mps-language-analysis

Analyze an MPS language by name — discover concepts, properties, references, children, aspects (editor/constraints/behavior), and metadata. Use when investigating an unfamiliar language, exploring concept structure, or finding sample nodes to use as templates for JSON blueprints.

Quality

100%

Does it follow best practices?

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Adds up to 20 points to the overall score

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Securityby

Passed

No findings from the security scan

Quality

Content

100%

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

A tight, action-oriented skill body with concrete tool calls, a clearly sequenced read-only workflow, and properly signaled one-level-deep reference files. Nothing is over-explained or buried in nested references.

Dimension	Reasoning	Score
Conciseness	Lean body that assumes MPS competence — no padding explaining what a concept or aspect is — with every section (Critical Directives, workflow, aspects, Reference Index) earning its place.	3 / 3
Actionability	Concrete, copy-paste-ready MCP calls with named parameters (e.g. mps_mcp_get_concept_details with languageRefs, mps_mcp_query_nodes with FIND_INSTANCES, sampleOnly: true) rather than abstract description.	3 / 3
Workflow Clarity	A clear numbered sequence (Verify → Retrieve → Extract → Drill Down) with an explicit confirmation checkpoint in step 1; read-only analysis, so destructive/batch feedback loops are not required.	3 / 3
Progressive Disclosure	Overview in SKILL.md with well-signaled one-level-deep references to the real references/search-concepts.md and references/concept-details.md files via a dedicated Reference Index section.	3 / 3
	Total	12 / 12 Passed

Description

100%

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, third-person description that pairs concrete capabilities with an explicit Use-when trigger clause. It is specific to the MPS domain and unlikely to conflict with sibling skills.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions — 'discover concepts, properties, references, children, aspects (editor/constraints/behavior), and metadata' — matching the multiple-specific-actions anchor.	3 / 3
Completeness	Explicitly states what it does (analyze a language by name, discover its structure) and includes an explicit 'Use when ...' trigger clause, satisfying both what and when.	3 / 3
Trigger Term Quality	Natural user phrasing is well covered: 'investigating an unfamiliar language, exploring concept structure, or finding sample nodes to use as templates for JSON blueprints' aligns with what a user would actually say.	3 / 3
Distinctiveness Conflict Risk	The MPS-language-by-name niche is narrow and its triggers ('unfamiliar language', 'concept structure', 'sample nodes as templates') are distinct, making mis-trigger unlikely.	3 / 3
	Total	12 / 12 Passed

Validation

93%

Warnings & errors only

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

Validation — 15 / 16 Passed

Validation for skill structure

Criteria	Description	Result
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	15 / 16 Passed

Repository: JetBrains/MPS
Path: .agents/skills/mps-language-analysis/SKILL.md
Commit: 52c9d63

Reviewed: about 5 hours ago

Table of Contents

Discovery Implementation Validation

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