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skill_add_assets

Add multiple asset files to a skill in a single operation, using a shared overwrite policy to efficiently scaffold and initialize project resources.

Instructions

function_purpose: Bulk add multiple assets to a skill.

Description:

  • Convenience wrapper over add_skill_asset for efficiency when scaffolding several files.

  • Applies a shared overwrite policy (individual entries may still be rejected if invalid).

IMPORTANT: After adding assets, you should ALWAYS create a note (via skill_store_note) documenting:

  • What each asset contains and its purpose

  • When and why an agent should load/use them

  • Any context needed to understand them

  • Example usage patterns if applicable

Args:

  • name: str Skill name

  • assets: list[dict] Each: {path: str, content: str, encoding?: "text"|"base64"}

  • overwrite: bool Allow overwriting existing files

Returns:

  • list of result dicts (see add_skill_asset).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYes
assetsYes
overwriteNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

No annotations are provided, so the description carries the full burden. It discloses that a shared overwrite policy applies, that individual entries may be rejected if invalid, and that a follow-up note is mandatory. It also mentions the return type, offering a clear behavioral picture for a mutation tool.

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 well-structured with clear sections (function_purpose, description, important note, args, returns). It is somewhat lengthy due to the IMPORTANT note, but every sentence earns its place, especially the follow-up documentation requirement.

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?

The description covers the tool's operation, validation behavior, and return type reference. It points to add_skill_asset for return details, which is acceptable given the output schema exists. It could detail partial failure behavior more explicitly, but it mentions individual rejection.

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 input schema has no descriptions (0% coverage), but the Args section explains each parameter: name, assets with detailed item structure (path, content, encoding), and overwrite as a boolean. This adds 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 'Bulk add multiple assets to a skill,' clearly stating the verb and resource. It explicitly differentiates from the sibling tool 'skill_add_asset' by identifying itself as a 'convenience wrapper' for efficiency, making its purpose distinct.

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 says it is for 'efficiency when scaffolding several files,' which indicates when to use it. It does not explicitly say 'use skill_add_asset for a single asset,' but the contrast with the sibling tool implies this. The shared overwrite policy and rejection behavior provide additional usage context.

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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