Skill Creator

This skill provides guidance for creating effective skills.

About Skills

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

How Skills Actually Work

Understanding how skills work helps you design more effective ones:

Skills are prompt-based context modifiers, not executable code. When a skill is invoked, it doesn't execute actions directly. Instead, it:

1. Injects instructions into the conversation context (via hidden messages to Claude)
2. Modifies execution context by changing tool permissions and optionally switching models
3. Guides Claude's behaviour through detailed instructions, rather than performing operations itself

Skill selection happens through pure LLM reasoning. There's no algorithmic matching, keyword search, or intent classification. Claude reads the skill descriptions in the Skill tool's prompt and uses its language model to decide which skill matches the user's request. This makes the description field absolutely critical - it's the only signal Claude has for deciding when to use your skill.

Progressive disclosure keeps context lean. Skills use a three-level loading system:

1. Level 1: Metadata (name + description) - Always loaded into Claude's context and is how Claude knows when it should use the skill (~100 words)
2. Level 2: SKILL.md body - Loaded only after the skill triggers (<5k words)
3. Level 3: Bundled resources - Loaded by Claude as needed (unlimited, since scripts can execute without reading)

This architecture means your description must be both concise (to fit within token budgets) and comprehensive (to enable accurate skill selection).

What Skills Provide

1. Specialised 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 Claude needs: system prompt, conversation history, other Skills' metadata, and the actual user request.

Default assumption: Claude is already very smart. Only add context Claude doesn't already have. Challenge each piece of information: "Does Claude 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 behaviour.

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 Claude 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, one of the most important sections to get right)
│   │   └── metadata: any notes about the skill that are only for human consumption (optional, not parsed by Claude)
│   └── Markdown instructions (required)
└── Bundled Resources (optional, only if required and add value)
    ├── 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 name and description fields. These are the only fields that Claude reads 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 (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
• Note: Scripts may still need to be read by Claude for patching or environment-specific adjustments

References (references/)

Documentation and reference material intended to be loaded as needed into context to inform Claude's process and thinking.

• When to include: For documentation that Claude should reference while working
• Examples: references/finance.md for financial schemas, references/mnda.md for company NDA template, references/policies.md for company policies, references/api_docs.md for API specifications
• Use cases: Database schemas, API documentation, domain knowledge, company policies, detailed workflow guides
• Benefits: Keeps SKILL.md lean, loaded only when Claude determines 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 Claude produces.

• When to include: When the skill needs files that will be used in the final output
• Examples: assets/logo.png for brand assets, assets/slides.pptx for PowerPoint templates, assets/frontend-template/ for HTML/React boilerplate, assets/font.ttf for typography
• Use cases: Templates, images, icons, boilerplate code, fonts, sample documents that get copied or modified
• Benefits: Separates output resources from documentation, enables Claude 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
• QUICK_START.md
• SUMMARY.md
• etc.

The skill should only contain the information needed for an AI agent to do the job at hand. It should not contain auxiliary 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:

1. Metadata (name + description) - Always in context (~100 words)
2. SKILL.md body - When skill triggers (<5k words)
3. Bundled resources - As needed by Claude (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 minimise 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

Claude loads FORMS.md, REFERENCE.md, or EXAMPLES.md only when needed. Note: Be careful not to duplicate information across files as this will only lead to context bloat and a reduced signal to noise ratio.

Pattern 2: Domain-specific organisation

For Skills with multiple domains, organise 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)

This allows the agent using the skill to decide to only read in the relevant domain information that is needed for the task.

When a user asks about sales metrics, Claude only reads sales.md.

Similarly, for skills supporting multiple frameworks or variants, organise by variant:

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

When the user chooses AWS, Claude only reads 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)

Claude reads 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 Claude can see the full scope when previewing.

Skill Creation Process

Skill creation involves these steps:

1. Understand the skill with concrete examples
2. Plan reusable skill contents (scripts, references, assets)
3. Initialise the skill (run init_skill.py)
4. Edit the skill (implement resources and write SKILL.md)
5. 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, analyse 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, analyse each concrete example to create a list of the reusable resources to include: scripts, references, and assets.

Step 3: Initialising 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 <skill-name>

The script:

• Creates the skill directory in ~/.claude/skills/
• 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 customised or deleted

After initialisation, customise 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 instance of Claude to use. Include information that would be beneficial and non-obvious to Claude. Consider what procedural knowledge, domain-specific details, or reusable assets would help another Claude 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 initialisation 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 in skill instructions (e.g., "Extract text from the PDF" not "You should extract text from the PDF"). Skills are prompt templates that guide Claude's behaviour through direct instructions, not descriptions of what Claude should do. Imperative language is clearer and more token-efficient.

Frontmatter

Write the YAML frontmatter with name and description:

• name: The skill name (must match directory name exactly, hyphen-case format)

• description: This is the most critical field for your skill. Since Claude uses pure language model reasoning to select skills (no algorithmic matching), this description is the only signal Claude has to decide when to invoke your skill.

  Writing effective descriptions:

  • Be comprehensive yet concise: Include what the skill does AND when to use it. You're competing for space in a ~15,000 character token budget alongside all other skills.
  • Front-load key information: Claude reads descriptions in the Skill tool's prompt. Put the most distinctive information first.
  • Include specific triggers: Mention file types, task types, or contexts that should trigger the skill.
  • Think like Claude: What would make this skill stand out when Claude is reasoning about which skill matches the user's request?
  • Never defer to the body: The body is only loaded AFTER triggering, so "When to Use This Skill" sections in the body are useless for selection. Put all triggering information in the description.

  Example for a docx skill: "Comprehensive document creation, editing, and analysis with support for tracked changes, comments, formatting preservation, and text extraction. Use when Claude needs to work with professional documents (.docx files) for: (1) Creating new documents, (2) Modifying or editing content, (3) Working with tracked changes, (4) Adding comments, or any other document tasks"

  Why this matters: Claude receives your description as part of a formatted list in the Skill tool's prompt, reasons about which skill matches the user's intent, and makes a decision purely through its language understanding. There's no keyword matching or semantic search - just Claude reading and reasoning.

Optional frontmatter fields (use only when needed):

• allowed-tools: Comma-separated list of tools the skill can use without user approval. When a skill is invoked, these tools are automatically pre-approved in the execution context. Example: "Read,Write,Bash(git:*),Grep". Use wildcards to scope permissions (e.g., Bash(git:*) allows all git commands). The idea is to only include tools your skill actually needs - however we should make sure we don't over-restrict and accidentally block needed functionality so it's usually best to leave this filed commented out with what you think the skill needs which will let the user uncomment and use it if they wish.

• model: Override the model for this skill's execution. Add this field but leave it with the value of "inherit" to use the session's current model (default), the user may wish to change it to a specific model later e.g. "claude-opus-4-5-20250514".

Body

Write instructions for using the skill and its bundled resources.

Step 5: 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.

Iteration workflow:

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

Important Skill Design Considerations

Remember:

• Claude / Claude Code 'Skill' for acquiring knowledge when working with a specific domain, technology or task that is unlikely to be detailed or up to date in the model's training data.
• It's important that the content contains valuable information and guidance for AI coding agents - but at the same time if the skill content is too large and wordy it will bloat the coding agents context window, reducing the signal to noise ratio and leave less context available for doing the work - so we need to strike a balance.
• There is no value in adding when not to use a skill in the skill content as it will already have been triggered by that point.
• Do not duplicate information throughout the skill - focus on concise, high-value information that will add valuable knowledge to the agent without creating a novel.
• Only include code samples if they are critical to understanding a unique aspect, pattern or approach that could not simply be considered 'good quality coding'.
• If a word, sentence, paragraph or section does not add any knowledge or concise clarification - it should be removed.

If you have the ingest CLI tool available

• You may use ingest *.md after creating a skill to get an estimated number of tokens the skill will use when loaded into context.
• Total token guidance (this will depend on the scope and complexity of the knowledge that the skill is providing):
  • Great: 1k-5k
  • Good: 5k-9k
  • OK 9k-12k
  • Poor: 12k+
• Aim for <4k tokens in the main SKILL.md file if possible.

After Creating A Skill

After creating a skill you must always perform a critical self-review and improvement the skill you've created to ensure it's information is valuable, concise, free of duplication, fluff or other low-value prose, then go through a process of refining and 'thinning' the language used in the skill so that the important information is more prominent but the overall word count is reduced - making the skill more effective and efficient to use.

IMPORTANT FRAMING: Verbosity is not rewarded - knowledge quality is!