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mps-aspect-textgen

Use when authoring or debugging MPS TextGen — concept-to-plain-text serialisation for languages whose final output is text (source files, config, scripts, DDL, XML, markdown). Covers `ConceptTextGenDeclaration` roots, `append` / `indent buffer` / `with indent` statements, text layout areas, context objects (e.g. imports sets), attributes, the base text gen component (`LanguageTextGenDeclaration`), binary `write`, `found error`, and the indentation-buffer model. Reach for this skill whenever the task involves editing `<lang>/languageModels/textGen.mps`.

Quality

86%

Does it follow best practices?

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SecuritybySnyk

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The risk profile of this skill

SKILL.md
Quality
Evals
Security

Quality

Content

78%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A well-engineered reference skill: the body carries only high-value, non-inferable rules and routes everything else through clearly signaled one-level-deep references. The main improvements are inline JSON payload examples for the common insert path and an explicit validate-fix-revalidate loop in the workflow. No bundle files were present to verify the referenced paths exist.

Suggestions

Include one minimal copy-paste-ready `mps_mcp_insert_root_node_from_json` JSON payload for a file-generating `ConceptTextGenDeclaration` directly in the workflow step, keeping the full vocabulary in references/json-blueprints.md.

Add an explicit feedback loop to workflow step 6, e.g. "If `mps_mcp_check_root_node_problems` reports errors, fix and re-check before rebuilding" rather than relying solely on references/common-failures.md.

Deduplicate the used-languages prerequisite: state the three required languages once (either the prerequisite paragraph or workflow step 1) and cross-reference the other location.

DimensionReasoningScore

Conciseness

The body is dense with non-obvious, MPS-specific knowledge (concept-exact dispatch, the depth-counter indentation model, required used languages) and avoids explaining general concepts, but the model-creation/three-used-languages prerequisite is stated twice — once in the prerequisite paragraph and again nearly verbatim in workflow step 1 — so minor trimming is possible.

4 / 5

Actionability

Concrete, near-executable guidance throughout: named MCP tools with parameter values ("`mps_mcp_create_model` with `moduleName: "<lang>"` and `modelName: "<lang>.textGen"`") and exact statement forms; it falls short of fully copy-paste-ready because complete JSON payload shapes are delegated to references/json-blueprints.md rather than shown inline.

4 / 5

Workflow Clarity

The Common-Path Workflow is a clearly sequenced six-step process with a pre-flight prerequisite, named failure modes ("unresolved-concept errors"), and an explicit validation step ("Validate with `mps_mcp_check_root_node_problems`, rebuild the language module, regenerate any consumer"), but an explicit fix-and-re-validate feedback loop is not stated in the workflow itself — error recovery is routed to references/common-failures.md.

4 / 5

Progressive Disclosure

Exemplary structure: a "Start here — most common case" routing line, eight one-level-deep references each signaled with an "Open X when Y" trigger, and a Related Skills section giving each companion's purpose; the body stays an overview while details (AST shapes, worked examples, failure modes) live in references. Note: no references/ directory was present in the evaluated workspace, so referenced paths could not be verified on disk.

5 / 5

Total

17

/

20

Passed

Description

92%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A strong description: it states a precise capability inventory, gives explicit "Use when" triggers including the exact model-file path, and carves out a distinct niche. The only gap is a few natural trigger synonyms (e.g., "text generation") that would broaden recall.

DimensionReasoningScore

Specificity

The description enumerates multiple concrete capabilities — "`ConceptTextGenDeclaration` roots, `append` / `indent buffer` / `with indent` statements, text layout areas, context objects (e.g. imports sets), attributes, the base text gen component (`LanguageTextGenDeclaration`), binary `write`, `found error`, and the indentation-buffer model" — comprehensive coverage with no notable gaps.

5 / 5

Completeness

Explicitly answers both what ("concept-to-plain-text serialisation for languages whose final output is text" plus the enumerated constructs) and when ("Use when authoring or debugging MPS TextGen... whenever the task involves editing textGen.mps") with concrete trigger phrases.

5 / 5

Trigger Term Quality

Good natural keyword coverage — "authoring or debugging", "TextGen", "editing `<lang>/languageModels/textGen.mps`", plus output formats "source files, config, scripts, DDL, XML, markdown" — but misses common synonyms such as "text generation" or "generate text from models", fitting the 4 anchor rather than 5's synonym-plus-extension comprehensiveness.

4 / 5

Distinctiveness Conflict Risk

A clear niche — the MPS TextGen aspect for text-output languages — with distinct triggers (TextGen, textGen.mps) and minimal conflict risk even against closely related MPS aspect skills.

5 / 5

Total

19

/

20

Passed

Validation

81%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 13 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

relative_links

Relative link issues: 1 missing

Warning

referenced_paths_exist

Referenced path issues: 20 missing

Warning

Total

13

/

16

Passed

Repository

JetBrains/MPS

Path

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

Reviewed

Table of Contents

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