Skills

Skills are reusable, curated capabilities that combine remote tools with ready-to-use code, such as data analysis scripts or automation helpers. They are exposed to agents as tools whose names start with the skill_ prefix and are executed inside the Daytona sandbox alongside the built-in tools.

When an agent uses a skill tool, Vertesia:

• Tracks the skill in the conversation state, including its name, source URL, language, and required packages.
• Automatically syncs the skill's scripts into the Daytona sandbox under /home/daytona/skills/{skill_name}/.
• Installs any language and system packages declared by the skill so they are available for subsequent execute_shell calls.

This makes skills ideal for encapsulating complex logic (for example, spreadsheet analysis or ETL pipelines) without exposing their internal implementation to the model.

How Skills Work with Agents

Skills are discovered and registered as part of the tools available to the agent. From the model's perspective they behave like normal tools:

• The tool name has the form skill_<collection>_<action> (for example, skill_data_analysis_analyze_spreadsheet).
• The tool input schema defines the parameters the model must provide.
• The tool response can include both content and artifacts for downstream processing.

When a skill tool is executed:

1. The agent invokes the skill by name (for example, skill_data_analysis_analyze_spreadsheet).
2. The remote tools infrastructure (see Custom Tools) runs the skill and returns a result.
3. Vertesia records the skill usage in the conversation state and syncs its scripts into the Daytona sandbox.
4. Subsequent execute_shell calls can import and reuse those scripts from /home/daytona/skills/{skill_name}/, with any declared packages already installed.

This pattern lets you keep heavy lifting (for example, Python + pandas data analysis) in versioned skill collections while the LLM focuses on orchestration.

Typical Skill Use Cases

Common categories of skills include:

• Data analysis skills – Analyze, transform, and summarize structured data or spreadsheets using Python and popular libraries.
• Code and scripting skills – Execute shell automation or TypeScript scripting tasks that are too complex or specialized to express directly via prompts.
• Document processing skills – Implement advanced parsing, enrichment, or extraction workflows over documents fetched from the knowledge base.
• Math and utility skills – Provide robust numerical and unit-conversion helpers beyond what the base model offers reliably.

Examples from the built-in skills server include:

• data-analysis/analyze-data, etl-pipeline, analyze-spreadsheet, and create-spreadsheet for Python + pandas workflows.
• data-analysis/make-chart for teaching the model how to emit chart JSON specs inside ```chart code blocks that the UI renders as interactive charts.
• document/zip-archives for working with zipfiles whose text rendition is an index of contained files, showing how to download the .zip into the sandbox, unzip it with unzip, and then analyze the extracted contents with other skills.

From the agent's perspective, each of these skills is just another tool that can be chained with built-in tools such as search_documents, fetch_document, write_artifact, and execute_shell.

Authoring Skills

Skills are hosted by tool servers built with the Vertesia tools SDK and registered as tool collections on applications. The same mechanisms used for Custom Tools apply:

• A tool server exposes a set of tools and skills over HTTP or MCP.
• Each skill includes metadata about its scripts, language, and required packages.
• When the agent uses a skill, Vertesia uses this metadata to sync scripts and install dependencies into the sandbox.

For a practical starting point, see the Vertesia tools server examples and the @vertesia/tools-sdk documentation used by the platform's own skills.