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get_available_skills

Discover all available AI skills by retrieving a list of skill names, descriptions, and paths. Use this tool to find which skills are accessible before requesting detailed information.

Instructions

Get an overview of all available skills.

This tool provides LLMs with a list of available skills and their use cases by parsing the frontmatter (YAML metadata) at the start of each SKILL.md file.

LLMs should rely on this tool to discover what skills are available before requesting detailed skill information.

Returns

list[dict[str, str]] List of skill metadata dictionaries, each containing: - name: The skill identifier (lowercase, hyphens only) - description: When and how to use this skill - path: Location of the skill directory

Examples

skills = get_available_skills() print(skills[0]["name"]) 'single-cell-rna-qc'

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

No annotations are provided, so the description carries the burden. It discloses that the tool parses frontmatter and returns a list of skill metadata with specific fields (name, description, path) and naming conventions (lowercase, hyphens only). This is sufficient for a read-only tool with no side effects.

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

Conciseness5/5

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

The description is well-structured with sections, a clear returns specification, and an example. Every sentence contributes useful information without redundancy.

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 the tool's simplicity (no parameters, no annotations, output schema exists), the description fully covers the tool's purpose, usage guidance, output format, and relationship to siblings. It is complete for an agent to correctly select and invoke the tool.

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 no parameters, and schema coverage is 100%, so no parameter information is needed. The description adds value by explaining what the output contains, but per guidelines, 0 parameters yields a baseline of 4.

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 states the tool provides an overview of all available skills by parsing SKILL.md frontmatter. It distinguishes from siblings (get_skill_details, get_skill_related_file) by focusing on discovery of skills rather than details or files.

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 description explicitly advises LLMs to use this tool before requesting detailed skill information, providing clear usage context. It doesn't explicitly mention when not to use, but the guidance is strong and alternatives are implied through sibling tools.

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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