Implement systematic evaluation pipeline to measure, track, and optimize SuperClaude's performance across all dimensions using Context Engineering principles.
Implements systematic evaluation pipelines to measure, track, and optimize SuperClaude's performance. Provides real-time metrics collection, A/B testing frameworks, and data-driven optimization recommendations.
/plugin marketplace add SuperClaude-Org/SuperClaude_Plugin/plugin install sc@superclaudeImplement systematic evaluation pipeline to measure, track, and optimize SuperClaude's performance across all dimensions using Context Engineering principles.
/sc:metrics command execution@agent-metrics-analyst "analyze last 100 commands"
/sc:metrics week --optimize
Data-Driven & Analytical:
## 📊 Performance Analysis Summary
### Key Metrics (Last 7 Days)
┌─────────────────────┬──────────┬────────────┐
│ Metric │ Current │ vs Previous│
├─────────────────────┼──────────┼────────────┤
│ Total Commands │ 2,847 │ +12% │
│ Avg Tokens/Command │ 3,421 │ -8% ✅ │
│ Avg Latency │ 2.3s │ +0.1s │
│ Quality Score │ 0.89 │ ↑ from 0.85│
│ Estimated Cost │ $47.23 │ -15% ✅ │
└─────────────────────┴──────────┴────────────┘
### Top Performing Commands
1. `/sc:implement` - 0.92 quality, 2,145 avg tokens
2. `/sc:refactor` - 0.91 quality, 1,876 avg tokens
3. `/sc:design` - 0.88 quality, 2,543 avg tokens
### 🎯 Optimization Opportunities
**High Impact**: Compress `/sc:research` output (-25% tokens, no quality loss)
**Medium Impact**: Cache common patterns in `/sc:analyze` (-12% latency)
**Low Impact**: Optimize agent activation logic (-5% overhead)
### Recommended Actions
1. ✅ Implement token compression for research mode
2. 📊 Run A/B test on analyze command optimization
3. 🔍 Monitor quality impact of proposed changes
{
"session_id": "sess_20251011_001",
"commands_executed": 47,
"cumulative_tokens": 124567,
"cumulative_latency_ms": 189400,
"quality_scores": [0.91, 0.88, 0.93],
"anomalies_detected": [],
"agent_activations": {
"system-architect": 12,
"backend-engineer": 18
}
}
Database: ~/.claude/metrics/metrics.db (SQLite)
Tables:
command_metrics - All command executionsagent_performance - Agent-specific metricsoptimization_history - A/B test resultsuser_patterns - Usage patterns per userCREATE TABLE command_metrics (
id INTEGER PRIMARY KEY AUTOINCREMENT,
timestamp DATETIME NOT NULL,
command VARCHAR(50) NOT NULL,
tokens_used INTEGER NOT NULL,
latency_ms INTEGER NOT NULL,
quality_score REAL CHECK(quality_score >= 0 AND quality_score <= 1),
agent_activated VARCHAR(100),
user_rating INTEGER CHECK(user_rating >= 1 AND user_rating <= 5),
session_id VARCHAR(50),
cost_usd REAL,
context_size INTEGER,
compression_ratio REAL
);
CREATE INDEX idx_timestamp ON command_metrics(timestamp);
CREATE INDEX idx_command ON command_metrics(command);
CREATE INDEX idx_session ON command_metrics(session_id);
CREATE TABLE agent_performance (
id INTEGER PRIMARY KEY AUTOINCREMENT,
agent_name VARCHAR(50) NOT NULL,
activation_count INTEGER DEFAULT 0,
avg_quality REAL,
avg_tokens INTEGER,
success_rate REAL,
last_activated DATETIME,
total_cost_usd REAL
);
CREATE TABLE optimization_experiments (
id INTEGER PRIMARY KEY AUTOINCREMENT,
experiment_name VARCHAR(100) NOT NULL,
variant_a TEXT,
variant_b TEXT,
start_date DATETIME,
end_date DATETIME,
winner VARCHAR(10),
improvement_pct REAL,
statistical_significance REAL,
p_value REAL
);
{
"metric_type": "command_execution",
"timestamp": "2025-10-11T15:30:00Z",
"source_agent": "system-architect",
"metrics": {
"tokens": 2341,
"latency_ms": 2100,
"quality_score": 0.92,
"user_satisfaction": 5,
"context_tokens": 1840,
"output_tokens": 501
}
}
# Auto-activation example
@metrics_analyst.record
def execute_command(command: str, args: dict):
start_time = time.time()
result = super_claude.run(command, args)
latency = (time.time() - start_time) * 1000
metrics_analyst.record_execution({
'command': command,
'tokens_used': result.tokens,
'latency_ms': latency,
'quality_score': result.quality
})
return result
/sc:metrics session - Current session metrics/sc:metrics week - Weekly performance report/sc:metrics optimize - Optimization recommendations/sc:metrics export csv - Export data for analysisVersion: 1.0.0
Status: Ready for Implementation
Priority: P0 (Critical for Context Engineering compliance)
You are an elite AI agent architect specializing in crafting high-performance agent configurations. Your expertise lies in translating user requirements into precisely-tuned agent specifications that maximize effectiveness and reliability.