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Designesy

by io.github.LE-VAI

designesy_skill_md

Get the Designesy SKILL.md — the agent-skill-format export of the design-system contract, written as behavioral rules an AI coding agent can drop into .agents/skills/ or a system prompt. Use this when you want the contract in a form that steers how an agent builds UI (tokens, anti-patterns, behavioral rules, verification). When NOT to use: for the raw contract JSON, use designesy_contract; for scoring, use designesy_score. Read-only — no side effects. Returns markdown text (SKILL.md format) — drop into .agents/skills/ or paste into a system prompt. No parameters.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.8/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden and does so effectively. It explicitly states 'Read-only — no side effects' and describes the return value ('Returns markdown text (SKILL.md format)'). This covers the key behavioral aspects for an agent selecting the tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is mostly concise and front-loaded with the primary purpose, but it repeats the 'drop into .agents/skills/ or a system prompt' detail in both the first and last sentences. This slight redundancy prevents a perfect score.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite the tool's simplicity (no parameters, no output schema), the description provides all necessary context: what the tool returns, the format, read-only behavior, and when to use it versus alternatives. The context is complete for selection and correct invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has zero parameters and the description notes 'No parameters.' Since there are no parameters, the schema covers 100% of the (empty) set, and the description doesn't need to add parameter semantics. Baseline of 4 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly identifies the tool's function: retrieving the Designesy SKILL.md, an agent-skill-format export of the design-system contract. It uses a specific verb ('Get') and resource, and explicitly distinguishes it from siblings like designesy_contract (raw JSON) and designesy_score (scoring).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives explicit usage context: 'Use this when you want the contract in a form that steers how an agent builds UI.' It also provides clear exclusions with named alternatives: 'When NOT to use: for the raw contract JSON, use designesy_contract; for scoring, use designesy_score.'

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Designesy toolsInspect
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TDQS

A4.7/5.0
Disambiguation5/5

Each tool has a clearly scoped purpose, and the extensive 'When NOT to use' notices cleanly separate the many scoring variants (e.g., score, drift, readiness, monitor, tokens, motion, a11y). Even similar informational endpoints (contract, skill, llms) are differentiated by format and use case. No two tools appear to do the same thing.

Naming Consistency5/5

All tools follow a consistent 'designesy_' prefix, and scoring tools uniformly append '_score' (e.g., drift_score, tokens_score, monitor_score). Non-score tools use descriptive noun suffixes (catalog, contract, report, guardrails). The pattern is predictable and uniform throughout.

Tool Count4/5

At 17 tools, the set is slightly above the ideal 3-15 range, but the breadth of the design-system intelligence domain justifies the count. Each scoring variant targets a different artifact (live URL, token file, Lottie, temporal drift) and the informational endpoints serve distinct formats. The tool count is heavy but not bloated.

Completeness5/5

The toolset covers the full assessment lifecycle: full audit (score), drift and temporal governance (drift_score, monitor_score), AI readiness (readiness_score), token and motion validation (tokens_score, motion_score), accessibility framework (a11y_score), diff (compare), composite report (report), guardrails generation, and multiple discovery formats (catalog, contract, skill_md, llms). No obvious dead ends exist; each tool leads to a usable artifact or clear next step.

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