The Complete Guide to Building Skills for Claude: Create, Test, and Distribute Custom Workflows

If you want Claude to act like a partner who knows your work habits—executing specific tasks without repeated instructions—building a Skill is the most effective solution available today. This guide systematically breaks down the core concepts, design principles, construction steps, testing methods, and distribution strategies for Skills, helping developers and power users transform complex workflows into capabilities Claude can call upon automatically.

The Core Question This Article Answers: How do you build a high-quality, distributable Claude Skill from scratch that genuinely improves workflow efficiency?

What is a Claude Skill? Redefining AI Capability Boundaries

A Skill is essentially a folder containing a specific set of instructions that teaches Claude how to handle specific tasks or workflows. Instead of re-explaining your preferences, processes, and domain expertise in every conversation, you can teach Claude once through a Skill and benefit from it in every subsequent interaction.

Core Value and Application Scenarios

Skills are most powerful when workflows are repeatable. Whether generating frontend designs from specifications, conducting research with consistent methodology, creating documents that follow team style guides, or orchestrating multi-step processes, Skills play a vital role. They work seamlessly with Claude’s built-in capabilities like code execution and document creation.

For those building MCP (Model Context Protocol) integrations, Skills add a powerful layer of functionality, helping turn raw tool access into reliable, optimized workflows. By using Skills, we can achieve:

1. Standardization: Ensuring Claude works consistently across the entire organization.
2. Efficiency Gains: Reducing the cost of repetitive explanations in every conversation.
3. Reliability: Embedding best practices into every interaction.

A Core Analogy: The Kitchen and the Recipe

To intuitively understand the relationship between Skills and MCP, we can use a kitchen analogy:

• MCP Provides the Professional Kitchen: It connects Claude to various services (like Notion, Asana, Linear), providing access to tools, ingredients, and equipment. This is the foundation of “what Claude can do.”
• Skills Provide the Recipes: They contain step-by-step instructions on how to use those tools to create value. This is the guidance on “how Claude should do it.”

Together, users can accomplish complex tasks without figuring out every step themselves. Without Skills, users might connect an MCP but not know what to do next, leading to increased support tickets, inconsistent results, and misplaced blame on the connector itself.

Technical Foundations: File Structure and Design Principles

The first step in building a Skill is understanding its file structure and core design principles. This is not just a technical specification; it is the foundation for ensuring the Skill can be correctly identified and efficiently executed by Claude.

Standard File Structure

A typical Skill folder structure looks like this:

your-skill-name/
├── SKILL.md        # Required - Main skill file
├── scripts/        # Optional - Executable code (Python, Bash, etc.)
├── references/     # Optional - Documentation loaded as needed
└── assets/         # Optional - Templates, fonts, icons used in output

• SKILL.md (Required): This is the core of the Skill, containing YAML metadata and Markdown instructions. The filename must be strictly SKILL.md (case-sensitive).
• scripts/: Stores executable scripts (Python, Bash, etc.) for processing data or performing validation.
• references/: Stores reference documentation that Claude can choose to consult as needed.
• assets/: Stores static resources like templates or fonts required for the output.

Three Core Design Principles

Skill design follows three key principles, ensuring efficiency and portability across different scenarios.

1. Progressive Disclosure

Skills use a three-level loading system designed to minimize token usage while maintaining specialized expertise.

• First Level (YAML Frontmatter): Always loaded in Claude’s system prompt. It provides just enough information for Claude to know when the skill should be used without loading all content. This is the “face” of the Skill, determining trigger accuracy.
• Second Level (SKILL.md Body): Loaded when Claude thinks the skill is relevant to the current task. This contains the full instructions and guidance.
• Third Level (Linked Files): Additional files bundled within the skill directory (in scripts/ or references/), which Claude navigates and discovers only as necessary.

This design ensures both response speed and deep support for complex tasks.

2. Composability

Claude can load multiple Skills simultaneously. This means your Skill should work well alongside others, rather than assuming it is the only capability available. Design should focus on specific domains, avoiding overly broad functionality.

3. Portability

Skills work identically across Claude.ai, Claude Code, and the API. Create once, and it runs on all supported surfaces, provided the environment meets the Skill’s dependencies. This ensures a “build once, run anywhere” experience.

Naming and Formatting Specifications

During construction, strictly adhering to naming and formatting rules is crucial to avoid upload errors.

• Skill Folder Naming: Must use kebab-case (hyphen-separated), e.g., notion-project-setup. Spaces, underscores, and capital letters are forbidden.
• SKILL.md Naming: Must match SKILL.md exactly. Any variation (like skill.md or SKILL.MD) will cause errors.
• No README.md: The Skill folder should not contain a README.md. All documentation should be integrated into SKILL.md or the references/ directory. (When distributing via GitHub, a repo-level README is for human users, but this is separate from the internal Skill structure).

Planning and Design: From Scenarios to Success Metrics

Before writing any code or instructions, defining the use cases and success criteria is vital. This is the watershed moment that distinguishes a “functional” Skill from a “great” one.

Define Concrete Use Cases

An excellent Skill should be designed for 2-3 specific use cases. A good use case definition should include triggers, steps, and expected results.

Example: Project Sprint Planning

• Trigger: User says “Help me plan this sprint” or “Create sprint tasks.”
• Steps:

  1. Fetch current project status via MCP (e.g., Linear).
  2. Analyze team velocity and capacity.
  3. Suggest task prioritization.
  4. Create tasks in Linear with proper labels and estimates.
• Result: A fully planned sprint with all tasks created.

When defining use cases, ask: What does the user want to achieve? What multi-step workflows are required? Which tools (built-in or MCP) are needed? What domain knowledge or best practices should be embedded?

Common Skill Categories and Patterns

Based on observations, Skills generally fall into three main categories, each with unique technical focuses.

Category	Use Case	Key Techniques	Example
Document & Asset Creation	Creating consistent, high-quality output like docs, presentations, code, designs.	Embedded style guides, template structures, quality checklists. No external tools required; relies on Claude’s built-in capabilities.	frontend-design Skill for building production-grade interfaces.
Workflow Automation	Multi-step processes benefiting from consistent methodology, including coordination across multiple MCP servers.	Step-by-step workflows with validation gates, templates for common structures, iterative refinement loops.	skill-creator Skill for interactively guiding the creation of new skills.
MCP Enhancement	Workflow guidance enhancing the tool access provided by an MCP server.	Coordinating multiple MCP calls in sequence, embedding domain expertise, error handling for common MCP issues.	sentry-code-review Skill using Sentry data to automatically analyze and fix bugs in GitHub PRs.

Setting Success Criteria

How do you know if your Skill is working correctly? Here are quantitative and qualitative metrics. These are aspirational targets rather than precise thresholds.

Quantitative Metrics:

• Trigger Rate: The Skill should trigger on 90% of relevant queries.

  • How to measure: Run 10-20 test queries that should trigger the Skill. Track how often it loads automatically vs. requires manual invocation.
• Tool Call Efficiency: Completes the workflow in X tool calls.

  • How to measure: Compare the same task with and without the Skill enabled. Count tool calls and total tokens consumed.
• API Failure Rate: 0 failed API calls per workflow.

  • How to measure: Monitor MCP server logs during test runs. Track retry rates and error codes.

Qualitative Metrics:

• Users don’t need to prompt Claude about next steps.
• Workflows complete without user correction.
• Results are consistent across different sessions.

Reflection & Insight: During early testing, we found many developers fall into the “over-engineering” trap, trying to make one Skill solve everything. However, experience shows that focusing on solving a single, challenging task until Claude succeeds consistently, and then distilling that into a Skill, is often more efficient than broad testing from the start. This “deep first, then broad” strategy leverages Claude’s in-context learning and provides faster valid signals.

Writing in Action: Constructing the SKILL.md Core File

SKILL.md is the brain of the Skill. Understanding its structure, specifically the YAML frontmatter, is critical for determining if the Skill can be correctly triggered and executed.

YAML Frontmatter: The Art of the Trigger

YAML frontmatter is the basis for Claude deciding whether to load your Skill. This is the first level of progressive disclosure; its importance cannot be overstated.

Minimum Required Format:

---
name: your-skill-name
description: What it does. Use when user asks to [specific phrases].
---

Detailed Field Requirements:

1. name (Required):

   • Must be kebab-case.
   • No spaces or capitals.
   • Should match the folder name.

2. description (Required):

   • Must contain BOTH: What the Skill does + When to use it (trigger conditions).
   • Must be under 1024 characters.
   • No XML tags (< or >).
   • Should include specific tasks or phrases a user might say.
   • Mention relevant file types if applicable.

3. license (Optional): If open-sourcing, use standard identifiers like MIT or Apache-2.0.

4. metadata (Optional): Custom key-value pairs. Suggested fields include author, version, mcp-server.

Security Restrictions:

• XML angle brackets are strictly forbidden in the frontmatter.
• Skill names must not contain “claude” or “anthropic” (reserved keywords).

Guide to Writing the Description Field

The description field is not just an explanation; it is the core for matching user intent.

Structure of an Excellent Description: [What it does] + [When to use it] + [Key capabilities]

Good Examples:

# Example 1: Specific and actionable
description: Analyzes Figma design files and generates developer handoff documentation. Use when user uploads .fig files, asks for "design specs", "component documentation", or "design-to-code handoff".

# Example 2: Includes specific trigger phrases
description: Manages Linear project workflows including sprint planning, task creation, and status tracking. Use when user mentions "sprint", "Linear tasks", "project planning", or asks to "create tickets".

Bad Examples:

# Too vague
description: Helps with projects.

# Missing triggers
description: Creates sophisticated multi-page documentation systems.

# Too technical, lacking user perspective
description: Implements the Project entity model with hierarchical relationships.

Writing Main Instructions

After the frontmatter, write the specific operational instructions in Markdown. A template structure including instructions, steps, examples, and troubleshooting is recommended.

Recommended Template Structure:

---
name: your-skill
description: [Function and Triggers]
---

# Your Skill Name

## Instructions
[General Guiding Principles]

## Step 1: [First Step]
Clear explanation of what happens.
Example:
```bash
python scripts/fetch_data.py --project-id PROJECT_ID

Expected output: [Describe what success looks like]

Examples

Example 1: [Common Scenario]

User says: “Set up a new marketing campaign”
Actions:

1. Fetch existing campaigns via MCP
2. Create new campaign with provided parameters
   Result: Campaign created with confirmation link

Troubleshooting

Error: [Common Error Message]

Cause: [Why it happens]
Solution: [How to fix]

**Best Practices:**

1.  **Specific and Actionable:** Use clear commands and parameters rather than vague instructions.
    *   *Good:* Run `python scripts/validate.py --input {filename}` to check data format.
    *   *Bad:* Validate the data before proceeding.
2.  **Include Error Handling:** Anticipate common issues (like MCP connection failures) and provide checking steps.
3.  **Reference Bundled Resources:** Instruct Claude on when to consult documents in the `references/` directory.
4.  **Utilize Progressive Disclosure:** Keep core instructions in `SKILL.md`; move detailed docs to `references/` and link to them.

## Five Patterns: Strategies for Building Complex Workflows

Based on the experience of early adopters and internal teams, we have summarized five common and effective Skill construction patterns. The choice depends on your specific problem.

### Pattern 1: Sequential Workflow Orchestration

**Use Case:** Users need multi-step processes in a specific order.

**Example Structure:** New customer onboarding workflow.

```markdown
## Workflow: Onboard New Customer

### Step 1: Create Account
Call MCP tool: `create_customer`
Parameters: name, email, company

### Step 2: Setup Payment
Call MCP tool: `setup_payment_method`
Wait for: payment method verification

### Step 3: Create Subscription
Call MCP tool: `create_subscription`
Parameters: plan_id, customer_id (from Step 1)

### Step 4: Send Welcome Email
Call MCP tool: `send_email`
Template: welcome_email_template

Key Techniques: Explicit step ordering, defining dependencies between steps, setting validation at each stage, preparing rollback instructions for failures.

Pattern 2: Multi-MCP Coordination

Use Case: Workflows span multiple services.

Example: Design-to-development handoff.

1. Phase 1: Design Export (Figma MCP) -> 2. Phase 2: Asset Storage (Drive MCP) -> 3. Phase 3: Task Creation (Linear MCP) -> 4. Phase 4: Notification (Slack MCP)

Key Techniques: Clear phase separation, passing data between MCPs, validation before moving to the next phase, centralized error handling.

Pattern 3: Iterative Refinement

Use Case: Output quality improves with iteration.

Example: Report generation.

Flow: Initial Draft -> Quality Check (Run validation script) -> Refinement Loop (Fix issues, re-validate) -> Finalization.

Key Techniques: Explicit quality criteria, iterative improvement logic, validation scripts, clear conditions for stopping iteration.

Pattern 4: Context-Aware Tool Selection

Use Case: Using different tools to achieve the same outcome based on context.

Example: Smart file storage.

## Decision Tree
1. Check file type and size
2. Determine best storage location:
   - Large files (>10MB): Use cloud storage MCP
   - Collaborative docs: Use Notion/Docs MCP
   - Code files: Use GitHub MCP
   - Temporary files: Use local storage

Key Techniques: Clear decision criteria, fallback options, transparent explanation of choices.

Pattern 5: Domain-Specific Intelligence

Use Case: The Skill adds specialized knowledge beyond tool access.

Example: Financial compliance payment processing.

Before processing payment, execute compliance checks (sanctions lists, jurisdiction, risk assessment). Only process if checks pass; otherwise, flag for review. Document the audit trail throughout.

Key Techniques: Embedding domain expertise in logic, compliance checks before action, comprehensive documentation, clear governance processes.

Testing, Iteration, and Diagnosis

Building a Skill is not a one-time task but a continuous iterative process. Effective testing and diagnostic strategies ensure the stability and reliability of the Skill.

Three Tiers of Testing Methods

Depending on quality requirements, different levels of testing can be adopted:

1. Manual Testing in Claude.ai: Run queries directly and observe behavior. Fast iteration, no setup required.
2. Scripted Testing in Claude Code: Automate test cases for repeatable validation across changes.
3. Programmatic Testing via Skills API: Systematically run evaluation suites against defined test sets.

Recommended Testing Process

Effective Skill testing typically covers three areas:

1. Triggering Tests
Goal: Ensure the Skill loads at the right times.

• Should trigger: On obvious tasks and paraphrased requests.
• Should NOT trigger: On unrelated topics.

2. Functional Tests
Goal: Verify the Skill produces correct outputs.

• Verify output generation, successful API calls, effective error handling, and edge case coverage.

3. Performance Comparison
Goal: Prove the Skill improves results compared to a baseline.
Compare conversation turns, API failure counts, and token consumption with and without the Skill.

Diagnosis and Fixes for Common Issues

Issue: Skill Won’t Upload

• Error: “Could not find SKILL.md” -> Cause: Filename not exact. Fix: Rename to SKILL.md (case-sensitive).
• Error: “Invalid frontmatter” -> Cause: YAML formatting issues, like missing --- delimiters or unclosed quotes.

Issue: Skill Doesn’t Trigger

• Diagnosis: The description field might be too broad or missing trigger phrases.
• Fix: Revise the description to include phrases users would actually say and relevant file types.
• Debug Tip: Ask Claude directly: “When would you use the [skill name] skill?” and adjust the description based on the response.

Issue: Skill Triggers Too Often

• Fix: Add negative triggers (e.g., “Do NOT use for…”), or narrow the scope to be more specific.

Issue: Instructions Not Followed

• Common Causes: Instructions too verbose, critical instructions buried, ambiguous language.
• Fix: Simplify instructions, use headers like ## Important to highlight key points, and use precise language. For critical validations, bundling a script is recommended over relying on language instructions.

Distribution and Sharing: Letting Value Flow

Skills make MCP integrations more complete. Connectors with Skills offer users a faster path to value, providing a competitive edge.

Current Distribution Model

How Individual Users Get Skills:

1. Download the Skill folder.
2. Zip the folder if necessary.
3. Upload via Claude.ai Settings > Capabilities > Skills.
4. Or place in the Claude Code skills directory.

Organization-Level Skills:
Admins can deploy Skills workspace-wide, enabling automatic updates and centralized management.

Recommended Distribution Path

The currently recommended approach is to host the Skill on GitHub, accompanied by a clear README (for human users) and examples.

1. Host on GitHub: Create a public repository containing installation instructions, example usage, and screenshots.
2. Document in Your MCP Repo: Link to the Skill from your MCP documentation, explaining the value of using both together, and provide a quick-start guide.
3. Create an Installation Guide: Provide clear steps for downloading, installing, enabling, and testing.

Positioning Your Skill

When describing your Skill, focus on outcomes rather than features. Highlight the MCP + Skill combination story—MCP provides “what can be done,” and Skill provides “how to do it.”

• Good Positioning: “The ProjectHub Skill enables teams to set up complete project workspaces in seconds—including pages, databases, and templates—instead of spending 30 minutes on manual setup.”
• Bad Positioning: “The ProjectHub Skill is a folder containing YAML metadata and Markdown instructions that calls our MCP server tools.”

Conclusion and Practical Checklist

Building a Claude Skill is an effective way to transform repetitive, complex workflows into capabilities AI can execute automatically. By following the principles of progressive disclosure, composability, and portability, and employing practical patterns like sequential orchestration and multi-MCP coordination, we can significantly enhance AI’s professionalism and reliability in specific domains.

One-Page Summary

1. Core Composition: Skill = SKILL.md (YAML metadata + Markdown instructions) + scripts/references/assets.
2. Design Core: Progressive disclosure (Metadata -> Instructions -> Files); define use cases before coding.
3. Key Files: SKILL.md naming must be exact; description must contain function and trigger timing.
4. Building Patterns: Sequential workflow, Multi-MCP coordination, Iterative refinement, Context-aware selection, Domain intelligence.
5. Testing Focus: Verify trigger accuracy, functional correctness, and performance improvements.
6. Distribution: GitHub hosting + Organization deployment + API integration.

Practical Operations Checklist

Before Building:

• [ ] Identify 2-3 concrete use cases
• [ ] Identify required tools (built-in or MCP)
• [ ] Plan folder structure

During Development:

• [ ] Folder named in kebab-case
• [ ] SKILL.md file exists and is named correctly
• [ ] YAML frontmatter has --- delimiters
• [ ] description includes “What it does” and “When to use”
• [ ] No XML tags

Before Upload:

• [ ] Test triggering (relevant/irrelevant queries)
• [ ] Functional tests pass
• [ ] Compress as .zip if needed

After Upload:

• [ ] Test in real conversations
• [ ] Monitor trigger frequency
• [ ] Collect feedback and iterate

Frequently Asked Questions (FAQ)

Q1: What is the difference between a Skill and MCP?
MCP (Model Context Protocol) is primarily responsible for connecting Claude to external services (like Notion, GitHub), providing “tools” and “ingredients.” A Skill is a “recipe” that teaches Claude how to use these tools and ingredients to complete complex tasks with specific steps and logic.

Q2: What is the most critical part of the SKILL.md file?
The description field in the YAML frontmatter is the most critical. It directly determines whether Claude will trigger your Skill at the right moment. The description must clearly state the function and usage scenarios, including specific trigger phrases a user might use.

Q3: Can my Skill folder contain a README.md?
No. The Skill folder should not contain a README.md internally, as this can cause loading issues. All AI-facing documentation should be in SKILL.md or the references/ directory. If distributing via GitHub, a repo-level README for human users is appropriate.

Q4: How do I prevent my Skill from triggering on unrelated topics?
Add negative triggers in the description field, for example, explicitly stating “Do NOT use for [certain scenario],” or make the description more specific to limit its scope, avoiding overly broad language.

Q5: Can I use external scripts in a Skill?
Yes. You can place executable scripts like Python or Bash in the scripts/ directory and call them in the SKILL.md instructions. This is particularly useful for tasks requiring determinism, such as data validation or complex logic processing.

Q6: How long does it typically take to build a Skill?
If you already have a clear workflow and necessary tools (like an MCP server), using the skill-creator Skill for assistance, you can typically build and test a functional Skill in 15-30 minutes.

Q7: How can other team members use my Skill?
Currently, organization admins can deploy Skills to the entire workspace, enabling automatic updates and centralized management. For public sharing, hosting on GitHub with clear installation instructions is recommended.

Q8: Are Skills universally compatible across different platforms?
Yes, Skills are portable. The same Skill folder works identically on Claude.ai, Claude Code, and the API, provided the runtime environment meets any of the Skill’s dependency requirements.