MPS: Skill for Claude Code

.agents/skills/mps-mcp-workflow/SKILL.md

mps-mcp-workflow is a skill for Claude Code, Codex from JetBrains/MPS. It costs 83 tokens per session (6,406 once invoked), scanned A, original, Apache-2.0.

A complete workflow guide for JetBrains MPS projects, including models, custom languages, generators, validation, and the tools used to edit them. MPS is a visual, structured environment for creating programming languages and code generators.

In plain words
What is it for?
Use it to create and modify MPS languages, models, generators, and nodes; run validation; preserve model identifiers; and use the MPS integration tools correctly.
Why use it?
It prevents unsafe direct editing of MPS’s XML files and helps developers select the correct workflow and tool for each kind of change.

Skill for Claude CodeCodex ✓ vendor

Written for no agent in particular: nothing here depends on one. Also seen: mentions CLAUDE.md; installed under .agents/ (shared by several agents); mentions AGENTS.md.

This is JetBrains/MPS's own configuration. It tells Claude Code and Codex how to work on MPS itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything MPS configures →

Needs its repository: it runs a file that does not travel with it, so clone the repository first. The line is python3 scripts/mps_dump.py node /var/folders/.../mps-node-123.json "Score Reading" \.

About the project

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.

JetBrains/MPS · 1,666 stars · on GitHub · jetbrains.com

Reuse

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.

Copy the file
curl -O https://raw.githubusercontent.com/JetBrains/MPS/master/.agents/skills/mps-mcp-workflow/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/JetBrains/MPS

Made for: Claude Code, Codex.

Wrote 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.

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README.md
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Your own site
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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.

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Per session 83 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 6,406 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 4 findings, up to high

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • high Anti-Refusal · line 26
    Skill instructs the agent to omit warnings, disclaimers, or ethical commentary. Stripping safety caveats hides risk from the user and is a common jailbreak preamble.
    Fix: Remove instructions that suppress warnings, disclaimers, or ethical commentary. Let the agent surface safety-relevant caveats to the user.
  • medium Prompt Injection · line 19
    Subtle instructions detected that may alter agent decision-making or introduce hidden biases.
    Fix: Review content for implicit steering or bias. Ensure instructions are explicit and align with the skill's stated purpose.
  • medium Agent Snooping · line 41
    Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.
    Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
  • medium Agent Snooping · line 45
    Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.
    Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00083 $0.06406
Opus 5.5 $0.00033 $0.02562
Sonnet 5.5 $0.00017 $0.01281
Haiku 4.5 $0.00008 $0.00641

Measured 2d ago against content hash 1907d4393069, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

mps-mcp-workflow 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 2d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/mps_dump.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

.agents/skills/mps-mcp-workflow/SKILL.md · 183 lines

How it starts

The opening of the file, as written. The whole thing — 183 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Projectional Agent Toolkit – JetBrains MPS for Agents

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.

Entry point for working with JetBrains MPS (Meta Programming System) models, languages, generators, and the MPS MCP tools.

Read this together with AGENTS.md whenever the task involves MPS artifacts or MPS MCP tooling.

Critical Directives

⚠️ STOP — If you are reading raw .mps or .mpl XML files, you are way off track. MPS model files are opaque serialized XML: you cannot safely understand or edit them as plain text, and doing so will corrupt the model. Use mps_mcp_* tools instead. If they are not available, ask the user to start MPS and enable the MPS MCP server before continuing.

  • Pass projectPath on every mps_mcp_* call, starting with the first one. The platform routes every call by resolving projectPath to an open project, so a call without it is rejected before dispatch ("Unable to determine the target project…") and costs a turn. Pass the open MPS project's base directory — a path at or inside it, never an ancestor such as the repository root. If you are not yet aware of that path, pass the session's current working directory (the one you already have — do not spend a turn discovering it); if that is rejected, take the path from the rejection message, which lists the open projects (Currently open projects: {...}). mps_mcp_list_open_projects reports the same value as mpsProjectBaseDirectory when it can run. Reuse that one path for every later call in the session.
  • Empty Currently open projects: {"projects":[]} is the welcome screen, not a bad path. Retrying mps_mcp_* cannot succeed until some project is open. Load the mps-project-management companion skill from the same origin and open the project via CLI.
  • Always prefer MPS MCP tools over hand-editing .mps / .mpl XML. Hand-edits silently corrupt model files.
  • Before editing a language aspect, load its aspect skill. Changing typesystem rules → mps-aspect-typesystem (follow its Common-Path step 9: live-check the rules on a scratch sandbox); constraints → mps-aspect-constraints; behavior → mps-aspect-behavior; editor → mps-aspect-editor; generator → mps-aspect-generator; structure → mps-aspect-structure-concepts; any other aspect → mps-language-aspects-overview. The directives below do not replace the aspect skill.
  • Preserve node IDs. Prefer mps_mcp_update_root_node_from_json or surgical edits (mps_mcp_update_node, …) over delete-and-reinsert. Deleting destroys persistent IDs and breaks incoming references.
  • Copy-then-modify beats reconstruct-from-JSON. To create a node that closely resembles an existing one, duplicate it with mps_mcp_alter_nodes COPY_NODE and adjust the copy with mps_mcp_update_node, instead of printing the original and re-authoring it as a JSON blueprint. The copy is guaranteed structurally valid and needs no blueprint authoring at all.
  • Generate deep blueprints, do not hand-nest them. If you write a blueprint as JSON rather than through mps_mcp_parse_java_and_insert, and it holds a BaseLanguage body or nests more than ~5 node levels, generate it with mps-node-editing's scripts/blueprint.py and pass the printed temp-file path. Load the mps-node-editing companion skill from the same origin to run it; its "Scripts" section is the contract. Hand-counted ]} runs are the usual cause of INVALID_JSON … unbalanced. Helper scripts and generated JSON go under $TMPDIR, never into the project or another directory of the user.
  • Rebuild after compiled-aspect changes. After modifying typesystem, constraints, behavior, editor, or any other compiled aspect, rebuild the language module with mps_mcp_alter_nodes MAKE before validating with mps_mcp_check_root_node_problems on a consumer model. A MAKE answering runtimeReady: true is the reload; mps_mcp_reload_all compiles nothing (it reloads the classes already on disk) and adds nothing after it. Without a rebuild, cached compiled rules continue to fire — checking will report stale errors or miss new ones.
  • The Structure-to-Aspect/Scaffold Prerequisite Chain (Ordering & Recovery). When editing a language's structure aspect (e.g., creating concepts/enums or modifying properties, children, or references via mps_mcp_alter_structure), you MUST adhere to the following strict sequence of cross-tool steps before invoking dependent tools (such as mps_mcp_get_concept_details or mps_mcp_scaffold_editor):
    1. Structure Edit: Modify the structure aspect (using mps_mcp_alter_structure).
    2. Clean Make/Rebuild: Run mps_mcp_alter_nodes with MAKE and rebuild=true targeting the language module (a model-scoped make can leave a never-before-deployed language's runtime hollow). Note: a plain mps_mcp_reload_all alone is not sufficient because the generated structure aspect descriptor classes on disk must be regenerated before the ClassLoader reloads them.
    3. Verify Descriptors: Retrieve the compiled concept details using mps_mcp_get_concept_details and verify that the descriptorStatus is not "hollow" and no warnings exist.
    4. Scaffold / Aspect Editing: Once the descriptors are verified as compiled and active, proceed to scaffold the editor (mps_mcp_scaffold_editor) or define other aspects (behavior, constraints, typesystem, generator, etc.). Failing to follow this ordering results in stale or hollow descriptors where dependent tools silently see empty properties, children, or references. A MAKE writes only generated output (source_gen/classes_gen), changes no model, and is allowed in a task that forbids model edits. Tools that need the runtime now answer LANGUAGE_NOT_BUILT for a concept of an unbuilt language, with details.languageBuild.recovery naming the exact MAKE call to run. Shortcut: mps_mcp_alter_structure CREATE_CONCEPTS with make: true already does steps 2–3 — it clean-rebuilds, verifies every created descriptor against the runtime, and auto-recovers a never-deployed language (one module-scoped rebuild, recoveryStage: "module-rebuild"). Trust its makeStatus; run the manual chain only for structure edits made through tools that do not make (UPDATE_*/RENAME_* operations, mps_mcp_update_node).
  • Surgical edits over full-root rewrites. When a single child changes, prefer mps_mcp_update_node over re-running mps_mcp_update_root_node_from_json on the whole root.
  • Never simulate the typesystem. To decide whether a type fits, what a slot requires, what an operator returns, or why a node has a type, call mps_mcp_query_types (IS_SUBTYPE, SUPERTYPES, COMMON_SUPERTYPE, COERCE, OPERATION_TYPE, EXPLAIN_TYPE) or mps_mcp_query_nodes (GET_TYPE, GET_EXPECTED_TYPE). Print a SubtypingRule / InferenceRule root only to change it, or to pick the sibling you COPY_NODE — not to predict what the engine will answer: even while authoring, ask the engine for the rest, including what your change does to operators and joins (OPERATION_TYPE, COMMON_SUPERTYPE). If an engine answer surprises you (null, false), ask EXPLAIN_TYPE; do not open the rule. See references/analysis-tools/type-queries.md for the operation table and caveats.
  • Never simulate the dataflow engine. To see the program a dataflow builder emits, which builder emitted an instruction, why a node is unreachable, which writes reach a read or which reads a write feeds, call mps_mcp_query_dataflow (PRINT_PROGRAM, REACHING_WRITES, REACHING_READS); do not print builder roots to predict the program, and do not hand-write a console probe of jetbrains.mps.lang.dataFlow. See references/analysis-tools/dataflow-queries.md.
  • Check the IDE log when the envelope and the effect disagree. When a tool reports success but the effect is missing, or a skill says an error "surfaces only in idea.log", call mps_mcp_read_ide_log with sinceMinutes covering the call window and minLevel="WARN" (no path needed, works for standalone MPS); references/ide-log.md has the recipe and the fallback for older servers.
  • Validate frequently. A successful insertion ("ok": true) does not mean the AST is semantically valid — always follow with mps_mcp_check_root_node_problems. Read warnings and details.uncheckedSubtrees, not just the data verdict: commented-out code and other IDontApplyTypesystemRules / ISkipConstraintsChecking subtrees are not checked, so a clean verdict over them proves nothing — ask IS_SUBTYPE. A problem may carry rules: [{rule, ruleReference, statement}]: rules[0].rule names the reporting rule — cite it instead of guessing; print ruleReference only to change that rule.

Read the full file on GitHub · 183 lines

Files

What ships with it

32 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.

Changes

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.

  1. 2d ago Changed · -2 lines 1907d4393069
  2. 8d ago Changed · +3 lines f82e364f7327
  3. 11d ago Changed · +1 lines 4f683bd9d7fb
  4. 29d ago Changed · +6 lines · +3 tokens per session 11c2801daee9
  5. 1mo ago Changed · +11 lines 3210919f7d3f
  6. 1mo ago Changed · +40 lines 6fc7df276b71
  7. 1mo ago Changed d62282def920
  8. 2mo ago First seen · 124 lines · 80 tokens per session scan A 991c5f14d5ae

Subscribe to this mod's changes

mps-mcp-workflow is a skill published in the GitHub repository JetBrains/MPS (1,666 stars, last pushed yesterday), licensed Apache-2.0. It adds 83 tokens to every session and 6,406 once invoked, about $0.0003 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.

Related

mps-mcp-workflow compared with skills from other repositories

mps-mcp-workflow next to 6 skills from other repositories
ModTypeStarsLast pushLicenceScanTokensWorks with
mps-mcp-workflowJetBrains/MPSSkill1.7kyesterdayApache-2.0A83Claude Code, Codex
langiumeclipse-langium/langium-aiSkill314d agoMITA33Claude Code
lai-gen-evalseclipse-langium/langium-aiSkill314d agoMITA42Claude Code
laieclipse-langium/langium-aiSkill314d agoMITA52Claude Code
lai-gen-descriptoreclipse-langium/langium-aiSkill314d agoMITA49Claude Code
lai-gen-mcpeclipse-langium/langium-aiSkill314d agoMITA43Claude Code
lai-gen-language-skilleclipse-langium/langium-aiSkill314d agoMITA33Claude Code