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skill_get_detail

Retrieve a skill's complete parsed details including frontmatter, markdown body, and contextual notes. Get full guidance and metadata to understand and apply the skill correctly.

Instructions

function_purpose: Get full parsed details for a specific skill by name (frontmatter + body + notes).

Description:

  • Returns the complete parsed skill including frontmatter fields and the markdown body content.

  • By default, appends all notes from the _notes/ directory to provide complete context including learnings, improvements, corrections, and examples discovered while using the skill.

Args:

  • name: str The hyphen-case name of the skill (must match the skill directory name)

  • include_notes: bool If True (default), append notes from _notes/ to the body for complete context

  • markdown_output: bool If True, return formatted markdown string instead of JSON dict (default: False)

Returns:

  • If markdown_output=False: dict containing name, description, license?, allowed_tools?, metadata?, path, body

  • If markdown_output=True: formatted markdown string with frontmatter and body

Usage:

  • Use this when the agent needs the full guidance text and metadata for a skill.

  • Notes are included by default to ensure the agent sees all relevant context, corrections, and examples.

  • Set include_notes=False only if you want just the core SKILL.md content without historical notes.

  • Set markdown_output=True to get a readable markdown document instead of JSON structure.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYes
include_notesNo
markdown_outputNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

With no annotations, the description carries the full burden. It discloses the default note-appending behavior, the output formats (JSON dict vs markdown string), and the content of the returned dict. This gives a clear picture of what the tool does without relying on 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 structured into clear sections (Description, Args, Returns, Usage), with the purpose stated upfront. While somewhat lengthy, every sentence provides necessary detail and there is no redundancy except a slight overlap between the 'function_purpose' line and the first Description sentence.

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?

The description covers all relevant aspects: what the tool returns (list of dict fields), how to control output, and when to use it. It is complete for a tool with three parameters and one required, especially given the existing output schema.

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

Parameters5/5

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

The Args section thoroughly explains each parameter: name, include_notes, and markdown_output, including default values and effects. This fully compensates for the 0% schema description coverage, adding significant meaning beyond the bare schema.

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 opens with 'Get full parsed details for a specific skill by name', which is a specific verb+resource combination. It clearly distinguishes this tool from siblings like skill_list_all and skill_read_asset by focusing on fetching a single skill's parsed content.

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

Usage Guidelines4/5

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

The Usage section provides explicit guidance: 'Use this when the agent needs the full guidance text and metadata for a skill.' It also advises when to set include_notes=False and markdown_output=True. However, it does not explicitly name alternatives or state when not to use this tool versus sibling tools like skill_read_asset.

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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