skill-creator-skill
💡 Summary
This skill guides users in creating effective, modular skills for AI agents with specialized knowledge and workflows.
🎯 Target Audience
🤖 AI Roast: “Powerful, but the setup might scare off the impatient.”
Risk: Medium. Review: shell/CLI command execution; outbound network access (SSRF, data egress); API keys/tokens handling and storage; filesystem read/write scope and path traversal. Run with least privilege and audit before enabling in production.
name: skill-creator description: Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends agent' capabilities with specialized knowledge, workflows, or tool integrations. license: LICENSE
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 agent from general-purpose assistants into specialized assistants equipped with procedural knowledge that no model can fully possess.
What Skills Provide
- Specialized workflows - Multi-step procedures for specific domains
- Tool integrations - Instructions for working with specific file formats or APIs
- Domain expertise - Company-specific knowledge, schemas, business logic
- 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 agent need: system prompt, conversation history, other Skills' metadata, and the actual user request.
Default assumption: agent are already very smart. Only add context agent don't already have. Challenge each piece of information: "Does an agent 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 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 intended to be 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
nameanddescriptionfields. These are the only fields that agent read to determine when the skill gets used, thus it is very important to 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 (if at all).
Bundled Resources (optional)
Scripts (scripts/)
Executable code files for tasks that require deterministic reliability. For detailed guidance on creating and using scripts in skills, see references/scripts.md.
References (references/)
Documentation and reference material intended to be loaded as needed into context to inform agent' process and thinking.
- When to include: For documentation that agent should reference while working
- Examples:
references/finance.mdfor financial schemas,references/mnda.mdfor company NDA template,references/policies.mdfor company policies,references/api_docs.mdfor API specifications - Use cases: Database schemas, API documentation, domain knowledge, company policies, detailed workflow guides
- Benefits: Keeps SKILL.md lean, loaded only when agent determine it's needed
- Best practice: If files are large (>10k words), include grep search patterns in SKILL.md
- Avoid duplication: Information should live in either SKILL.md or references files, not both. Prefer references files for detailed information unless it's truly core to the skill—this keeps SKILL.md lean while making information discoverable without hogging the context window. Keep only essential procedural instructions and workflow guidance in SKILL.md; move detailed reference material, schemas, and examples to references files.
Assets (assets/)
Files not intended to be loaded into context, but rather used within the output agent produce.
- When to include: When the skill needs files that will be used in the final output
- Examples:
assets/logo.pngfor brand assets,assets/slides.pptxfor PowerPoint templates,assets/frontend-template/for HTML/React boilerplate,assets/font.ttffor typography - Use cases: Templates, images, icons, boilerplate code, fonts, sample documents that get copied or modified
- Benefits: Separates output resources from documentation, enables agent to use files without loading them into context
What to Not Include in a Skill
A skill should only contain essential files that directly support its functionality. Do NOT create extraneous documentation or auxiliary files, including:
- README.md
- INSTALLATION_GUIDE.md
- QUICK_REFERENCE.md
- CHANGELOG.md
- etc.
The skill should only contain the information needed for an AI agent to do the job at hand. It should not contain auxilary context about the process that went into creating it, setup and testing procedures, user-facing documentation, etc. Creating additional documentation files just adds clutter and confusion.
Progressive Disclosure Design Principle
Skills use a three-level loading system to manage context efficiently:
- Metadata (name + description) - Always in context (~100 words)
- SKILL.md body - When skill triggers (<5k words)
- Bundled resources - As needed by agent (Unlimited because scripts can be executed without reading into context window)
Progressive Disclosure Patterns
Keep SKILL.md body to the essentials and under 500 lines to minimize context bloat. Split content into separate files when approaching this limit. When splitting out content into other files, it is very important to reference them from SKILL.md and describe clearly when to read them, to ensure the reader of the skill knows they exist and when to use them.
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 (patterns, examples, configuration) into separate reference files.
Pattern 1: High-level guide with references
# PDF Processing ## Quick start Extract text with pdfplumber: [code example] ## Advanced features - **Form filling**: See [FORMS.md](FORMS.md) for complete guide - **API reference**: See [REFERENCE.md](REFERENCE.md) for all methods - **Examples**: See [EXAMPLES.md](EXAMPLES.md) for common patterns
Agent load FORMS.md, REFERENCE.md, or EXAMPLES.md only when needed.
Pattern 2: Domain-specific organization
For Skills with multiple domains, organize content by domain to avoid loading irrelevant context:
bigquery-skill/
├── SKILL.md (overview and navigation)
└── reference/
├── finance.md (revenue, billing metrics)
├── sales.md (opportunities, pipeline)
├── product.md (API usage, features)
└── marketing.md (campaigns, attribution)
When a user asks about sales metrics, agent only read sales.md.
Similarly, for skills supporting multiple frameworks or variants, organize by variant:
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, agent only read aws.md.
Pattern 3: Conditional details
Show basic content, link to advanced content:
# DOCX Processing ## Creating documents Use docx-js for new documents. See [DOCX-JS.md](DOCX-JS.md). ## Editing documents For simple edits, modify the XML directly. **For tracked changes**: See [REDLINING.md](REDLINING.md) **For OOXML details**: See [OOXML.md](OOXML.md)
Agent read REDLINING.md or OOXML.md only when the user needs those features.
Important guidelines:
- Avoid deeply nested references - Keep references one level deep from SKILL.md. All reference files should link directly from SKILL.md.
- Structure longer reference files - For files longer than 100 lines, include a table of contents at the top so agent can see the full scope when previewing.
Skill Creation Process
Skill creation involves these steps:
- Understand the skill with concrete examples
- Plan reusable skill contents (scripts, references, assets)
- Initialize the skill (run init_skill.py)
- Edit the skill (implement resources and write SKILL.md)
- Package the skill (run package_skill.py)
- 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
Pros
- Provides a structured approach to skill creation
- Encourages modular design for better maintainability
- Offers clear guidelines for documentation and resources
Cons
- May require technical expertise to implement effectively
- Could be overwhelming for beginners without prior knowledge
- Limited examples may hinder understanding for some users
Related Skills
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B“Powerful, but the setup might scare off the impatient.”
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pytorch
S“It's the Swiss Army knife of deep learning, but good luck figuring out which of the 47 installation methods is the one that won't break your system.”
Disclaimer: This content is sourced from GitHub open source projects for display and rating purposes only.
Copyright belongs to the original author observerw.
