Apply Anthropic Skill Best Practices

Anthropic's official skill authoring best practices

Apply Anthropic's official skill authoring best practices to your skill.

Good Skills are concise, well-structured, and tested with real usage. This guide provides practical authoring decisions to help you write Skills that Claude can discover and use effectively.

Core principles

Skill Metadata

Not every token in your Skill has an immediate cost. At startup, only the metadata (name and description) from all Skills is pre-loaded. Claude reads SKILL.md only when the Skill becomes relevant, and reads additional files only as needed. However, being concise in SKILL.md still matters: once Claude loads it, every token competes with conversation history and other context.

Test with all models you plan to use

Skills act as additions to models, so effectiveness depends on the underlying model. Test your Skill with all the models you plan to use it with.

Testing considerations by model:

• Claude Haiku (fast, economical): Does the Skill provide enough guidance?
• Claude Sonnet (balanced): Is the Skill clear and efficient?
• Claude Opus (powerful reasoning): Does the Skill avoid over-explaining?

What works perfectly for Opus might need more detail for Haiku. If you plan to use your Skill across multiple models, aim for instructions that work well with all of them.

Skill structure

<Note>
  **YAML Frontmatter**: The SKILL.md frontmatter supports two fields:

• name - Human-readable name of the Skill (64 characters maximum)

• description - One-line description of what the Skill does and when to use it (1024 characters maximum)

  For complete Skill structure details, see the Skills overview.

  </Note>

Naming conventions

Use consistent naming patterns to make Skills easier to reference and discuss. We recommend using gerund form (verb + -ing) for Skill names, as this clearly describes the activity or capability the Skill provides.

Good naming examples (gerund form):

• "Processing PDFs"
• "Analyzing spreadsheets"
• "Managing databases"
• "Testing code"
• "Writing documentation"

Acceptable alternatives:

• Noun phrases: "PDF Processing", "Spreadsheet Analysis"
• Action-oriented: "Process PDFs", "Analyze Spreadsheets"

Avoid:

• Vague names: "Helper", "Utils", "Tools"
• Overly generic: "Documents", "Data", "Files"
• Inconsistent patterns within your skill collection

Consistent naming makes it easier to:

• Reference Skills in documentation and conversations
• Understand what a Skill does at a glance
• Organize and search through multiple Skills
• Maintain a professional, cohesive skill library

Writing effective descriptions

The description field enables Skill discovery and should include both what the Skill does and when to use it.

<Warning>
  **Always write in third person**. The description is injected into the system prompt, and inconsistent point-of-view can cause discovery problems.

• Good: "Processes Excel files and generates reports"
• Avoid: "I can help you process Excel files"
• Avoid: "You can use this to process Excel files"

  </Warning>

Be specific and include key terms. Include both what the Skill does and specific triggers/contexts for when to use it.

Each Skill has exactly one description field. The description is critical for skill selection: Claude uses it to choose the right Skill from potentially 100+ available Skills. Your description must provide enough detail for Claude to know when to select this Skill, while the rest of SKILL.md provides the implementation details.

Effective examples:

PDF Processing skill:

description: Extract text and tables from PDF files, fill forms, merge documents. Use when working with PDF files or when the user mentions PDFs, forms, or document extraction.

Excel Analysis skill:

description: Analyze Excel spreadsheets, create pivot tables, generate charts. Use when analyzing Excel files, spreadsheets, tabular data, or .xlsx files.

Git Commit Helper skill:

description: Generate descriptive commit messages by analyzing git diffs. Use when the user asks for help writing commit messages or reviewing staged changes.

Avoid vague descriptions like these:

description: Helps with documents

description: Processes data

description: Does stuff with files

Progressive disclosure patterns

SKILL.md serves as an overview that points Claude to detailed materials as needed, like a table of contents in an onboarding guide. For an explanation of how progressive disclosure works, see How Skills work in the overview.

Practical guidance:

• Keep SKILL.md body under 500 lines for optimal performance
• Split content into separate files when approaching this limit
• Use the patterns below to organize instructions, code, and resources effectively

Visual overview: From simple to complex

A basic Skill starts with just a SKILL.md file containing metadata and instructions:

<img src="https://mintcdn.com/anthropic-claude-docs/4Bny2bjzuGBK7o00/images/agent-skills-simple-file.png?fit=max&auto=format&n=4Bny2bjzuGBK7o00&q=85&s=87782ff239b297d9a9e8e1b72ed72db9" alt="Simple SKILL.md file showing YAML frontmatter and markdown body" data-og-width="2048" width="2048" data-og-height="1153" height="1153" data-path="images/agent-skills-simple-file.png" data-optimize="true" data-opv="3" srcset="https://mintcdn.com/anthropic-claude-docs/4Bny2bjzuGBK7o00/images/agent-skills-simple-file.png?w=280&fit=max&auto=format&n=4Bny2bjzuGBK7o00&q=85&s=c61cc33b6f5855809907f7fda94cd80e 280w, https://mintcdn.com/anthropic-claude-docs/4Bny2bjzuGBK7o00/images/agent-skills-simple-file.png?w=560&fit=max&auto=format&n=4Bny2bjzuGBK7o00&q=85&s=90d2c0c1c76b36e8d485f49e0810dbfd 560w, https://mintcdn.com/anthropic-claude-docs/4Bny2bjzuGBK7o00/images/agent-skills-simple-file.png?w=840&fit=max&auto=format&n=4Bny2bjzuGBK7o00&q=85&s=ad17d231ac7b0bea7e5b4d58fb4aeabb 840w, https://mintcdn.com/anthropic-claude-docs/4Bny2bjzuGBK7o00/images/agent-skills-simple-file.png?w=1100&fit=max&auto=format&n=4Bny2bjzuGBK7o00&q=85&s=f5d0a7a3c668435bb0aee9a3a8f8c329 1100w, https://mintcdn.com/anthropic-claude-docs/4Bny2bjzuGBK7o00/images/agent-skills-simple-file.png?w=1650&fit=max&auto=format&n=4Bny2bjzuGBK7o00&q=85&s=0e927c1af9de5799cfe557d12249f6e6 1650w, https://mintcdn.com/anthropic-claude-docs/4Bny2bjzuGBK7o00/images/agent-skills-simple-file.png?w=2500&fit=max&auto=format&n=4Bny2bjzuGBK7o00&q=85&s=46bbb1a51dd4c8202a470ac8c80a893d 2500w" />

As your Skill grows, you can bundle additional content that Claude loads only when needed:

<img src="https://mintcdn.com/anthropic-claude-docs/4Bny2bjzuGBK7o00/images/agent-skills-bundling-content.png?fit=max&auto=format&n=4Bny2bjzuGBK7o00&q=85&s=a5e0aa41e3d53985a7e3e43668a33ea3" alt="Bundling additional reference files like reference.md and forms.md." data-og-width="2048" width="2048" data-og-height="1327" height="1327" data-path="images/agent-skills-bundling-content.png" data-optimize="true" data-opv="3" srcset="https://mintcdn.com/anthropic-claude-docs/4Bny2bjzuGBK7o00/images/agent-skills-bundling-content.png?w=280&fit=max&auto=format&n=4Bny2bjzuGBK7o00&q=85&s=f8a0e73783e99b4a643d79eac86b70a2 280w, https://mintcdn.com/anthropic-claude-docs/4Bny2bjzuGBK7o00/images/agent-skills-bundling-content.png?w=560&fit=max&auto=format&n=4Bny2bjzuGBK7o00&q=85&s=dc510a2a9d3f14359416b706f067904a 560w, https://mintcdn.com/anthropic-claude-docs/4Bny2bjzuGBK7o00/images/agent-skills-bundling-content.png?w=840&fit=max&auto=format&n=4Bny2bjzuGBK7o00&q=85&s=82cd6286c966303f7dd914c28170e385 840w, https://mintcdn.com/anthropic-claude-docs/4Bny2bjzuGBK7o00/images/agent-skills-bundling-content.png?w=1100&fit=max&auto=format&n=4Bny2bjzuGBK7o00&q=85&s=56f3be36c77e4fe4b523df209a6824c6 1100w, https://mintcdn.com/anthropic-claude-docs/4Bny2bjzuGBK7o00/images/agent-skills-bundling-content.png?w=1650&fit=max&auto=format&n=4Bny2bjzuGBK7o00&q=85&s=d22b5161b2075656417d56f41a74f3dd 1650w, https://mintcdn.com/anthropic-claude-docs/4Bny2bjzuGBK7o00/images/agent-skills-bundling-content.png?w=2500&fit=max&auto=format&n=4Bny2bjzuGBK7o00&q=85&s=3dd4bdd6850ffcc96c6c45fcb0acd6eb 2500w" />

The complete Skill directory structure might look like this:

pdf/
├── SKILL.md              # Main instructions (loaded when triggered)
├── FORMS.md              # Form-filling guide (loaded as needed)
├── reference.md          # API reference (loaded as needed)
├── examples.md           # Usage examples (loaded as needed)
└── scripts/
    ├── analyze_form.py   # Utility script (executed, not loaded)
    ├── fill_form.py      # Form filling script
    └── validate.py       # Validation script

Pattern 1: High-level guide with references

---
name: PDF Processing
description: Extracts text and tables from PDF files, fills forms, and merges documents. Use when working with PDF files or when the user mentions PDFs, forms, or document extraction.
---

# PDF Processing

## Quick start

Extract text with pdfplumber:
```python
import pdfplumber
with pdfplumber.open("file.pdf") as pdf:
    text = pdf.pages[0].extract_text()
```

## 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.

Pattern 2: Domain-specific organization

For Skills with multiple domains, organize content by domain to avoid loading irrelevant context. When a user asks about sales metrics, Claude only needs to read sales-related schemas, not finance or marketing data. This keeps token usage low and context focused.

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)

# BigQuery Data Analysis

## Available datasets

**Finance**: Revenue, ARR, billing → See [reference/finance.md](reference/finance.md)
**Sales**: Opportunities, pipeline, accounts → See [reference/sales.md](reference/sales.md)
**Product**: API usage, features, adoption → See [reference/product.md](reference/product.md)
**Marketing**: Campaigns, attribution, email → See [reference/marketing.md](reference/marketing.md)

## Quick search

Find specific metrics using grep:

```bash
grep -i "revenue" reference/finance.md
grep -i "pipeline" reference/sales.md
grep -i "api usage" reference/product.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.

Avoid deeply nested references

Claude may partially read files when they're referenced from other referenced files. When encountering nested references, Claude might use commands like head -100 to preview content rather than reading entire files, resulting in incomplete information.

Keep references one level deep from SKILL.md. All reference files should link directly from SKILL.md to ensure Claude reads complete files when needed.

Bad example: Too deep:

# SKILL.md
See [advanced.md](advanced.md)...

# advanced.md
See [details.md](details.md)...

# details.md
Here's the actual information...

Good example: One level deep:

# SKILL.md

**Basic usage**: [instructions in SKILL.md]
**Advanced features**: See [advanced.md](advanced.md)
**API reference**: See [reference.md](reference.md)
**Examples**: See [examples.md](examples.md)

Structure longer reference files with table of contents

For reference files longer than 100 lines, include a table of contents at the top. This ensures Claude can see the full scope of available information even when previewing with partial reads.

Example:

# API Reference

## Contents
- Authentication and setup
- Core methods (create, read, update, delete)
- Advanced features (batch operations, webhooks)
- Error handling patterns
- Code examples

## Authentication and setup
...

## Core methods
...

Claude can then read the complete file or jump to specific sections as needed.

For details on how this filesystem-based architecture enables progressive disclosure, see the Runtime environment section in the Advanced section below.

Workflows and feedback loops

Use workflows for complex tasks

Break complex operations into clear, sequential steps. For particularly complex workflows, provide a checklist that Claude can copy into its response and check off as it progresses.

Example 1: Research synthesis workflow (for Skills without code):

## Research synthesis workflow

Copy this checklist and track your progress:

```
Research Progress:
- [ ] Step 1: Read all source documents
- [ ] Step 2: Identify key themes
- [ ] Step 3: Cross-reference claims
- [ ] Step 4: Create structured summary
- [ ] Step 5: Verify citations
```

**Step 1: Read all source documents**

Review each document in the `sources/` directory. Note the main arguments and supporting evidence.

**Step 2: Identify key themes**

Look for patterns across sources. What themes appear repeatedly? Where do sources agree or disagree?

**Step 3: Cross-reference claims**

For each major claim, verify it appears in the source material. Note which source supports each point.

**Step 4: Create structured summary**

Organize findings by theme. Include:
- Main claim
- Supporting evidence from sources
- Conflicting viewpoints (if any)

**Step 5: Verify citations**

Check that every claim references the correct source document. If citations are incomplete, return to Step 3.

This example shows how workflows apply to analysis tasks that don't require code. The checklist pattern works for any complex, multi-step process.

Example 2: PDF form filling workflow (for Skills with code):

## PDF form filling workflow

Copy this checklist and check off items as you complete them:

```
Task Progress:
- [ ] Step 1: Analyze the form (run analyze_form.py)
- [ ] Step 2: Create field mapping (edit fields.json)
- [ ] Step 3: Validate mapping (run validate_fields.py)
- [ ] Step 4: Fill the form (run fill_form.py)
- [ ] Step 5: Verify output (run verify_output.py)
```

**Step 1: Analyze the form**

Run: `python scripts/analyze_form.py input.pdf`

This extracts form fields and their locations, saving to `fields.json`.

**Step 2: Create field mapping**

Edit `fields.json` to add values for each field.

**Step 3: Validate mapping**

Run: `python scripts/validate_fields.py fields.json`

Fix any validation errors before continuing.

**Step 4: Fill the form**

Run: `python scripts/fill_form.py input.pdf fields.json output.pdf`

**Step 5: Verify output**

Run: `python scripts/verify_output.py output.pdf`

If verification fails, return to Step 2.

Clear steps prevent Claude from skipping critical validation. The checklist helps both Claude and you track progress through multi-step workflows.

Implement feedback loops

Common pattern: Run validator → fix errors → repeat

This pattern greatly improves output quality.

Example 1: Style guide compliance (for Skills without code):

## Content review process

1. Draft your content following the guidelines in STYLE_GUIDE.md
2. Review against the checklist:
   - Check terminology consistency
   - Verify examples follow the standard format
   - Confirm all required sections are present
3. If issues found:
   - Note each issue with specific section reference
   - Revise the content
   - Review the checklist again
4. Only proceed when all requirements are met
5. Finalize and save the document

This shows the validation loop pattern using reference documents instead of scripts. The "validator" is STYLE_GUIDE.md, and Claude performs the check by reading and comparing.

Example 2: Document editing process (for Skills with code):

## Document editing process

1. Make your edits to `word/document.xml`
2. **Validate immediately**: `python ooxml/scripts/validate.py unpacked_dir/`
3. If validation fails:
   - Review the error message carefully
   - Fix the issues in the XML
   - Run validation again
4. **Only proceed when validation passes**
5. Rebuild: `python ooxml/scripts/pack.py unpacked_dir/ output.docx`
6. Test the output document

The validation loop catches errors early.

Content guidelines

Avoid time-sensitive information

Don't include information that will become outdated:

Bad example: Time-sensitive (will become wrong):

If you're doing this before August 2025, use the old API.
After August 2025, use the new API.

Good example (use "old patterns" section):

## Current method

Use the v2 API endpoint: `api.example.com/v2/messages`

## Old patterns

<details>
<summary>Legacy v1 API (deprecated 2025-08)</summary>

The v1 API used: `api.example.com/v1/messages`

This endpoint is no longer supported.
</details>

The old patterns section provides historical context without cluttering the main content.

Use consistent terminology

Choose one term and use it throughout the Skill:

Good - Consistent:

• Always "API endpoint"
• Always "field"
• Always "extract"

Bad - Inconsistent:

• Mix "API endpoint", "URL", "API route", "path"
• Mix "field", "box", "element", "control"
• Mix "extract", "pull", "get", "retrieve"

Consistency helps Claude understand and follow instructions.

Common patterns

Template pattern

Provide templates for output format. Match the level of strictness to your needs.

For strict requirements (like API responses or data formats):

## Report structure

ALWAYS use this exact template structure:

```markdown
# [Analysis Title]

## Executive summary
[One-paragraph overview of key findings]

## Key findings
- Finding 1 with supporting data
- Finding 2 with supporting data
- Finding 3 with supporting data

## Recommendations
1. Specific actionable recommendation
2. Specific actionable recommendation
```

For flexible guidance (when adaptation is useful):

## Report structure

Here is a sensible default format, but use your best judgment based on the analysis:

```markdown
# [Analysis Title]

## Executive summary
[Overview]

## Key findings
[Adapt sections based on what you discover]

## Recommendations
[Tailor to the specific context]
```

Adjust sections as needed for the specific analysis type.

Examples pattern

For Skills where output quality depends on seeing examples, provide input/output pairs just like in regular prompting:

## Commit message format

Generate commit messages following these examples:

**Example 1:**
Input: Added user authentication with JWT tokens
Output:
```
feat(auth): implement JWT-based authentication

Add login endpoint and token validation middleware
```

**Example 2:**
Input: Fixed bug where dates displayed incorrectly in reports
Output:
```
fix(reports): correct date formatting in timezone conversion

Use UTC timestamps consistently across report generation
```

**Example 3:**
Input: Updated dependencies and refactored error handling
Output:
```
chore: update dependencies and refactor error handling

- Upgrade lodash to 4.17.21
- Standardize error response format across endpoints
```

Follow this style: type(scope): brief description, then detailed explanation.

Examples help Claude understand the desired style and level of detail more clearly than descriptions alone.

Conditional workflow pattern

Guide Claude through decision points:

## Document modification workflow

1. Determine the modification type:

   **Creating new content?** → Follow "Creation workflow" below
   **Editing existing content?** → Follow "Editing workflow" below

2. Creation workflow:
   - Use docx-js library
   - Build document from scratch
   - Export to .docx format

3. Editing workflow:
   - Unpack existing document
   - Modify XML directly
   - Validate after each change
   - Repack when complete

<Tip>
  If workflows become large or complicated with many steps, consider pushing them into separate files and tell Claude to read the appropriate file based on the task at hand.
</Tip>

Evaluation and iteration

Build evaluations first

Create evaluations BEFORE writing extensive documentation. This ensures your Skill solves real problems rather than documenting imagined ones.

Evaluation-driven development:

1. Identify gaps: Run Claude on representative tasks without a Skill. Document specific failures or missing context
2. Create evaluations: Build three scenarios that test these gaps
3. Establish baseline: Measure Claude's performance without the Skill
4. Write minimal instructions: Create just enough content to address the gaps and pass evaluations
5. Iterate: Execute evaluations, compare against baseline, and refine

This approach ensures you're solving actual problems rather than anticipating requirements that may never materialize.

Evaluation structure:

{
  "skills": ["pdf-processing"],
  "query": "Extract all text from this PDF file and save it to output.txt",
  "files": ["test-files/document.pdf"],
  "expected_behavior": [
    "Successfully reads the PDF file using an appropriate PDF processing library or command-line tool",
    "Extracts text content from all pages in the document without missing any pages",
    "Saves the extracted text to a file named output.txt in a clear, readable format"
  ]
}

<Note>
  This example demonstrates a data-driven evaluation with a simple testing rubric. We do not currently provide a built-in way to run these evaluations. Users can create their own evaluation system. Evaluations are your source of truth for measuring Skill effectiveness.
</Note>

Develop Skills iteratively with Claude

The most effective Skill development process involves Claude itself. Work with one instance of Claude ("Claude A") to create a Skill that will be used by other instances ("Claude B"). Claude A helps you design and refine instructions, while Claude B tests them in real tasks. This works because Claude models understand both how to write effective agent instructions and what information agents need.

Creating a new Skill:

1. Complete a task without a Skill: Work through a problem with Claude A using normal prompting. As you work, you'll naturally provide context, explain preferences, and share procedural knowledge. Notice what information you repeatedly provide.

2. Identify the reusable pattern: After completing the task, identify what context you provided that would be useful for similar future tasks.

   Example: If you worked through a BigQuery analysis, you might have provided table names, field definitions, filtering rules (like "always exclude test accounts"), and common query patterns.

3. Ask Claude A to create a Skill: "Create a Skill that captures this BigQuery analysis pattern we just used. Include the table schemas, naming conventions, and the rule about filtering test accounts."

   <Tip>
     Claude models understand the Skill format and structure natively. You don't need special system prompts or a "writing skills" skill to get Claude to help create Skills. Simply ask Claude to create a Skill and it will generate properly structured SKILL.md content with appropriate frontmatter and body content.
   </Tip>

4. Review for conciseness: Check that Claude A hasn't added unnecessary explanations. Ask: "Remove the explanation about what win rate means - Claude already knows that."

5. Improve information architecture: Ask Claude A to organize the content more effectively. For example: "Organize this so the table schema is in a separate reference file. We might add more tables later."

6. Test on similar tasks: Use the Skill with Claude B (a fresh instance with the Skill loaded) on related use cases. Observe whether Claude B finds the right information, applies rules correctly, and handles the task successfully.

7. Iterate based on observation: If Claude B struggles or misses something, return to Claude A with specifics: "When Claude used this Skill, it forgot to filter by date for Q4. Should we add a section about date filtering patterns?"

Iterating on existing Skills:

The same hierarchical pattern continues when improving Skills. You alternate between:

• Working with Claude A (the expert who helps refine the Skill)
• Testing with Claude B (the agent using the Skill to perform real work)
• Observing Claude B's behavior and bringing insights back to Claude A

1. Use the Skill in real workflows: Give Claude B (with the Skill loaded) actual tasks, not test scenarios

2. Observe Claude B's behavior: Note where it struggles, succeeds, or makes unexpected choices

   Example observation: "When I asked Claude B for a regional sales report, it wrote the query but forgot to filter out test accounts, even though the Skill mentions this rule."

3. Return to Claude A for improvements: Share the current SKILL.md and describe what you observed. Ask: "I noticed Claude B forgot to filter test accounts when I asked for a regional report. The Skill mentions filtering, but maybe it's not prominent enough?"

4. Review Claude A's suggestions: Claude A might suggest reorganizing to make rules more prominent, using stronger language like "MUST filter" instead of "always filter", or restructuring the workflow section.

5. Apply and test changes: Update the Skill with Claude A's refinements, then test again with Claude B on similar requests

6. Repeat based on usage: Continue this observe-refine-test cycle as you encounter new scenarios. Each iteration improves the Skill based on real agent behavior, not assumptions.

Gathering team feedback:

1. Share Skills with teammates and observe their usage
2. Ask: Does the Skill activate when expected? Are instructions clear? What's missing?
3. Incorporate feedback to address blind spots in your own usage patterns

Why this approach works: Claude A understands agent needs, you provide domain expertise, Claude B reveals gaps through real usage, and iterative refinement improves Skills based on observed behavior rather than assumptions.