AI Skill Report Card
AI Skill Discovery
AI Skill Discovery & Analysis
Quick start - analyze a skill marketplace:
Analyze the top 10 skills by usage from this marketplace data and identify:
1. Most popular skill categories
2. Common naming patterns
3. Usage distribution patterns
4. Emerging trends in AI agent capabilities
Workflow12 / 15
Progress:
- Parse skill marketplace data (names, descriptions, usage stats)
- Categorize skills by functionality and domain
- Analyze usage patterns and popularity metrics
- Identify naming conventions and best practices
- Extract capability trends and gaps
- Generate insights and recommendations
Analysis Framework
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Skill Categorization
- Document processing (PDF, DOCX, XLSX, PPTX)
- Content generation (presentations, visuals, reports)
- Research & analysis (market research, SEO, trends)
- Development tools (UI/UX, app building, APIs)
- Business automation (lead generation, social media)
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Usage Pattern Analysis
- High-usage skills (>3000 uses)
- Medium-usage skills (1000-3000 uses)
- Emerging skills (<1000 uses)
- Usage distribution curves
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Naming Convention Extraction
- Gerund forms vs. noun forms
- Domain-specific prefixes
- Length patterns
- Descriptiveness levels
Recommendation▾
Quick Start needs actual concrete data input/output - provide a specific marketplace dataset example with real skill names and usage numbers
Examples15 / 20
Example 1: Input: MiniMax skill marketplace with 20 skills ranging from 1000-6000 uses Output:
- Document processing dominates (35% of top skills)
- Average usage for document skills: 4,200 uses
- Naming pattern: tool-focused names perform better
- Gap identified: Real-time data processing skills underrepresented
Example 2: Input: Community-contributed skills vs. platform-official skills Output:
- Official skills average 3.2x higher usage
- Community skills show more specialized/niche capabilities
- Innovation pipeline: Community → Official adoption pattern
Recommendation▾
Examples lack concrete input/output pairs - show actual skill lists with specific names, usage counts, and the exact analysis output
Best Practices
- Usage Metrics as Indicators: Higher usage often correlates with broader applicability
- Category Clustering: Group similar skills to identify oversaturated vs. underserved areas
- Naming Analysis: Extract patterns that correlate with higher adoption
- Gap Identification: Look for missing capabilities in popular categories
- Trend Forecasting: Early adopters of emerging skills indicate future directions
Common Pitfalls
- Don't assume usage equals quality - some niche skills serve critical functions
- Avoid overemphasizing single metrics - consider recency, complexity, and specialization
- Don't ignore low-usage skills - they may serve specialized but valuable use cases
- Avoid category bias - some domains naturally have lower usage but high value