SKILL.md Lint by Moltline

Packaging Check

packaging_check

Read-onlyIdempotent

Validate a zip layout plan for a skill marketplace before packaging. FREE.

Checks SKILL.md placement rules and flags junk files. Typical input {"file_paths": ["my-skill/SKILL.md", "my-skill/examples.md"], "marketplace": "agensi"} returns {"verdict": "pass" | "fail", "issues": ["..."], "skill_files_found": ["my-skill/SKILL.md"]}.

Use on a planned file layout, before zipping. Not for the content of a skill file (lint_skill). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""} (for example {"error": "file_paths is empty — list every file that will be inside the zip"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`file_paths`	Yes	Every file path that will be inside the zip, relative to the zip root, e.g. ["my-skill/SKILL.md"]; at least 1 entry.
`marketplace`	No	Rule set to apply: "agensi" (SKILL.md at root or one folder deep) or "generic" (junk-file checks only). Default "agensi".	agensi

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4.5/5.0

Behavior5/5

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

Beyond annotations (readOnlyHint, idempotentHint, destructiveHint: false), the description discloses that the tool returns error messages instead of raising protocol errors, that it is safe to retry after correcting input, and provides full example error output. No contradictions with annotations.

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 efficiently structured: starts with single-line purpose and cost, then example, usage guidance, error behavior. Two minor areas reduce score: the example could be slightly leaner ('Typical input...') and the error notes repeat the JSON format. Still highly effective.

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

Completeness4/5

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

Given 2 parameters, full schema coverage, and an output schema, the description largely completes the picture with usage guidance and error behavior. The output schema exists but is not referenced; describing return value types briefly could push completeness to 5.

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?

Schema description coverage is 100%, so the schema already documents both parameters thoroughly. The description adds a concrete input example but does not add additional semantics beyond the schema's property descriptions.

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 states a specific verb ('Validate'), resource ('zip layout plan for a skill marketplace'), and context ('before packaging'). It also distinguishes from the sibling tool lint_skill by noting it checks layout, not content.

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 explicitly says when to use ('on a planned file layout, before zipping') and when not to use ('Not for the content of a skill file – lint_skill'). This provides clear guidance.

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

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TDQS

A4.5/5.0

Disambiguation5/5

Each tool addresses a distinct concern: cron expressions, text diffs, JSON validation, SKILL.md linting, zip packaging, and regex testing. There is no overlap in purpose or output, so an agent can unambiguously select the right tool for a given task.

Naming Consistency5/5

All tool names follow a consistent snake_case verb_noun pattern (cron_explain, diff_texts, json_validate, lint_skill, packaging_check, regex_test). The pattern is uniform and predictable, making the API easy to navigate.

Tool Count5/5

Six tools is well within the ideal 3-15 range for a focused server. Each tool serves a clear purpose, and the count feels neither bloated nor sparse for a linting and validation toolkit.

Completeness4/5

The core domain of validating SKILL.md files is well covered by lint_skill and packaging_check, and the generic utilities (cron, diff, JSON, regex) support auxiliary validation needs. A minor gap is the lack of a tool to fetch or write files directly, but the read-only linting workflow is complete.

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