRule). - Non-typesystem checking rules — produce errors/warnings/messages without contributing to type inference (
NonTypesystemRule, a.k.a. "checking rule").
Lives in <lang>/languageModels/typesystem.mps, language jetbrains.mps.lang.typesystem. Rule bodies are BaseLanguage + smodel + collections + closures.
Critical Directives
- One
InferenceRule(or other root rule) per concept whose type/check you compute. Multiple rules collectively constrain a node — keep each rule focused. - The
inferprefix makes an inequation soft (the solver tries to satisfy it, will not immediately error). Withoutinfer, violating the inequation reports an error. Choose deliberately. - In equation/inequation JSON, both
leftExpressionandrightExpressionareTypeClauseslots — always wrap the real Expression in aNormalTypeClause(normalTypechild holds the actual Expression). Do not put the Expression directly underleftExpression/rightExpression. - TextGen-style dispatch caveat does not apply here; typesystem rules are inherited via concept hierarchy, but the
overridesproperty on a rule suppresses inherited rules from superconcepts. - TextGen / typesystem error messages: wrap smodel expressions that render types with
<...>presentation:error "Expected " + <expectedType> + " but got " + <actualType> -> node;. Avoid rawtoString. when concrete (typeof(expr) as v) { ... }defers a block until the type is fully resolved — use it before deciding whether to report an error or assign a result type.- Quick fixes (
TypesystemQuickFix) are roots, not executed automatically — the user triggers them via the UI. Wire them into a report through thehelginsIntentionslot (TypesystemIntentionwrapper withquickFixref +actualArguments). Seereferences/quick-fixes.md. - Reusable helper code (utility classes, shared algorithms) can live as a plain BaseLanguage
ClassConceptroot directly in the typesystem model. No separate utility module is required. - For MPS-typed return types (
sequence<node<X>>,list<node<X>>) the Java parser gives backList<SNode>— fix permps-model-manipulation/references/variable-declarations.md. - Edit typesystem models through MPS MCP tools (
mps_mcp_insert_root_node_from_json,mps_mcp_update_node,mps_mcp_parse_java_and_insert). Do not hand-edit.mpsfiles. - After edits run
mps_mcp_check_root_node_problems, compile the language, and test on sample models.
What ships with it
8 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 6d ago First seen · 61 lines · 133 tokens per session scan A ce66e9929c61
mps-aspect-typesystem is a skill published in the GitHub repository JetBrains/MPS (1,658 stars, last pushed yesterday), licensed Apache-2.0. It adds 133 tokens to every session and 2,129 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
langium
A comprehensive skill to understanding how Langium-based projects work — from grammar definition through code generation, runtime parsing, linking, validation, and LSP integration.
lai-gen-evals
Expand and refine the evaluation suite for a Langium DSL project. Generates comprehensive eval files that cover syntactic correctness, semantic validity, user intent matching, edge cases, and language understanding.
lai-gen-mcp
Generate a Model Context Protocol (MCP) server that exposes a Langium DSL's parser and validator as an MCP tool, allowing any MCP-compatible client to validate DSL code and receive diagnostics.
lai
Guide for using the langium-ai (LAI) CLI to generate language descriptors, synthesize system prompts, run evaluations, and iteratively refine AI-powered tooling in Langium projects. Use when working with lai commands, descriptors, or evaluation files.
lai-gen-descriptor
Generate or refine a language descriptor for a Langium DSL project. Bootstraps a new descriptor via lai gen descriptor if none exists, then guides refinement of paths, services, examples, documentation, and structure.
lai-gen-language-skill
Skill for generating a skill for understanding a specific Langium-based DSL. Used in cooperation with the lai & langium skills for understanding.