Generating Claude Skills
Generates complete Claude Code agent skills with proper YAML structure, workflow sections, and examples. Use when asked to "create a skill", "build a skill", or "generate skill".
Quick Start
When someone says "請幫我建立skill" or "create a skill for X":
YAML--- name: [gerund-action] description: [Third-person description]. Use when [trigger phrases]. --- # Quick Start [Immediate actionable example] # Workflow Progress: - [ ] Step 1: [Action] - [ ] Step 2: [Action] # Examples **Example 1:** Input: [specific] Output: [specific] # Best Practices - Guideline 1 - Guideline 2 # Common Pitfalls - Don't do X - Avoid Y
Workflow
Progress:
- Extract core action from user request
- Convert to gerund-form kebab-case name
- Write trigger-focused description
- Create actionable Quick Start
- Build step-by-step workflow with checklist
- Add concrete input/output examples
Step 1: Name Generation Framework
- Identify verb: "analyze" → "analyzing"
- Add object: "analyzing-data", "writing-proposals"
- Check length: max 64 characters
- Validate kebab-case: lowercase + hyphens only
Step 2: Description Template Format: "[Action verb]s [object] and [outcome]. Use when [trigger phrases]."
- "Analyzes datasets and generates insights. Use when asked to 'analyze data' or 'examine dataset'."
Step 3: Content Structure Template
- Quick Start: Code/steps, no explanation
- Workflow: Numbered steps + progress checklist
- Examples: Minimum 2 input/output pairs
- Best Practices: 3-5 actionable guidelines
- Common Pitfalls: What NOT to do
Examples
Example 1: Input: "幫我建立一個分析CSV數據的skill" Output: Complete SKILL.md file:
YAML--- name: analyzing-csv-data description: Analyzes CSV datasets and generates statistical insights with visualizations. Use when asked to "analyze CSV", "examine data file", or "process spreadsheet". --- # Quick Start ```python import pandas as pd df = pd.read_csv('data.csv') print(df.describe()) df.hist()
Workflow
Progress:
- Load and validate CSV file
- Generate descriptive statistics
- Create visualizations
- Identify patterns and outliers
Step 1: Load data with error handling Step 2: Check data types and missing values Step 3: Generate summary statistics Step 4: Create relevant plots
Examples
Example 1: Input: sales_data.csv with columns: date, product, revenue Output: Summary stats, time series plot, top products
Best Practices
- Always check for missing values first
- Use appropriate plot types for data
- Include data validation steps
Common Pitfalls
- Don't assume clean data
- Don't create too many plots
**Example 2:**
Input: "Create a skill for API documentation"
Output:
```yaml
---
name: documenting-apis
description: Creates comprehensive API documentation with endpoints, examples, and schemas. Use when asked to "document API", "write API docs", or "create API reference".
---
# Quick Start
```markdown
Creates a new user account.
Request:
JSON{"name": "John", "email": "john@example.com"}
Response:
JSON{"id": 123, "name": "John", "created_at": "2024-01-01T00:00:00Z"}
Workflow
Progress:
- List all endpoints
- Document request/response formats
- Add authentication requirements
- Include error codes
Examples
Example 1: Input: REST API with user management Output: Complete OpenAPI spec with examples
Best Practices
- Include realistic examples
- Document error responses
- Show authentication headers
Common Pitfalls
- Don't skip error documentation
- Don't use fake/unrealistic data
# Best Practices
- **Name Convention**: Always gerund + object (`processing-images`, not `image-processor`)
- **Trigger Optimization**: Include exact phrases users say ("analyze this", "create docs")
- **Quick Start Rule**: First code block should be copy-pasteable
- **Example Quality**: Use realistic data, show complete workflows
- **Checklist Strategy**: Break complex tasks into trackable steps
- **Length Target**: Aim for 400-600 lines total
# Common Pitfalls
- Don't use abstract examples - show actual file contents
- Don't write "I will" or "you can" - use third person action verbs
- Don't create mega-skills - split complex domains into focused skills
- Don't forget progress checklists for multi-step workflows
- Don't explain basic concepts (what JSON is, how functions work)
- Don't provide multiple options - give the best single approach