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mo9652962-ai

skill-maintenance-mcp

by mo9652962-ai

Skill Validate

skill_validate

Validate skill directories by checking required frontmatter, scanning for corruption, detecting orphan references, and summarizing decision logs. Ensures skills are discoverable and well-formed.

Instructions

技能体检: frontmatter 必填(name/description/version)+ 损坏扫描 + 孤儿 reference(未挂链=不可发现)+ decision-log 概要。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
skill_pathYes技能目录绝对路径

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.9/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full behavioral burden. It lists what is checked but never states whether the operation is read-only, whether it mutates or repairs anything, or what side effects occur. A diagnostic tool implies no mutation, but nothing in the text confirms it.

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?

A single dense sentence that front-loads the purpose and packs four distinct checks via separators, with zero filler. It is terse but every clause earns its place by naming a concrete check.

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

Completeness3/5

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

An output schema exists, so return values need not be explained, and the checks are enumerated. What is missing is routing against the overlapping siblings, particularly skill_scan_corruption and the two decision-log tools, which the description neither differentiates from nor excludes.

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

Parameters3/5

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

There is a single parameter with 100% schema description coverage ('技能目录绝对路径' / absolute path to the skill directory), so the schema already documents it fully. The description adds no syntax, format, or constraint detail beyond the schema, which is the baseline expectation.

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

Purpose4/5

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

The description names a concrete verb (体检/validate) and enumerates the exact checks performed: required frontmatter fields, corruption scan, orphan references, and a decision-log summary. This tells the agent precisely what the tool inspects. However, it does not distinguish itself from skill_scan_corruption, which appears to cover one of the same checks.

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

Usage Guidelines2/5

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

There is no when-to-use or when-not-to-use guidance at all. With four siblings — skill_backup, skill_scan_corruption, skill_log_decision, skill_read_decisions — the agent must guess whether to call this aggregate validator or the narrower per-concern tools, especially since corruption scanning overlaps with skill_scan_corruption.

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