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Skill Specifications

Skill specifications define reusable capabilities that agents can use, implemented as Python modules.

Overview​

Skills are Python modules that provide specific capabilities to agents. Each skill spec defines the module path, dependencies, and required environment variables.

Required Fields​

id (string)​

Unique identifier (kebab-case, e.g., data-analysis, code-generation)

name (string)​

Display name shown in the UI

description (string)​

Skill capabilities description

module (string)​

Python module path where the skill is implemented

module: agent_skills.data_analysis
module: agent_skills.code_generation
module: agent_skills.web_search

Optional Fields​

envvars (list of strings)​

Required environment variable IDs. References specs in envvars/ directory.

envvars:
  - OPENAI_API_KEY
  - GITHUB_TOKEN

optional_env_vars (list of strings)​

Optional environment variables that enhance functionality

optional_env_vars:
  - PLOT_DPI
  - CHART_THEME
  - LOG_LEVEL

dependencies (list of strings)​

Required Python packages with version constraints

dependencies:
  - pandas>=2.0.0
  - matplotlib>=3.7.0
  - seaborn>=0.12.0
  - numpy>=1.24.0

tags (list of strings)​

Categorization tags

tags:
  - data
  - analysis
  - visualization

icon (string)​

UI icon identifier

emoji (string)​

UI emoji identifier

Complete Example​

# Copyright (c) 2025-2026 Datalayer, Inc.
# Distributed under the terms of the Modified BSD License.

id: data-analysis
name: Data Analysis Skill
description: Perform statistical analysis and data visualization

module: agent_skills.data_analysis

envvars:
  - OPENAI_API_KEY

optional_env_vars:
  - PLOT_DPI
  - CHART_THEME

dependencies:
  - pandas>=2.0.0
  - matplotlib>=3.7.0
  - seaborn>=0.12.0
  - numpy>=1.24.0
  - scipy>=1.10.0

tags:
  - data
  - analysis
  - visualization
  - statistics

Skill Hierarchy​

Skill Specification
├── Basic Info (id, name, description, tags)
├── Implementation (module)
├── Environment
│   ├── envvars (required environment variables)
│   └── optional_env_vars (optional variables)
├── Dependencies (Python packages)
└── UI (icon, emoji)

Linking Skills to Agents​

Skills are referenced in agent specifications by their ID:

# In agent spec
skills:
  - data-analysis
  - code-generation
  - web-search

Best Practices​

  1. ID Format: Use kebab-case (data-analysis, not data_analysis)
  2. Module Naming: Use Python package naming conventions (agent_skills.module_name)
  3. Dependencies: Pin major versions to avoid breaking changes (pandas>=2.0.0)
  4. Environment Variables: Clearly separate required vs optional
  5. Tags: Use consistent tags across related skills
  6. Description: Focus on what the skill enables the agent to do
  7. Version Constraints: Use >= for minimum versions, avoid overly restrictive pinning
  8. Documentation: Ensure the module path points to existing Python code