Skill Builder

Skill Builder

This skill provides guidance for creating effective skills for our Pydantic AI agents.

About Skills

Skills are modular, self-contained packages that extend the agent's capabilities by providing specialized knowledge, workflows, and tools. They are dynamically loaded into the agent's context using the agent-skills plugin.

What Skills Provide

1. Specialized workflows - Multi-step procedures for specific domains
2. Tool integrations - Instructions for working with specific file formats or APIs
3. Domain expertise - Specific knowledge, schemas, business logic

Anatomy of a Skill

universal_skills/skills/skill-name/
├── SKILL.md (required)
│   ├── YAML frontmatter (required): Contains metadata including `name`, `description`, `license`, `tags`, and `metadata`. This tells the agent when to load the skill. The description must be comprehensive and outline exactly *when* the skill should be triggered.
│   └── Markdown body (required): The actual prompt instructions dictating how the agent should utilize the skill's bundled scripts or documentation.
└── Bundled Resources (optional)
    ├── scripts/          - Executable code (Python/Bash/etc.)
    └── docs/             - Documentation intended to be loaded into context as needed

SKILL.md (required)

Every SKILL.md consists of:

• Frontmatter (YAML): Contains name, skill_type (skill for an atomic skill — use workflow only via the skill-workflow-builder), description, license, tags, and metadata (author, version). This tells the system when to load the skill and pass it as a tool to the agent. skill_type is how the installer and the atomicity gate classify the capability.
• Body (Markdown): The actual prompt instructions dictating how the agent should utilize the skill's bundled scripts or documentation. Keep it concise.

Bundled Resources (optional)

Scripts (scripts/)

Executable scripts (e.g. scripts/main.py). The agent executes these to interact with the system or external APIs without rewriting code from scratch.

Docs/References (docs/ or references/)

Supplemental markdown files. These are not loaded into the system prompt by default but can be read by the agent if it determines it needs more information (e.g., docs/api_reference.md). Keeps the main SKILL.md token-efficient.

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Best Practices for Agent Skills

1. Optimize for Discoverability

The name and description in frontmatter are all the agent sees before triggering.

• Naming: 1–64 characters, lowercase, numbers, and hyphens. Must match parent directory.
• Description: (Max 1,024 chars). Describe in third person. Include "negative triggers" (when NOT to use).

2. Progressive Disclosure

Maintain a clean context by loading details only when needed.

• 500-Line Limit: Keep SKILL.md under 500 lines. Focus on high-level navigation.
• Flat Directories: Keep resources exactly one level deep (e.g., references/api.md).
• Just-in-Time (JiT) Loading: Explicitly tell the agent WHEN to read a specific reference file.

3. Procedural Instructions

• Step-by-Step Numbering: Use strict chronological sequences.
• Third-Person Imperative: Use direct commands (e.g., "Extract the text..." instead of "I will extract...").
• Identical Terminology: Use consistent domain-native terms (e.g., "template" instead of "html").

4. Deterministic Scripts

Offload fragile or repetitive logic to tested scripts in scripts/. Returnhuman-readable stdout/stderr so the agent can self-correct.

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Skill Creation Process

Skill creation involves these steps:

1. Understand the skill with concrete examples
2. Plan reusable skill contents (scripts, references, assets)
3. Initialize the skill (run init_skill.py)
4. Edit the skill (implement resources and write SKILL.md)
5. Package the skill (run package_skill.py)
6. Iterate based on real usage

Follow these steps in order, skipping only if there is a clear reason why they are not applicable.

Step 1: Understanding the Skill with Concrete Examples

Skip this step only when the skill's usage patterns are already clearly understood. It remains valuable even when working with an existing skill.

To create an effective skill, clearly understand concrete examples of how the skill will be used. This understanding can come from either direct user examples or generated examples that are validated with user feedback.

For example, when building an image-editor skill, relevant questions include:

• "What functionality should the image-editor skill support? Editing, rotating, anything else?"
• "Can you give some examples of how this skill would be used?"
• "I can imagine users asking for things like 'Remove the red-eye from this image' or 'Rotate this image'. Are there other ways you imagine this skill being used?"
• "What would a user say that should trigger this skill?"

To avoid overwhelming users, avoid asking too many questions in a single message. Start with the most important questions and follow up as needed for better effectiveness.

Conclude this step when there is a clear sense of the functionality the skill should support.

Step 2: Planning the Reusable Skill Contents

To turn concrete examples into an effective skill, analyze each example by:

1. Considering how to execute on the example from scratch
2. Identifying what scripts, references, and assets would be helpful when executing these workflows repeatedly

Example: When building a pdf-editor skill to handle queries like "Help me rotate this PDF," the analysis shows:

1. Rotating a PDF requires re-writing the same code each time
2. A scripts/rotate_pdf.py script would be helpful to store in the skill

Example: When designing a frontend-webapp-builder skill for queries like "Build me a todo app" or "Build me a dashboard to track my steps," the analysis shows:

1. Writing a frontend webapp requires the same boilerplate HTML/React each time
2. An assets/hello-world/ template containing the boilerplate HTML/React project files would be helpful to store in the skill

Example: When building a big-query skill to handle queries like "How many users have logged in today?" the analysis shows:

1. Querying BigQuery requires re-discovering the table schemas and relationships each time
2. A references/schema.md file documenting the table schemas would be helpful to store in the skill

To establish the skill's contents, analyze each concrete example to create a list of the reusable resources to include: scripts, references, and assets.

Step 3: Initializing the Skill

At this point, it is time to actually create the skill.

Skip this step only if the skill being developed already exists, and iteration or packaging is needed. In this case, continue to the next step.

When creating a new skill from scratch, always run the init_skill.py script. The script conveniently generates a new template skill directory that automatically includes everything a skill requires, making the skill creation process much more efficient and reliable.

Usage:

scripts/init_skill.py  --path 

The script:

• Creates the skill directory at the specified path
• Generates a SKILL.md template with proper frontmatter and TODO placeholders
• Creates example resource directories: scripts/, references/, and assets/
• Adds example files in each directory that can be customized or deleted

After initialization, customize or remove the generated SKILL.md and example files as needed.

Step 4: Edit the Skill

When editing the (newly-generated or existing) skill, remember that the skill is being created for another agent instance to use. Include information that would be beneficial and non-obvious to the agent. Consider what procedural knowledge, domain-specific details, or reusable assets would help another agent instance execute these tasks more effectively.

Learn Proven Design Patterns

Consult these helpful guides based on your skill's needs:

• Multi-step processes: See references/workflows.md for sequential workflows and conditional logic
• Specific output formats or quality standards: See references/output-patterns.md for template and example patterns

These files contain established best practices for effective skill design.

Start with Reusable Skill Contents

To begin implementation, start with the reusable resources identified above: scripts/, references/, and assets/ files. Note that this step may require user input. For example, when implementing a brand-guidelines skill, the user may need to provide brand assets or templates to store in assets/, or documentation to store in references/.

Added scripts must be tested by actually running them to ensure there are no bugs and that the output matches what is expected. If there are many similar scripts, only a representative sample needs to be tested to ensure confidence that they all work while balancing time to completion.

Any example files and directories not needed for the skill should be deleted. The initialization script creates example files in scripts/, references/, and assets/ to demonstrate structure, but most skills won't need all of them.

Update SKILL.md

Writing Guidelines: Always use imperative/infinitive form.

Frontmatter

Write the YAML frontmatter following the universal-skills standard:

• name: The skill name (kebab-case)
• description: This is the primary triggering mechanism for your skill, and helps the agent understand when to use the skill. It is the only thing your agent sees when deciding which skill to load.
• Goal: Give your agent just enough info to know what it does and when/why to trigger it.
• Format: Max 1024 chars, written in third person.
• Structure: First sentence explains what it does. Second sentence starts with "Use when [specific triggers]".
• Good Example: "Comprehensive document creation and analysis. Use when the agent needs to work with professional documents (.docx files) or when the user mentions PDFs, forms, or text extraction."
• Bad Example: "Helps with documents." (Gives the agent no way to distinguish this from other document skills).
• license: MIT
• tags: A list of relevant keywords to aid in discovery or search (e.g. [agent, documentation, architecture]).
• metadata: A set of metadata containing key details.
• author: The author's name.
• version: The starting version, typically '0.1.21' aligning with universal-skills release version.

Do not include any other fields in YAML frontmatter unless instructed.

kg-* skill suite only (CONCEPT:AU-ECO.mcp.kg-skill-verb-coverage). Skills in the graph-os kg-* suite carry two extra fields so the kg-coverage-doctor gate can diff them 1:1 against the MCP verb surface:

• tier: one of core | modality | meta | surface. core/modality skills wrap a

graph-os verb; meta/surface skills (routers, builders, webui) are exempt from the verb-mapping check.

• wraps: (optional) a list of MCP verbs this skill fronts, when it is not a simple 1:1

kg-→graph_ slug match — e.g. wraps: [graph_ingest, source_sync, source_drain, source_connector, document_process] on kg-ingest. Omit it when the slug already implies the verb (kg-query → graph_query).

Body

Write instructions for using the skill and its bundled resources.

Step 5: Packaging a Skill

Once development of the skill is complete, it must be packaged into a distributable .skill file that gets shared with the user. The packaging process automatically validates the skill first to ensure it meets all requirements:

scripts/package_skill.py 

Optional output directory specification:

scripts/package_skill.py  ./dist

The packaging script will:

1. Validate the skill automatically, checking:

• YAML frontmatter format and required fields
• Skill naming conventions and directory structure
• Description completeness and quality
• File organization and resource references

1. Package the skill if validation passes, creating a .skill file named after the skill (e.g., my-skill.skill) that includes all files and maintains the proper directory structure for distribution. The .skill file is a zip file with a .skill extension.

If validation fails, the script will report the errors and exit without creating a package. Fix any validation errors and run the packaging command again.

Step 6: Iterate

After testing the skill, users may request improvements. Often this happens right after using the skill, with fresh context of how the skill performed.

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Validation Guide

Before finalizing, validate the skill with an LLM:

1. Discovery Validation

Test if the description triggers correctly. Prompt: > I am building an Agent Skill. Based strictly on this YAML frontmatter: [Paste name/description] > 1. Generate 3 user prompts that should trigger this skill. > 2. Generate 3 user prompts that sound similar but should NOT trigger it. > 3. Critique the description for breadth/specificity.

2. Logic Validation

Ensure instructions are complete and non-ambiguous. Prompt: > Here is my SKILL.md and directory tree. [Paste contents] > Act as an agent that just triggered this. Simulate execution for [Specific Task]. > Flag any "Execution Blockers" where you are forced to guess or hallucinate.

3. Edge Case Testing

Prompt: > Act as a ruthless QA tester. Your goal is to break this skill. > Ask me 3–5 challenging questions about failure states, missing fallbacks, or environment assumptions.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: Knuckles-Team
• Source: Knuckles-Team/universal-skills
• License: MIT

Install and usage instructions live in the source repository linked above.