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-dsl-memorynpx skills add JetBrains/MPS --skill mps-dsl-memorygit 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-dsl-memory)<a href="https://agentmods.dev/skills/jetbrains/mps/mps-dsl-memory"><img src="https://agentmods.dev/badge/skills/jetbrains/mps/mps-dsl-memory.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 | $0.00049 | $0.01189 |
| Opus 5 | $0.00024 | $0.00594 |
| Sonnet 5 | $0.00010 | $0.00238 |
| Haiku 4.5 | $0.00005 | $0.00119 |
Grade A, and why
mps-dsl-memory 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 5d 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 — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.
When to Use
- A new MPS sample or DSL project has no generated DSL skill under
.agents/skills/<dsl-name>-dsl/, or the existing one is outdated. - The user says "bootstrap DSL skill", "generate DSL skill", or "document this DSL".
- You have just explored a DSL and want to persist your findings for future sessions as a project-local skill.
Prerequisites
- MPS MCP tools are available.
- The project has one or more language modules and at least one sandbox, example solution, or editable model that exercises the DSLs.
- Use MPS MCP tools for discovery and model edits. Do not hand-edit serialized
.mpsXML.
Workflow
- Discover languages: Call
mps_mcp_get_project_structurewithincludeModels: false. Identify all editable DSL language modules and derive each concept-tools language ref asl:<uuid>:<languageName>. - Choose skill scope: Prefer one generated skill per language. Use one combined skill only when the project languages are tightly coupled and users normally edit them together.
- Find examples: For each language or language group, call
mps_mcp_get_project_structurewith the relevant sandbox/example solution asstartingPointandincludeRootNodes: true. Record editable model refs and representative root refs. - Read concepts: Call
mps_mcp_get_concept_detailswith the relevantl:<uuid>:<languageName>refs. Capture rootable concepts, concrete children, properties, child roles, references, and usefulshortDescriptiontext. - Sample sparingly: Use
mps_mcp_print_nodeonly on representative roots or subtrees needed for reference targets, required roles, or reusable blueprints. Avoid dumping every root. - Generate DSL skills: Create or update
.agents/skills/<dsl-name>-dsl/for each selected scope. Preserve user-added notes unless they are stale or wrong. - Cross-link related skills: When generated skills cover languages that extend, depend on, or are commonly used inside each other, add short links and usage notes between them.
- Verify: Confirm linked reference files exist, blueprint JSON parses, no
.DS_Storeor editor artifacts were added, and one or two recorded node refs still resolve if the sandbox may have changed.
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.
- 5d ago First seen · 76 lines · 49 tokens per session scan A c128faa20b8e
mps-dsl-memory is a skill published in the GitHub repository JetBrains/MPS (1,658 stars, last pushed today), licensed Apache-2.0. It adds 49 tokens to every session and 1,189 once invoked, about $0.0002 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.
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