MPS: Skill for Claude Code

.agents/skills/mps-aspect-dataflow/SKILL.md

mps-aspect-dataflow is a skill for Claude Code, Codex from JetBrains/MPS. It costs 116 tokens per session (2,433 once invoked), scanned A, original, Apache-2.0.

Rules for describing how execution and data move through nodes in an MPS language. MPS is JetBrains' tool for building custom programming languages.

In plain words
What is it for?
It helps define dataflow builders, control-flow paths, reads and writes, jumps, labels, returns, and potentially unreachable sections for language concepts.
Why use it?
It lets MPS detect unreachable code, variables used before they are set, and other flow-related problems based on the paths a language declares.

Skill for Claude CodeCodex ✓ vendor

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

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 →

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-aspect-dataflow/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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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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Your own site · 80×15
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Per session 116 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,433 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 pass 7 Sept 2026
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 $0.00116 $0.02433
Opus 5.5 $0.00046 $0.00973
Sonnet 5.5 $0.00023 $0.00487
Haiku 4.5 $0.00012 $0.00243

Measured 8d ago against content hash 81dba61dae94, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

mps-aspect-dataflow 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 8d 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.

.agents/skills/mps-aspect-dataflow/SKILL.md · 72 lines

How it starts

The opening of the file, as written. The whole thing — 72 lines — stays where its author put it; the contents beside it link to each section on GitHub.

MPS Dataflow Aspect

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 dataflow aspect (jetbrains.mps.lang.dataFlow, l:7fa12e9c-b949-4976-b4fa-19accbc320b4) lets a language describe how control and data flow through nodes of a concept. MPS uses that information for reachability analysis, uninitialised-variable checks, and (via IBuilderMode) richer flow analyses such as nullable tracking.

Mental Model

Each DataFlowBuilderDeclaration answers: given a node of concept X, in what order might execution visit its children, and which variables are read or written?

The builder body is a BL StatementList (inside a BuilderBlock). You use normal BL control flow (if, foreach, local variables) to compute which emit instructions to output at runtime. The node implicit parameter (concept NodeParameter) is always in scope — its type is the concept referenced by conceptDeclaration, giving smodel-typed access to children and references.

The MPS dataflow engine builds a control-flow graph from the emitted instructions, then runs analyses (unreachable code, uninitialised variable reads) on that graph. The engine only traces the paths you declare; if no builder exists for a concept, MPS falls back to delegating all children in declaration order.

Critical Directives

  • code for is delegation, not a call. Never use jump for child delegation. Use EmitCodeForStatement to inline a child's own builder at this point.
  • ifjump is a two-way branch; by BaseLanguage's convention it jumps when the condition is FALSE. Emit it after code for node.condition, towards the else branch, to model an if; a builder may use it the other way (|| jumps when its left side is true).
  • An if's else branch is a BlockStatement wrapping a StatementList. IfStatement.ifFalseStatement rejects a bare StatementList; see references/json-patterns/pattern-4-if-statement.md.
  • Labels are node references, not string lookups. EmitLabelStatement.name is display-only; jumps reference the label node via LabelPosition.label. Two labels with the same string are still distinct targets.
  • Always null-guard 0..1 children with BL if (node.child != null) before emitting code for node.child.
  • write node vs write node.link: use write node (just NodeParameter as the variable expression) when the concept node is the variable being declared. Use write node.link (via SLinkAccess) when the node merely references the variable being written.
  • Leaf concepts need no builder. The engine treats unbuilt concepts as no-ops, or for ordinary parents delegates to children in declaration order.
  • Do not hand-edit serialized .mps dataflow files. Use MPS MCP node tools.

Read the full file on GitHub · 72 lines

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. 8d ago Changed · +1 lines 81dba61dae94
  2. 11d ago Changed · +12 lines bd322a6f8e9b
  3. 1mo ago Changed · +6 lines bbb0f5662a66
  4. 2mo ago First seen · 53 lines · 116 tokens per session scan A 2a0d200ccdcc

Subscribe to this mod's changes

mps-aspect-dataflow is a skill published in the GitHub repository JetBrains/MPS (1,666 stars, last pushed yesterday), licensed Apache-2.0. It adds 116 tokens to every session and 2,433 once invoked, about $0.0005 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-aspect-dataflow compared with skills from other repositories

mps-aspect-dataflow next to 6 skills from other repositories
ModTypeStarsLast pushLicenceScanTokensWorks with
mps-aspect-dataflowJetBrains/MPSSkill1.7kyesterdayApache-2.0A116Claude Code, Codex
langiumeclipse-langium/langium-aiSkill314d agoMITA33Claude 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
dotnet-reversezhaoxuya520/reverse-skillSkill44k1mo agoMITA144Claude Code
dart-run-static-analysisflutter/agent-pluginsSkill3.2k2d agoBSD-3-ClauseA43Claude Code