| name | feedback-analyzer |
| description | Analyze skill effectiveness through usage feedback, metrics analysis, and outcome assessment. Task-based operations for feedback collection, effectiveness measurement, trend analysis, and insight extraction. Use when analyzing skill effectiveness, measuring ROI, understanding usage patterns, or evaluating toolkit impact based on real usage data. |
| allowed-tools | Read, Write, Edit, Glob, Grep, Bash, WebSearch, WebFetch |
Feedback Analyzer
Overview
feedback-analyzer evaluates skill effectiveness through analysis of usage data, feedback, metrics, and outcomes.
Purpose: Data-driven understanding of what works and what doesn't
The 4 Analysis Operations:
- Collect Usage Data - Gather metrics on skill usage and effectiveness
- Measure Effectiveness - Quantify impact and ROI of skills
- Analyze Trends - Identify patterns in usage and effectiveness
- Extract Insights - Generate actionable insights from data
When to Use
- After skills have been used (have usage data)
- Measuring toolkit ROI and impact
- Understanding which skills provide most value
- Identifying underutilized skills
- Data-driven improvement decisions
Operations
Operation 1: Collect Usage Data
Purpose: Gather data on how skills are used
Data Sources:
- Build times (how long to build skills?)
- Usage frequency (which skills used most?)
- Effectiveness metrics (do skills achieve purposes?)
- Quality scores (from reviews)
- User feedback (satisfaction, issues)
Process:
- Identify data sources
- Collect available metrics
- Document usage patterns
- Organize data for analysis
Output: Usage data collection
Time: 30-60 minutes
Operation 2: Measure Effectiveness
Purpose: Quantify skill impact and ROI
Metrics:
- Time savings (vs without tool)
- Quality improvements (before/after)
- Efficiency gains (percentage faster)
- Usage rate (frequency of use)
- Satisfaction (user ratings)
Process:
- Define effectiveness criteria
- Calculate metrics
- Compare to baseline or targets
- Assess ROI
Output: Effectiveness measurements with evidence
Time: 45-90 minutes
Operation 3: Analyze Trends
Purpose: Identify patterns in effectiveness over time
Process:
- Plot metrics over time
- Identify trends (improving/degrading/stable)
- Find correlations
- Detect anomalies
Output: Trend analysis with insights
Time: 45-90 minutes
Operation 4: Extract Insights
Purpose: Generate actionable insights from data
Process:
- Synthesize findings
- Identify high-impact insights
- Make recommendations
- Prioritize actions
Output: Data-driven insights and recommendations
Time: 30-60 minutes
Example Analysis
Effectiveness Analysis: Development Toolkit
===========================================
Usage Data (Skills 1-23):
- Build times: 2h - 20h (mean: 6.8h)
- Efficiency: 35% - 97% faster than baseline (mean: 85%)
- Quality: 100% pass rate (5/5 structure)
Effectiveness Metrics:
- Time Saved: 392 hours total (85% reduction)
- Quality: Maintained (100% Grade A)
- Completion: 100% (all 23 finished)
- ROI: 392h saved / 68h invested = 576% ROI
Trends:
✅ Improving: Efficiency compounds (72% → 97%)
✅ Stable: Quality consistent (all 5/5)
⚠️ Plateau: Efficiency plateaus ~85-90% for simple skills
Insights:
1. Toolkit highly effective (576% ROI, 85% efficiency)
2. Quality maintained despite speed (100% pass rate)
3. Efficiency plateaus at 85-90% (cannot exceed certain minimum times)
4. Complex skills still benefit (35-50% faster)
Recommendations:
1. Continue using toolkit (proven effective)
2. Expect 85-90% efficiency for simple/medium skills
3. Adjust estimates for complex skills (30-50% faster, not 85%)
4. Focus on quality maintenance (already excellent)
Quick Reference
| Operation | Focus | Time | Output |
|---|
| Collect Usage Data | Gather metrics | 30-60m | Data collection |
| Measure Effectiveness | Quantify impact, ROI | 45-90m | Effectiveness metrics |
| Analyze Trends | Patterns over time | 45-90m | Trend analysis |
| Extract Insights | Actionable insights | 30-60m | Recommendations |
Integration: Uses data from skill-evolution-tracker, analysis skill
feedback-analyzer provides data-driven understanding of toolkit effectiveness for evidence-based improvement decisions.
