skill-writer

name	skill-writer
description	Guide users through creating portable AI skills. Use when the user wants to create, write, author, or redesign a skill, or needs help with SKILL.md files, frontmatter, portability, or skill structure.

Skill Writer

This Skill helps you create well-structured AI skills that follow best practices, remain English-first, and stay portable across assistants, IDEs, and apps.

Overview

Use this skill to create or refactor SKILL.md files with a portable structure, consistent tone, and reusable examples.

When to Use This Skill

Use this Skill when:

• Creating a new skill
• Writing or updating SKILL.md files
• Designing skill structure and frontmatter
• Troubleshooting skill discovery issues
• Converting existing prompts or workflows into skills

Instructions

Step 1: Determine Skill scope

First, understand what the Skill should do:

1. Ask clarifying questions:

   • What specific capability should this Skill provide?
   • When should the AI use this Skill?
   • What tools or resources does it need?
   • Is this for personal use or team sharing?

2. Keep it focused: One Skill = one capability

   • Good: "PDF form filling", "Excel data analysis"
   • Too broad: "Document processing", "Data tools"

Step 2: Choose Skill location

Determine where to create the Skill:

Personal Skills (<user-skill-root>/skill-name/):

• Individual workflows and preferences
• Experimental Skills
• Personal productivity tools

Project Skills (<workspace-skill-root>/skill-name/):

• Team workflows and conventions
• Project-specific expertise
• Shared utilities (committed to git)

Choose paths that match the host application. The path examples above are placeholders, not a required directory layout.

Step 3: Create Skill structure

Create the directory and files:

# Personal
mkdir -p <user-skill-root>/skill-name

# Project
mkdir -p <workspace-skill-root>/skill-name

For multi-file Skills:

skill-name/
├── SKILL.md (required)
├── reference.md (optional)
├── examples.md (optional)
├── scripts/
│   └── helper.py (optional)
└── templates/
    └── template.txt (optional)

Step 4: Write SKILL.md frontmatter

Create YAML frontmatter with required fields:

---
name: skill-name
description: Brief description of what this does and when to use it
---

Field requirements:

• name:

  • Lowercase letters, numbers, hyphens only
  • Max 64 characters
  • Must match directory name
  • Good: pdf-processor, git-commit-helper
  • Bad: PDF_Processor, Git Commits!

• description:

  • Max 1024 characters
  • Include BOTH what it does AND when to use it
  • Use specific trigger words users would say
  • Mention file types, operations, and context

Optional frontmatter fields:

• allowed-tools: Restrict tool access (comma-separated list)

  allowed-tools: Read, Grep, Glob
  

  Use for:
  • Read-only Skills
  • Security-sensitive workflows
  • Limited-scope operations

Step 5: Write effective descriptions

The description is critical for the AI to discover your Skill.

Formula: [What it does] + [When to use it] + [Key triggers]

Examples:

✅ Good:

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.

✅ Good:

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

❌ Too vague:

description: Helps with documents
description: For data analysis

Tips:

• Include specific file extensions (.pdf, .xlsx, .json)
• Mention common user phrases ("analyze", "extract", "generate")
• List concrete operations (not generic verbs)
• Add context clues ("Use when...", "For...")
• Avoid naming a specific IDE or assistant unless that dependency is essential

Step 6: Structure the Skill content

Use clear Markdown sections:

# Skill Name

Brief overview of what this Skill does.

## Quick Start

Provide a simple example to get started immediately.

## Instructions

Step-by-step guidance for the AI:
1. First step with clear action
2. Second step with expected outcome
3. Handle edge cases

## Examples

Show concrete usage examples with code or commands.

## Best Practices

- Key conventions to follow
- Common pitfalls to avoid
- When to use vs. not use

## Requirements

List any dependencies or prerequisites:
```bash
pip install package-name

Advanced Usage

For complex scenarios, see reference.md.

### Step 7: Add supporting files (optional)

Create additional files for progressive disclosure:

**reference.md**: Detailed API docs, advanced options
**examples.md**: Extended examples and use cases
**scripts/**: Helper scripts and utilities
**templates/**: File templates or boilerplate

Reference them from SKILL.md:
```markdown
For advanced usage, see [reference.md](reference.md).

Run the helper script:
\`\`\`bash
python scripts/helper.py input.txt
\`\`\`

Step 8: Validate the Skill

Check these requirements:

✅ File structure:

• SKILL.md exists in correct location
• Directory name matches frontmatter name

✅ YAML frontmatter:

• Opening --- on line 1
• Closing --- before content
• Valid YAML (no tabs, correct indentation)
• name follows naming rules
• description is specific and < 1024 chars

✅ Content quality:

• Clear instructions for the AI
• Concrete examples provided
• Edge cases handled
• Dependencies listed (if any)
• Main flow is portable across hosts
• English wording is consistent throughout

✅ Testing:

• Description matches user questions
• Skill activates on relevant queries
• Instructions are clear and actionable

Step 9: Test the Skill

1. Reload or restart the host application if it caches skills

2. Ask relevant questions that match the description:

   Can you help me extract text from this PDF?
   

3. Verify activation: The host should load or expose the skill as expected

4. Check behavior: Confirm the AI follows the instructions correctly

Step 10: Debug if needed

If the host does not use the Skill:

1. Make description more specific:

   • Add trigger words
   • Include file types
   • Mention common user phrases

2. Check file location:

   ls <user-skill-root>/skill-name/SKILL.md
   ls <workspace-skill-root>/skill-name/SKILL.md
   

3. Validate YAML:

   cat SKILL.md | head -n 10
   

4. Use host-specific diagnostics if available:

   • Enable the host's verbose or debug mode
   • Check whether the host requires a restart, reindex, or explicit registration

Portability Checklist

• Keep the primary instructions valid without assuming a specific IDE or assistant
• Use placeholders for directories, commands, and environment-specific values
• Move vendor-specific setup into a small compatibility note or reference file
• Keep examples in English and avoid mixed-language docs inside the skill

Common Patterns

Read-only Skill

---
name: code-reader
description: Read and analyze code without making changes. Use for code review, understanding codebases, or documentation.
allowed-tools: Read, Grep, Glob
---

Script-based Skill

---
name: data-processor
description: Process CSV and JSON data files with Python scripts. Use when analyzing data files or transforming datasets.
---

# Data Processor

## Instructions

1. Use the processing script:
\`\`\`bash
python scripts/process.py input.csv --output results.json
\`\`\`

2. Validate output with:
\`\`\`bash
python scripts/validate.py results.json
\`\`\`

Multi-file Skill with progressive disclosure

---
name: api-designer
description: Design REST APIs following best practices. Use when creating API endpoints, designing routes, or planning API architecture.
---

# API Designer

Quick start: See [examples.md](examples.md)

Detailed reference: See [reference.md](reference.md)

## Instructions

1. Gather requirements
2. Design endpoints (see examples.md)
3. Document with OpenAPI spec
4. Review against best practices (see reference.md)

Best Practices for Skill Authors

1. One Skill, one purpose: Don't create mega-Skills
2. Specific descriptions: Include trigger words users will say
3. Clear instructions: Write for the AI, not humans
4. Concrete examples: Show real code, not pseudocode
5. List dependencies: Mention required packages in description
6. Test with teammates: Verify activation and clarity
7. Version your Skills: Document changes in content
8. Use progressive disclosure: Put advanced details in separate files

Validation Checklist

Before finalizing a Skill, verify:

• Name is lowercase, hyphens only, max 64 chars
• Description is specific and < 1024 chars
• Description includes "what" and "when"
• YAML frontmatter is valid
• Instructions are step-by-step
• Examples are concrete and realistic
• Dependencies are documented
• File paths use forward slashes
• Skill activates on relevant queries
• AI follows instructions correctly

Troubleshooting

Skill doesn't activate:

• Make description more specific with trigger words
• Include file types and operations in description
• Add "Use when..." clause with user phrases

Multiple Skills conflict:

• Make descriptions more distinct
• Use different trigger words
• Narrow the scope of each Skill

Skill has errors:

• Check YAML syntax (no tabs, proper indentation)
• Verify file paths (use forward slashes)
• Ensure scripts have execute permissions
• List all dependencies

Examples

See the documentation for complete examples:

• Simple single-file Skill (commit-helper)
• Skill with tool permissions (code-reviewer)
• Multi-file Skill (pdf-processing)

Output format

When creating a Skill, I will:

1. Ask clarifying questions about scope and requirements
2. Suggest a Skill name and location
3. Create the SKILL.md file with proper frontmatter
4. Include clear instructions and examples
5. Add supporting files if needed
6. Provide testing instructions
7. Validate against all requirements

The result will be a complete, working Skill that follows all best practices and validation rules.