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get_skill_details

Retrieve detailed information about a specific skill, including its full SKILL.md content, file path, and a recursive list of all files in the skill directory. Choose to return content, file path, or both.

Instructions

Get detailed information about a specific skill.

This tool provides the full SKILL.md content and/or file path, along with a recursive list of all files contained within the skill's directory. LLMs should use get_skill_related_file() to read the content of specific files.

Parameters

skill_name : str The name of the skill (from get_available_skills). return_type : str Type of data to return: "content" (default), "file_path", or "both". - "content": Returns only the SKILL.md content as text - "file_path": Returns only the absolute path to SKILL.md - "both": Returns both content and file path in a dict

Returns

dict[str, any] Dictionary containing: - skill_content: Full text or path of SKILL.md (based on return_type) - files: List of relative file paths in the skill directory

Raises

ValueError If the skill is not found or return_type is invalid.

Examples

details = get_skill_details("single-cell-rna-qc", return_type="content") print(details["files"]) ['SKILL.md', 'scripts/qc_analysis.py', ...]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
skill_nameYes
return_typeNoboth
Behavior4/5

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

With no annotations, description carries full burden. It discloses return types (content/file_path/both), file list, and error conditions. It does not explicitly state read-only nature but implies it through output description.

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?

Well-structured with sections (Parameters, Returns, Raises, Examples). Slightly lengthy but all content is relevant and earned. Front-loaded purpose.

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?

Given no output schema, description fully documents return dict structure and exception. Relationship to siblings is clear. No gaps identified.

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?

Schema has 0% description coverage; description fully explains both parameters: skill_name (source from get_available_skills) and return_type (with options and defaults). Adds significant value beyond 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?

Description clearly states the tool retrieves detailed info about a specific skill. It distinguishes from siblings: get_available_skills lists skills, get_skill_related_file reads file contents.

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?

Description advises to use get_skill_related_file to read specific files, providing practical guidance on tool selection. However, it does not explicitly state when not to use this tool.

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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