Skip to main content
Glama

propose_skill

Submit a skill file to AutoVault after running validation, security scan, and three-tier deduplication.

Instructions

Submit a newly authored SKILL.md to AutoVault. Runs validation, security scan, capability cross-check, and three-tier deduplication (exact content hash → near-exact similarity → functional overlap). Use when the user asks to save a conversationally created skill, or after you've drafted one in response to a workflow the user wants reused. Always prefer this over writing skill files directly to disk.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
skill_mdYes
resourcesNo
source_sessionNo
allow_synthesized_frontmatterNo
checkNo
verboseNo
Behavior4/5

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

With no annotations, the description fully discloses that the tool runs validation, security scan, capability cross-check, and three-tier deduplication. This reveals significant behavioral traits beyond a simple save, such as potential rejection or merging. It does not, however, specify error behavior or what happens on duplicate detection.

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 concise at four sentences, with the primary action and key checks in the first sentence. Every sentence adds value, and the structure is front-loaded for quick understanding.

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

Completeness2/5

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

Given the complexity (6 parameters, no output schema), the description lacks parameter explanations and output details. While core usage is clear, an agent cannot reliably construct calls without parameter semantics or understand return values, which is a significant gap.

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

Parameters1/5

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

Schema coverage is 0%, and the description explains none of the 6 parameters beyond 'SKILL.md' for skill_md. Terms like resources, source_session, and allow_synthesized_frontmatter remain undefined, leaving the agent without guidance on how to populate them.

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 submits a SKILL.md to AutoVault and lists the specific checks performed (validation, security scan, etc.). It distinguishes itself from alternatives by explicitly recommending it over writing skill files directly to disk, implying it is for new skills, contrasting with siblings like update_skill.

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 says to use when saving a conversationally created skill or after drafting one for reuse. It also advises preferring this over direct file writes. However, it does not explicitly exclude use cases for updating existing skills, where update_skill might be more appropriate.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/autoworks-ai/autovault'

If you have feedback or need assistance with the MCP directory API, please join our Discord server