AI Skill Report Card
Building Claude Agent Skills
Creates and structures Claude Code agent skills following best practices. Use when building agent capabilities, documenting workflows, or packaging expertise into reusable skills.
Quick Start15 / 15
YAML--- name: analyzing-data-trends description: Analyzes time-series data to identify patterns and anomalies. Use when examining datasets for insights or trend detection. --- # Data Trend Analysis
Recommendation▾
Add more concrete examples of common edge cases and how to handle them
Quick Start15 / 15
Pythonimport pandas as pd import matplotlib.pyplot as plt df = pd.read_csv('data.csv') df['date'] = pd.to_datetime(df['date']) df.set_index('date', inplace=True) trend = df.rolling(window=7).mean() plt.plot(df.index, df.values, alpha=0.3) plt.plot(trend.index, trend.values, linewidth=2) plt.show()
Recommendation▾
Include template frameworks for different skill types (analysis, automation, review, etc.)
Workflow15 / 15
- Load and clean data
- Convert to time series format
- Apply smoothing techniques
- Identify patterns and outliers
- Generate visualizations
Recommendation▾
Provide guidance on skill testing and validation before deployment
Workflow15 / 15
Progress:
- Analyze the expertise/methodology to extract
- Create YAML frontmatter with gerund name and trigger-based description
- Write Quick Start with immediate actionable example
- Document step-by-step workflow with checklist format
- Provide concrete input/output examples
- List best practices and common pitfalls
- Keep under 500 lines, be concise and specific
Examples17 / 20
Example 1: Input: "I help companies optimize their database queries for better performance" Output:
YAML--- name: optimizing-database-queries description: Analyzes and improves SQL query performance through indexing and restructuring. Use when queries are slow or database performance needs improvement. --- # Database Query Optimization
Quick Start15 / 15
SQL-- Before: Slow query SELECT * FROM orders WHERE customer_id = 123 AND order_date > '2024-01-01'; -- After: Optimized with index CREATE INDEX idx_customer_date ON orders(customer_id, order_date); SELECT order_id, total FROM orders WHERE customer_id = 123 AND order_date > '2024-01-01';
**Example 2:**
Input: "Expert in reviewing pull requests and ensuring code quality"
Output:
```yaml
---
name: reviewing-code-quality
description: Conducts thorough code reviews focusing on maintainability, security, and performance. Use when evaluating pull requests or auditing codebases.
---
# Code Quality Review
Quick Start15 / 15
Review checklist:
- Code follows style guidelines
- No security vulnerabilities
- Proper error handling
- Unit tests included
- Documentation updated
Best Practices
- Use gerund naming:
analyzing-data,writing-documentation,debugging-applications - Include triggers in description: "Use when [specific scenario]"
- Start with executable examples: Code, commands, or concrete steps
- Provide checklists for complex workflows: Use
- [ ]format for trackable progress - Be implementation-focused: Show HOW, not just WHAT
- Default to single best approach: Avoid option paralysis
- Target practitioners: Assume technical competence, focus on methodology
Common Pitfalls
- Vague descriptions: "Helps with data" vs "Analyzes customer churn patterns in subscription data"
- Missing triggers: Description should explain WHEN to use the skill
- Abstract examples: Show real input/output, not placeholders
- Over-explanation: Don't define common terms or basic concepts
- Analysis paralysis: Give one recommended approach, not multiple options
- Passive voice: Use active voice and action-oriented language
- Too generic: Skills should capture specific expertise, not general capabilities