Official agent skill

Mps Aspect Dataflow

A skill your agent uses when defining or debugging MPS dataflow builders for a concept — control/data flow declarations that drive reachability analysis and variable-use checking.

OfficialApache-2.0Auto-check passedDevelopment

Install Mps Aspect Dataflow

skills CLI

$ npx skills add JetBrains/MPS --skill mps-aspect-dataflow -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install JetBrains/MPS mps-aspect-dataflow --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/JetBrains/MPS.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/mps-aspect-dataflow .claude/skills/mps-aspect-dataflow && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Paste into Claude Code

Install the "mps-aspect-dataflow" agent skill from https://github.com/JetBrains/MPS/tree/master/.agents/skills/mps-aspect-dataflow into .claude/skills/mps-aspect-dataflow/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mps-aspect-dataflow", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/JetBrains/MPS/tree/master/.agents/skills/mps-aspect-dataflow

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add JetBrains/MPS --skill mps-aspect-dataflow -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install JetBrains/MPS mps-aspect-dataflow --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/JetBrains/MPS.git skills-src && mkdir -p .agents/skills && cp -r skills-src/.agents/skills/mps-aspect-dataflow .agents/skills/mps-aspect-dataflow && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "mps-aspect-dataflow" agent skill from https://github.com/JetBrains/MPS/tree/master/.agents/skills/mps-aspect-dataflow into .agents/skills/mps-aspect-dataflow/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mps-aspect-dataflow", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add JetBrains/MPS --skill mps-aspect-dataflow -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install JetBrains/MPS mps-aspect-dataflow --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/JetBrains/MPS.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/.agents/skills/mps-aspect-dataflow .cursor/skills/mps-aspect-dataflow && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "mps-aspect-dataflow" agent skill from https://github.com/JetBrains/MPS/tree/master/.agents/skills/mps-aspect-dataflow into .cursor/skills/mps-aspect-dataflow/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mps-aspect-dataflow", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/JetBrains/MPS.git --path .agents/skills/mps-aspect-dataflow

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add JetBrains/MPS --skill mps-aspect-dataflow -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install JetBrains/MPS mps-aspect-dataflow --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/JetBrains/MPS.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/.agents/skills/mps-aspect-dataflow .gemini/skills/mps-aspect-dataflow && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "mps-aspect-dataflow" agent skill from https://github.com/JetBrains/MPS/tree/master/.agents/skills/mps-aspect-dataflow into .gemini/skills/mps-aspect-dataflow/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mps-aspect-dataflow", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install JetBrains/MPS mps-aspect-dataflow

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add JetBrains/MPS --skill mps-aspect-dataflow -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/JetBrains/MPS.git skills-src && mkdir -p .github/skills && cp -r skills-src/.agents/skills/mps-aspect-dataflow .github/skills/mps-aspect-dataflow && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "mps-aspect-dataflow" agent skill from https://github.com/JetBrains/MPS/tree/master/.agents/skills/mps-aspect-dataflow into .github/skills/mps-aspect-dataflow/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mps-aspect-dataflow", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add JetBrains/MPS --skill mps-aspect-dataflow -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install JetBrains/MPS mps-aspect-dataflow --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/JetBrains/MPS.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/.agents/skills/mps-aspect-dataflow .opencode/skills/mps-aspect-dataflow && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "mps-aspect-dataflow" agent skill from https://github.com/JetBrains/MPS/tree/master/.agents/skills/mps-aspect-dataflow into .opencode/skills/mps-aspect-dataflow/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mps-aspect-dataflow", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: mps-aspect-dataflow
GitHub stars: 1.7k
Token cost: ~1.9k tokens
SKILL.md length: 836 words
Files: 17 (incl. references)
Skills in repo: 40
Repo updated: Oct 7, 2026
First seen: Oct 8, 2026
Licence: Apache-2.0

At a glance

A skill your agent uses when defining or debugging MPS dataflow builders for a concept — control/data flow declarations that drive reachability analysis and variable-use checking.

Works in 6 steps: Create the dataflow model with… → Create a DataFlowBuilderDeclaration root… → Add a BuilderBlock child with a body (BL… → …
Debugging MPS dataflow builders for a concept — control/data flow declarations that drive reachability analysis and variable-use checking
SKILL.md covers Loading companion skills, Mental Model, Critical Directives and Common-Path Workflow, plus 2 more sections
Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Mps Aspect Dataflow is an agent skill from JetBrains/MPS, published by the product's own GitHub organization. Use when defining or debugging MPS dataflow builders for a concept — control/data flow declarations that drive reachability analysis and variable-use checking. Covers DataFlowBuilderDeclaration, BuilderBlock, emit instructions (code for, jump, ifjump, label, read, write, ret, mayBeUnreachable), positions (AfterPosition, BeforePosition, LabelPosition), the jetbrains.mps.lang.dataFlow language, the NodeParameter implicit, BL+smodel usage inside builder bodies, and IBuilderMode for advanced analyses such as…

Its SKILL.md is about 1.9k tokens, which your agent loads only when the skill is triggered. The skill folder holds 18 other files, including reference files (for example `references/aspect-model-stereotypes.md`, `references/baselanguage-builder-index.md` and `references/concept-catalog.md`).

It sits in Development. It works with JetBrains IDEs. The repository describes itself as: JetBrains Meta programming System. The licence is Apache-2.0.

When your agent uses it

Debugging MPS dataflow builders for a concept — control/data flow declarations that drive reachability analysis and variable-use checking

Example prompts

“/mps-aspect-dataflow”

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

Create the dataflow model with mps_mcp_create_model (moduleName: "", modelName: ".dataFlow") if absent. The aspect ID is dataFlow…
Create a DataFlowBuilderDeclaration root node; set conceptDeclaration to the concept being described; give it a name.
Add a BuilderBlock child with a body (BL StatementList).
Emit instructions: delegate to children with EmitCodeForStatement; model branches with EmitIfJumpStatement + EmitLabelStatement; record…
For loops, use BeforePosition/AfterPosition to encode loop-back/exit edges; wrap potentially-unreachable instructions in…
Validate with mps_mcp_check_root_node_problems. For tricky cases, inspect an existing baseLanguage builder via mps_mcp_print_node with…

What it can do on your machine

Read from SKILL.md and the folder at commit a92f944. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Mps Aspect Dataflow loads about 1.9k tokens when it runs, and up to ~12k if it reads all its reference files. Until then it costs about 139 tokens; SKILL.md has 836 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~139
When it runs · the whole SKILL.md, loaded when a task matches: ~1.9k
With references · SKILL.md plus every file in references/, read only if the agent opens them: ~12k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from JetBrains/MPS at commit a92f944, republished under its Apache-2.0 licence (© JetBrains). 836 words, ~1,917 tokens.

Download SKILL.mdSave it as .claude/skills/mps-aspect-dataflow/SKILL.md (or your agent's skills folder). This skill also uses 16 other files; get the full folder from GitHub.

name: mps-aspect-dataflow
description: Use when defining or debugging MPS dataflow builders for a concept — control/data flow declarations that drive reachability analysis and variable-use checking. Covers DataFlowBuilderDeclaration, BuilderBlock, emit instructions (code for, jump, ifjump, label, read, write, ret, mayBeUnreachable), positions (AfterPosition, BeforePosition, LabelPosition), the jetbrains.mps.lang.dataFlow language, the NodeParameter implicit, BL+smodel usage inside builder bodies, and IBuilderMode for advanced analyses such as nullable/non-null tracking.
type: reference

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 semantics: the jump is taken when the condition is FALSE. Use it after code for node.condition to model branching.
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.

Common-Path Workflow

Create the dataflow model with mps_mcp_create_model (moduleName: "<lang>", modelName: "<lang>.dataFlow") if absent. The aspect ID is dataFlow — camelCase, case-sensitive, no @ suffix; spelling it dataflow (lowercase) produces a utility model that MPS will not recognise. See aspect-model-stereotypes.md. Add jetbrains.mps.lang.dataFlow (and transitively jetbrains.mps.baseLanguage) as used languages on the model.
Create a DataFlowBuilderDeclaration root node; set conceptDeclaration to the concept being described; give it a name.
Add a BuilderBlock child with a body (BL StatementList).
Emit instructions: delegate to children with EmitCodeForStatement; model branches with EmitIfJumpStatement + EmitLabelStatement; record variable use with EmitReadStatement / EmitWriteStatement; mark exits with EmitRetStatement.
For loops, use BeforePosition/AfterPosition to encode loop-back/exit edges; wrap potentially-unreachable instructions in EmitMayBeUnreachable.
Validate with mps_mcp_check_root_node_problems. For tricky cases, inspect an existing baseLanguage builder via mps_mcp_print_node with deep: true.

Show full SKILL.md (275 more words)Show less

mps-model-manipulation — BL + smodel code inside builder bodies (DotExpression, SLinkAccess, NodeParameter, behavior method calls); for a builder body open only references/dot-expression-basics.md in the mps-model-manipulation skill root after loading that companion skill from the same origin.
mps-aspect-behavior — for behavior methods called from builders to compute target nodes (e.g. getLoopOrSwitch, getReturnJumpTarget).
mps-aspect-typesystem — when the dataflow you emit must agree with type checks.
mps-node-editing — generic JSON-blueprint node creation/replacement workflow.

Reference Index

Start here — most common case: writing one builder for one concept → read only references/json-patterns.md (the verified blueprint shapes), plus references/concept-catalog.md when you need an exact concept/role name; a builder that validates but analyses wrongly → only references/rules-and-pitfalls.md.

Concept catalog — open when you need exact concept names, properties, children, cardinalities, or abstract bases for DataFlowBuilderDeclaration, BuilderBlock, every emit statement, every position type, and the abstract bases. See references/concept-catalog.md.
Verified JSON patterns — open when constructing or editing a builder as JSON for mps_mcp_* tools. Includes variable read, single-child delegation, return-with-finally, if/elsif/else, while loop, assignment, variable declaration, break to ancestor, and a custom inverted-condition statement. See references/json-patterns.md.
Rules and pitfalls — open when a builder validates but the engine reports surprising reachability or variable-use results, when choosing between AfterPosition and LabelPosition, or before using modes/IBuilderMode. See references/rules-and-pitfalls.md.
Language setup and engine invocation — open when wiring a new dataflow model, or when debugging "why is my builder not being called". See references/setup-and-engine.md.
BaseLanguage builder index — open when you want to study a known-good builder for IfStatement, WhileStatement, ReturnStatement, BreakStatement, VariableDeclaration, TryFinallyStatement, SwitchStatement, ForStatement, etc. Lists builder names and persistent nodeReferences in r:00000000-0000-4000-0000-011c895902c2. See references/baselanguage-builder-index.md.
Sample language reference — open when looking for a minimal complete builder for a custom language (UnlessStatement_DataFlow). See references/sample-language-reference.md.

Files

SKILL.md and 16 other files (references) in .agents/skills/mps-aspect-dataflow of JetBrains/MPS.

SKILL.md
references/aspect-model-stereotypes.md
references/baselanguage-builder-index.md
references/concept-catalog.md
references/json-patterns.md
references/json-patterns/pattern-1-variable-read.md
references/json-patterns/pattern-2-minimal-delegation.md
references/json-patterns/pattern-3-return-statement.md
references/json-patterns/pattern-4-if-statement.md
references/json-patterns/pattern-5-while-loop.md
references/json-patterns/pattern-6-assignment.md
references/json-patterns/pattern-7-concept-is-the-variable.md
references/json-patterns/pattern-8-exit-to-enclosing-container.md
references/json-patterns/pattern-9-inverted-condition.md
references/rules-and-pitfalls.md
references/sample-language-reference.md
references/setup-and-engine.md

Open the folder on GitHubat commit a92f944

Compare with similar skills

Mps Aspect Dataflow next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Mps Aspect Dataflow compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Mps Aspect Dataflow this skillJetBrains/MPS	1.7k	—	~1.9k	Automated safety check: Pass	Apache-2.0	today
Doc SyncJetBrains/ideavim	10k	2 repos	~2.6k	Automated safety check: Pass	MIT	today
ChangelogJetBrains/ideavim	10k	1 repos	~2.7k	Automated safety check: Pass	MIT	today
Youtrack CommunityJetBrains/intellij-community	21k	—	~3.2k	Automated safety check: Notes	Custom licence	today
Roo Conflict Resolutionzgsm-ai/costrict	4.4k	—	~2.3k	Automated safety check: Pass	Apache-2.0	7 days ago
Extensions API MigrationJetBrains/ideavim	10k	—	~1.7k	Automated safety check: Pass	MIT	today

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Works with

JetBrains IDEs

Questions about Mps Aspect Dataflow

What does Mps Aspect Dataflow do?

A skill your agent uses when defining or debugging MPS dataflow builders for a concept — control/data flow declarations that drive reachability analysis and variable-use checking. Mps Aspect Dataflow is an agent skill from JetBrains/MPS, published by the product's own GitHub organization. Use when defining or debugging MPS dataflow builders for a concept — control/data flow declarations that drive reachability analysis and variable-use checking.

When should I use Mps Aspect Dataflow?

Mps Aspect Dataflow fits situations like: debugging MPS dataflow builders for a concept — control/data flow declarations that drive reachability analysis and variable-use checking.

How do I install Mps Aspect Dataflow in Claude Code?

Run `npx skills add JetBrains/MPS --skill mps-aspect-dataflow -a claude-code`. Or copy the skill folder (.agents/skills/mps-aspect-dataflow in JetBrains/MPS) into .claude/skills/mps-aspect-dataflow in your project. Claude Code loads it when a task matches its description.

How do I install Mps Aspect Dataflow in Codex?

Run `npx skills add JetBrains/MPS --skill mps-aspect-dataflow -a codex`. Or copy the skill folder (.agents/skills/mps-aspect-dataflow in JetBrains/MPS) into .agents/skills/mps-aspect-dataflow in your project. Codex loads it when a task matches its description.

Can I use Mps Aspect Dataflow in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add JetBrains/MPS --skill mps-aspect-dataflow -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/mps-aspect-dataflow, .gemini/skills/mps-aspect-dataflow, .github/skills/mps-aspect-dataflow and .opencode/skills/mps-aspect-dataflow in your project.

What does Mps Aspect Dataflow need to run?

SKILL.md names no scripts, command-line tools or credentials: Mps Aspect Dataflow is instructions for the agent only.

Does Mps Aspect Dataflow access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Mps Aspect Dataflow safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Mps Aspect Dataflow use?

Mps Aspect Dataflow is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Mps Aspect Dataflow use?

About 1.9k tokens (SKILL.md is roughly 7.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 9.7k tokens, read only when the agent opens those files.

What are the alternatives to Mps Aspect Dataflow?

Skills that share tags, products or a category with Mps Aspect Dataflow: Doc Sync (JetBrains/ideavim, 10k stars), Changelog (JetBrains/ideavim, 10k stars), Youtrack Community (JetBrains/intellij-community, 21k stars) and Roo Conflict Resolution (zgsm-ai/costrict, 4.4k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Mps Aspect Dataflow?

JetBrains (a GitHub organization, an official publisher) maintains it in JetBrains/MPS, which has 1,660 GitHub stars. The repository holds 40 skills in this directory. The repository was last updated on October 7, 2026.

Source: JetBrains/MPS on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Mps Aspect Dataflow

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Workflow steps

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Runs code

Network

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Context cost

Safety

SKILL.md

MPS Dataflow Aspect

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