AI Skill Report Card
Analyzing AI Platform Metrics
Quick Start8 / 15
Python# Platform health snapshot metrics = { 'topics': 57, 'experts': 42, 'knowledge_bases': 90, 'models': 19, 'agents': 11, 'users': 265 } growth_rates = { 'topics': 12, 'experts': 8, 'knowledge_bases': 15, 'models': 5, 'agents': 5, 'users': 20 } print("Platform Health Score:", calculate_health_score(metrics, growth_rates))
Recommendation▾
Replace the pseudocode Quick Start with concrete executable analysis code showing actual calculations and thresholds for health scoring
Workflow12 / 15
Progress:
- Collect core metrics (topics, experts, KBs, users, agents)
- Calculate growth rates and trends
- Analyze usage patterns and engagement
- Identify resource allocation opportunities
- Generate actionable insights
- Create executive summary with recommendations
Detailed Steps
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Metrics Collection
- Core resources: Topics, Experts, Knowledge Bases, Models, Agents
- User metrics: Total users, active users, growth rate
- Usage patterns: Chat frequency, peak times, response times
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Engagement Analysis
- Chat frequency trends over time periods
- Topic-expert relationship mapping
- Knowledge base access patterns
- Agent utilization rates
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Security & Access Review
- Access control distribution (Individual/Group/Organization)
- Knowledge base security posture
- Integration connectivity status
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Performance Assessment
- Response time analysis
- System adoption rates
- Resource utilization efficiency
- Error patterns from notifications
Recommendation▾
Transform abstract examples into real input/output pairs with actual metric dashboards and specific numeric insights rather than general recommendations
Examples10 / 20
Example 1: Input: Platform shows 265 users, 57 topics (+12%), 42 experts (+8%), 90 KBs (+15%) Output: "Strong growth trajectory. KB expansion (15%) outpacing user growth (implied) suggests deepening engagement. Recommend scaling expert capacity to match KB growth."
Example 2: Input: 81% of KBs are org-level access, 28.8% topics individual access, 7 active integrations Output: "Knowledge democratization strategy working (81% org access). Topic personalization high (28.8% individual). Integration ecosystem stable at 7 connections."
Recommendation▾
Add concrete calculation formulas and benchmarks for ratios, growth thresholds, and red flag indicators instead of just listing best practices
Best Practices
- Focus on ratios: Topics per expert (1.4), KBs per user (0.34), agents per expert (0.26)
- Track access patterns: Balance individual customization with organizational sharing
- Monitor notification patterns: Failed document processing indicates system stress
- Correlate growth rates: Identify resource bottlenecks before they impact performance
- Use time-series data: Daily chat frequency reveals usage patterns and adoption trends
Common Pitfalls
- Don't analyze metrics in isolation - look for correlations and dependencies
- Don't ignore failed notifications - they indicate system capacity issues
- Don't focus only on growth percentages - consider absolute numbers for capacity planning
- Don't overlook access distribution - security and collaboration balance is critical
- Don't assume high agent count means high utilization - check actual usage metrics