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.
Borrowing it
Nothing to install: this file belongs to JetBrains/MPS. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/JetBrains/MPS/master/.agents/skills/mps-language-modularity/SKILL.mdgit 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-language-modularity)<a href="https://agentmods.dev/skills/jetbrains/mps/mps-language-modularity"><img src="https://agentmods.dev/badge/skills/jetbrains/mps/mps-language-modularity/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/jetbrains/mps/mps-language-modularity"><img src="https://agentmods.dev/badge/skills/jetbrains/mps/mps-language-modularity.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00179 | $0.01963 |
| Opus 5.5 | $0.00072 | $0.00785 |
| Sonnet 5.5 | $0.00036 | $0.00393 |
| Haiku 4.5 | $0.00018 | $0.00196 |
Grade A, and why
mps-language-modularity 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 3d 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 — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MPS Language Modularity & Composition
Loading companion skills
Companion names in this skill are lazy dependencies: load only those relevant to the current task. If this skill came from an MCP server, use the host's skill loader to resolve the companion's unique discovered entry URI on the same host-assigned originating server. If the host has no server-backed skill loader, stop and report that limitation; do not silently fall back to a filesystem copy. If this skill came from a filesystem catalog, load the named sibling from that same catalog at <skills-root>/<skill-name>/SKILL.md, even if remote skill loaders are also available. Do not invent a tool name or server endpoint.
The design tier for multi-language MPS work: before authoring aspects, decide how the languages relate. This skill turns a 2×2 taxonomy into a decision procedure and routes each choice into the aspect skills that implement it.
The MPS-specific lesson: syntax composition is the easy part. MPS is projectional — no parser, no grammar to become ambiguous — so combining notations needs far less work than in parser-based tools (only alias clashes and completion UX still need a glance). The real difficulty is semantic: scopes, type systems, generator contracts, and migration. Choosing a modularity kind is mostly choosing where the dependency lives and how generated code interacts.
Critical directives
- Spend effort on semantics, not syntax. Scopes, type bridges, generator contracts, and migration are where modular languages break.
- Two questions decide the kind (below). Answer them before touching any aspect.
- MPS "extends" ≠ the design category. At the module level MPS calls almost any inter-language use "extension"; that tool-level dependency is independent of whether your design is referencing/extension/reuse/embedding. See
references/design-principles.md. - References vs. containment is the structural fork. Model a foreign element as a reference → referencing. Contain a foreign concept as a child → inline composition (extension vs embedding). Hard to reverse — pick deliberately.
- Reuse must be designed in. Abstract hooks, behavior contracts, and generator placeholders must exist before you need them; after-the-fact reuse (especially interwoven generation) is severely limited.
- The exported surface is an API. Behavior-method names, interface concepts, placeholder IDs, mapping labels, and generated abstract signatures are cross-language contracts — stabilize, version, and test them.
What ships with it
6 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.
- 3d ago Changed 258f227e2f61
- 1mo ago Changed · +4 lines bcba04ac0fc2
- 2mo ago First seen · 80 lines · 179 tokens per session scan A d0678570b76d
mps-language-modularity is a skill published in the GitHub repository JetBrains/MPS (1,666 stars, last pushed yesterday), licensed Apache-2.0. It adds 179 tokens to every session and 1,963 once invoked, about $0.0007 per session on Opus 5.5. 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.
mps-language-modularity compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| mps-language-modularityJetBrains/MPS | Skill | 1.7k | yesterday | Apache-2.0 | A | 179 | Claude Code, Codex |
| langiumeclipse-langium/langium-ai | Skill | 31 | 4d ago | MIT | A | 33 | Claude Code |
| lai-gen-evalseclipse-langium/langium-ai | Skill | 31 | 4d ago | MIT | A | 42 | Claude Code |
| laieclipse-langium/langium-ai | Skill | 31 | 4d ago | MIT | A | 52 | Claude Code |
| lai-gen-descriptoreclipse-langium/langium-ai | Skill | 31 | 4d ago | MIT | A | 49 | Claude Code |
| lai-gen-mcpeclipse-langium/langium-ai | Skill | 31 | 4d ago | MIT | A | 43 | Claude Code |
| lai-gen-language-skilleclipse-langium/langium-ai | Skill | 31 | 4d ago | MIT | A | 33 | Claude Code |