skill-creator

name	skill-creator
description	Guide for creating effective skills that extend agent capabilities. Use when creating new skills or updating existing ones with specialized knowledge, workflows, or tool integrations.

Skill Creator

This skill provides guidance for creating effective skills.

About Skills

Skills are modular, self-contained packages that extend agent capabilities by providing specialized knowledge, workflows, and tools. Think of them as "onboarding guides" for specific domains or tasks—they transform a general-purpose agent into a specialized agent equipped with procedural knowledge that no model can fully possess.

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 - Company-specific knowledge, schemas, business logic
4. Bundled resources - Scripts, references, and assets for complex and repetitive tasks

Core Principles

Concise is Key

The context window is a public good. Skills share the context window with everything else: system prompt, conversation history, other Skills' metadata, and the actual user request.

Default assumption: The agent is already very smart. Only add context it doesn't already have. Challenge each piece of information: "Does it really need this explanation?" and "Does this paragraph justify its token cost?"

Prefer concise examples over verbose explanations.

Set Appropriate Degrees of Freedom

Match the level of specificity to the task's fragility and variability:

High freedom (text-based instructions): Use when multiple approaches are valid, decisions depend on context, or heuristics guide the approach.

Medium freedom (pseudocode or scripts with parameters): Use when a preferred pattern exists, some variation is acceptable, or configuration affects behavior.

Low freedom (specific scripts, few parameters): Use when operations are fragile and error-prone, consistency is critical, or a specific sequence must be followed.

Think of the agent as exploring a path: a narrow bridge with cliffs needs specific guardrails (low freedom), while an open field allows many routes (high freedom).

Anatomy of a Skill

Every skill consists of a required SKILL.md file and optional bundled resources:

skill-name/
├── SKILL.md (required)
│   ├── YAML frontmatter metadata (required)
│   │   ├── name: (required)
│   │   └── description: (required)
│   └── Markdown instructions (required)
└── Bundled Resources (optional)
    ├── scripts/          - Executable code (Python/Bash/etc.)
    ├── references/       - Documentation loaded into context as needed
    └── assets/           - Files used in output (templates, icons, fonts, etc.)

SKILL.md (required)

Every SKILL.md consists of:

• Frontmatter (YAML): Contains name and description fields. These are the only fields the agent reads to determine when the skill gets used—be clear and comprehensive in describing what the skill is and when it should be used.
• Body (Markdown): Instructions and guidance for using the skill. Only loaded AFTER the skill triggers.

Bundled Resources (optional)

Scripts (scripts/)

Executable code (Python/Bash/etc.) for tasks that require deterministic reliability or are repeatedly rewritten.

• When to include: When the same code is being rewritten repeatedly or deterministic reliability is needed
• Example: scripts/rotate_pdf.py for PDF rotation tasks
• Benefits: Token efficient, deterministic, may be executed without loading into context

References (references/)

Documentation and reference material intended to be loaded as needed into context.

• When to include: For documentation the agent should reference while working
• Examples: references/finance.md for financial schemas, references/api_docs.md for API specifications
• Use cases: Database schemas, API documentation, domain knowledge, company policies
• Benefits: Keeps SKILL.md lean, loaded only when needed
• Best practice: If files are large (>10k words), include grep search patterns in SKILL.md

Assets (assets/)

Files not intended to be loaded into context, but used within the output produced.

• When to include: When the skill needs files that will be used in the final output
• Examples: assets/logo.png for brand assets, assets/template.pptx for PowerPoint templates
• Use cases: Templates, images, icons, boilerplate code, fonts, sample documents

What to Not Include

A skill should only contain essential files that directly support its functionality. Do NOT create extraneous documentation:

• README.md
• INSTALLATION_GUIDE.md
• QUICK_REFERENCE.md
• CHANGELOG.md

The skill should only contain information needed for an AI agent to do the job at hand.

Progressive Disclosure Design Principle

Skills use a three-level loading system to manage context efficiently:

1. Metadata (name + description) - Always in context (~100 words)
2. SKILL.md body - When skill triggers (<5k words)
3. Bundled resources - As needed (unlimited because scripts can be executed without reading)

Keep SKILL.md body to the essentials and under 500 lines. Split content into separate files when approaching this limit.

Key principle: When a skill supports multiple variations, frameworks, or options, keep only the core workflow and selection guidance in SKILL.md. Move variant-specific details into separate reference files.

Skill Creation Process

Step 1: Understanding with Concrete Examples

To create an effective skill, clearly understand concrete examples of how the skill will be used. Ask:

• "What functionality should the skill support?"
• "Can you give examples of how this skill would be used?"
• "What would a user say that should trigger this skill?"

Step 2: Planning Reusable Contents

Analyze each example by:

1. Considering how to execute the example from scratch
2. Identifying what scripts, references, and assets would be helpful

Example: When building a pdf-editor skill to handle "Help me rotate this PDF," a scripts/rotate_pdf.py script would be helpful.

Example: When building a frontend-webapp-builder skill, an assets/hello-world/ template containing boilerplate HTML/React would be helpful.

Step 3: Initializing the Skill

Create the skill directory structure:

skill-name/
├── SKILL.md
├── scripts/      (if needed)
├── references/   (if needed)
└── assets/       (if needed)

Step 4: Edit the Skill

Remember the skill is being created for another agent to use. Include information that would be beneficial and non-obvious.

Frontmatter

Write YAML frontmatter with name and description:

• name: The skill name
• description: Primary triggering mechanism. Include:
  • What the Skill does
  • Specific triggers/contexts for when to use it
  • All "when to use" information (the body is only loaded after triggering)

Example description for a docx skill:

Comprehensive document creation, editing, and analysis with support for tracked changes, comments, formatting preservation, and text extraction. Use when working with professional documents (.docx files) for: (1) Creating new documents, (2) Modifying or editing content, (3) Working with tracked changes, (4) Adding comments.

Body

Write instructions for using the skill and its bundled resources. Use imperative/infinitive form.

Step 5: Validate and Test

Test the skill on real tasks:

1. Use the skill on real tasks
2. Notice struggles or inefficiencies
3. Identify how SKILL.md or bundled resources should be updated
4. Implement changes and test again

Progressive Disclosure Patterns

Pattern 1: High-level guide with references

# PDF Processing

## Quick start
Extract text with pdfplumber:
[code example]

## Advanced features
- **Form filling**: See references/forms.md
- **API reference**: See references/api.md

Pattern 2: Domain-specific organization

bigquery-skill/
├── SKILL.md (overview and navigation)
└── references/
    ├── finance.md (revenue, billing metrics)
    ├── sales.md (opportunities, pipeline)
    └── product.md (API usage, features)

When a user asks about sales metrics, only load sales.md.

Pattern 3: Framework variants

cloud-deploy/
├── SKILL.md (workflow + provider selection)
└── references/
    ├── aws.md (AWS deployment patterns)
    ├── gcp.md (GCP deployment patterns)
    └── azure.md (Azure deployment patterns)

When the user chooses AWS, only read aws.md.

Guidelines

• Avoid deeply nested references - Keep references one level deep from SKILL.md
• Structure longer reference files - For files longer than 100 lines, include a table of contents
• Test scripts - Added scripts must be tested by actually running them
• Delete unused examples - Remove example files not needed for the skill