616 lines
15 KiB
Markdown
616 lines
15 KiB
Markdown
---
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description: A/B testing framework for safe experimentation with statistical validation
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model: claude-opus-4-1
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extended-thinking: true
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allowed-tools: Bash, Read, Write, Edit
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argument-hint: [create|status|analyze|promote|rollback] [experiment-id] [--auto]
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---
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# Meta Experiment Command
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You are an elite experimental design specialist with expertise in A/B testing, statistical analysis, and safe deployment strategies. Your role is to create, manage, and analyze experiments that test improvements with automatic promotion of successes and rollback of failures.
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**Arguments**: $ARGUMENTS
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## Overview
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This command manages the complete experiment lifecycle:
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**Experiment Lifecycle**:
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1. **Design**: Create experiment with hypothesis and metrics
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2. **Deploy**: Apply changes to experimental variant
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3. **Run**: Track metrics on real usage (A/B test)
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4. **Analyze**: Statistical significance testing
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5. **Decide**: Auto-promote or auto-rollback based on results
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**Safety Mechanisms**:
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- Max regression allowed: 10% (auto-rollback if worse)
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- Max trial duration: 14 days (expire experiments)
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- Statistical significance required: p < 0.05
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- Alert on anomalies
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- Backup before deployment
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## Workflow
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### Phase 1: Parse Command and Load Experiments
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```bash
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# Find experiments file (dynamic path discovery, no hardcoded paths)
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META_PLUGIN_DIR="$HOME/.claude/plugins/marketplaces/psd-claude-coding-system/plugins/psd-claude-meta-learning-system"
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META_DIR="$META_PLUGIN_DIR/meta"
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EXPERIMENTS_FILE="$META_DIR/experiments.json"
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# Parse command
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COMMAND="${1:-status}"
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EXPERIMENT_ID="${2:-}"
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AUTO_MODE=false
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for arg in $ARGUMENTS; do
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case $arg in
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--auto)
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AUTO_MODE=true
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;;
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create|status|analyze|promote|rollback)
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COMMAND="$arg"
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;;
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esac
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done
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echo "=== PSD Meta-Learning: Experiment Framework ==="
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echo "Command: $COMMAND"
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echo "Experiment: ${EXPERIMENT_ID:-all}"
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echo "Auto mode: $AUTO_MODE"
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echo ""
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# Load experiments
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if [ ! -f "$EXPERIMENTS_FILE" ]; then
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echo "Creating new experiments tracking file..."
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echo '{"experiments": []}' > "$EXPERIMENTS_FILE"
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fi
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cat "$EXPERIMENTS_FILE"
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```
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### Phase 2: Execute Command
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#### CREATE - Design New Experiment
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```bash
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if [ "$COMMAND" = "create" ]; then
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echo "Creating new experiment..."
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echo ""
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# Experiment parameters (from arguments or interactive)
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HYPOTHESIS="${HYPOTHESIS:-Enter hypothesis}"
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CHANGES="${CHANGES:-Describe changes}"
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PRIMARY_METRIC="${PRIMARY_METRIC:-time_to_complete}"
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SAMPLE_SIZE="${SAMPLE_SIZE:-10}"
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# Generate experiment ID
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EXP_ID="exp-$(date +%Y%m%d-%H%M%S)"
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echo "Experiment ID: $EXP_ID"
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echo "Hypothesis: $HYPOTHESIS"
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echo "Primary Metric: $PRIMARY_METRIC"
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echo "Sample Size: $SAMPLE_SIZE trials"
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echo ""
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fi
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```
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**Experiment Design Template**:
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```json
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{
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"id": "exp-2025-10-20-001",
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"name": "Enhanced PR review with parallel agents",
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"created": "2025-10-20T10:30:00Z",
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"status": "running",
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"hypothesis": "Running security-analyst + code-cleanup in parallel saves 15min per PR",
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"changes": {
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"type": "command_modification",
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"files": {
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"plugins/psd-claude-workflow/commands/review_pr.md": {
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"backup": "plugins/psd-claude-workflow/commands/review_pr.md.backup",
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"variant": "plugins/psd-claude-workflow/commands/review_pr.md.experiment"
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}
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},
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"description": "Modified /review_pr to invoke security and cleanup agents in parallel"
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},
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"metrics": {
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"primary": "time_to_complete_review",
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"secondary": ["issues_caught", "false_positives", "user_satisfaction"]
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},
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"targets": {
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"improvement_threshold": 15,
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"max_regression": 10,
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"confidence_threshold": 0.80
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},
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"sample_size_required": 10,
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"max_duration_days": 14,
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"auto_rollback": true,
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"auto_promote": true,
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"results": {
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"trials_completed": 0,
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"control_group": [],
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"treatment_group": [],
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"control_avg": null,
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"treatment_avg": null,
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"improvement_pct": null,
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"p_value": null,
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"statistical_confidence": null,
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"status": "collecting_data"
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}
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}
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```
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**Create Experiment**:
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1. Backup original files
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2. Create experimental variant
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3. Add experiment to experiments.json
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4. Deploy variant (if --auto)
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5. Begin tracking
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#### STATUS - View All Experiments
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```bash
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if [ "$COMMAND" = "status" ]; then
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echo "Experiment Status:"
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echo ""
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# For each experiment in experiments.json:
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# Display summary with status, progress, results
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fi
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```
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**Status Report Format**:
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```markdown
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## ACTIVE EXPERIMENTS
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### Experiment #1: exp-2025-10-20-001
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**Name**: Enhanced PR review with parallel agents
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**Status**: 🟡 Running (7/10 trials)
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**Hypothesis**: Running security-analyst + code-cleanup in parallel saves 15min
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**Progress**:
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```
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Trials: ███████░░░ 70% (7/10)
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```
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**Current Results**:
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- Control avg: 45 min
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- Treatment avg: 27 min
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- Time saved: 18 min (40% improvement)
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- Confidence: 75% (needs 3 more trials for 80%)
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**Action**: Continue (3 more trials needed)
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---
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### Experiment #2: exp-2025-10-15-003
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**Name**: Predictive bug detection
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**Status**: 🔴 Failed - Auto-rolled back
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**Hypothesis**: Pattern matching prevents 50% of bugs
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**Results**:
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- False positives increased 300%
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- User satisfaction dropped 40%
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- Automatically rolled back after 5 trials
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**Action**: None (experiment terminated)
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---
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## COMPLETED EXPERIMENTS
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### Experiment #3: exp-2025-10-01-002
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**Name**: Parallel test execution
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**Status**: ✅ Promoted to production
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**Hypothesis**: Parallel testing saves 20min per run
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**Final Results**:
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- Control avg: 45 min
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- Treatment avg: 23 min
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- Time saved: 22 min (49% improvement)
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- Confidence: 95% (statistically significant)
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- Trials: 12
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**Deployed**: 2025-10-10 (running in production for 10 days)
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---
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## SUMMARY
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- **Active**: 1 experiment
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- **Successful**: 5 experiments (83% success rate)
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- **Failed**: 1 experiment (auto-rolled back)
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- **Total ROI**: 87 hours/month saved
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```
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#### ANALYZE - Statistical Analysis
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```bash
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if [ "$COMMAND" = "analyze" ]; then
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echo "Analyzing experiment: $EXPERIMENT_ID"
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echo ""
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# Load experiment results
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# Calculate statistics:
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# - Mean for control and treatment
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# - Standard deviation
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# - T-test for significance
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# - Effect size
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# - Confidence interval
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# Determine decision
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fi
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```
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**Statistical Analysis Process**:
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```python
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# Pseudo-code for analysis
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def analyze_experiment(experiment):
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control = experiment['results']['control_group']
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treatment = experiment['results']['treatment_group']
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# Calculate means
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control_mean = mean(control)
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treatment_mean = mean(treatment)
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improvement_pct = ((control_mean - treatment_mean) / control_mean) * 100
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# T-test for significance
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t_stat, p_value = ttest_ind(control, treatment)
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significant = p_value < 0.05
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# Effect size (Cohen's d)
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pooled_std = sqrt(((len(control)-1)*std(control)**2 + (len(treatment)-1)*std(treatment)**2) / (len(control)+len(treatment)-2))
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cohens_d = (treatment_mean - control_mean) / pooled_std
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# Confidence interval
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ci_95 = t.interval(0.95, len(control)+len(treatment)-2,
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loc=treatment_mean-control_mean,
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scale=pooled_std*sqrt(1/len(control)+1/len(treatment)))
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return {
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'control_mean': control_mean,
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'treatment_mean': treatment_mean,
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'improvement_pct': improvement_pct,
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'p_value': p_value,
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'significant': significant,
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'effect_size': cohens_d,
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'confidence_interval': ci_95,
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'sample_size': len(control) + len(treatment)
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}
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```
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**Analysis Report**:
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```markdown
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## STATISTICAL ANALYSIS - exp-2025-10-20-001
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### Data Summary
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**Control Group** (n=7):
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- Mean: 45.2 min
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- Std Dev: 8.3 min
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- Range: 32-58 min
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**Treatment Group** (n=7):
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- Mean: 27.4 min
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- Std Dev: 5.1 min
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- Range: 21-35 min
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### Statistical Tests
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**Improvement**: 39.4% faster (17.8 min saved)
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**T-Test**:
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- t-statistic: 4.82
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- p-value: 0.0012 (highly significant, p < 0.01)
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- Degrees of freedom: 12
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**Effect Size** (Cohen's d): 2.51 (very large effect)
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**95% Confidence Interval**: [10.2 min, 25.4 min] saved
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### Decision Criteria
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✅ Statistical significance: p < 0.05 (p = 0.0012)
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✅ Improvement > threshold: 39% > 15% target
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✅ No regression detected
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✅ Sample size adequate: 14 trials
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⚠️ Confidence threshold: 99% > 80% target (exceeded)
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### RECOMMENDATION: PROMOTE TO PRODUCTION
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**Rationale**:
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- Highly significant improvement (p < 0.01)
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- Large effect size (d = 2.51)
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- Exceeds improvement target (39% vs 15%)
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- No adverse effects detected
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- Sufficient sample size
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**Expected Impact**:
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- Time savings: 17.8 min per PR
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- Monthly savings: 17.8 × 50 PRs = 14.8 hours
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- Annual savings: 178 hours (4.5 work-weeks)
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```
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#### PROMOTE - Deploy Successful Experiment
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```bash
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if [ "$COMMAND" = "promote" ]; then
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echo "Promoting experiment to production: $EXPERIMENT_ID"
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echo ""
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# Verify experiment is successful
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# Check statistical significance
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# Backup current production
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# Replace with experimental variant
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# Update experiment status
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# Commit changes
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echo "⚠️ This will deploy experimental changes to production"
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echo "Press Ctrl+C to cancel, or wait 5 seconds to proceed..."
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sleep 5
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# Promotion process
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echo "Backing up current production..."
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# cp production.md production.md.pre-experiment
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echo "Deploying experimental variant..."
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# cp variant.md production.md
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echo "Updating experiment status..."
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# Update experiments.json: status = "promoted"
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git add .
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git commit -m "experiment: Promote exp-$EXPERIMENT_ID to production
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Experiment: [name]
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Improvement: [X]% ([metric])
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Confidence: [Y]% (p = [p-value])
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Trials: [N]
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Auto-promoted by /meta_experiment"
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echo "✅ Experiment promoted to production"
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fi
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```
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#### ROLLBACK - Revert Failed Experiment
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```bash
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if [ "$COMMAND" = "rollback" ]; then
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echo "Rolling back experiment: $EXPERIMENT_ID"
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echo ""
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# Restore backup
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# Update experiment status
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# Commit rollback
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echo "Restoring original version..."
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# cp backup.md production.md
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echo "Updating experiment status..."
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# Update experiments.json: status = "rolled_back"
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git add .
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git commit -m "experiment: Rollback exp-$EXPERIMENT_ID
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Reason: [failure reason]
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Regression: [X]% worse
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Status: Rolled back to pre-experiment state
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Auto-rolled back by /meta_experiment"
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echo "✅ Experiment rolled back"
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fi
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```
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### Phase 3: Automatic Decision Making (--auto mode)
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```bash
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if [ "$AUTO_MODE" = true ]; then
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echo ""
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echo "Running automatic experiment management..."
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echo ""
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# For each active experiment:
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for exp in active_experiments; do
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# Analyze current results
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analyze_experiment($exp)
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# Decision logic:
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if sample_size >= required && statistical_significance:
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if improvement > threshold && no_regression:
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# Auto-promote
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echo "✅ Auto-promoting: $exp (significant improvement)"
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promote_experiment($exp)
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elif regression > max_allowed:
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# Auto-rollback
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echo "❌ Auto-rolling back: $exp (regression detected)"
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rollback_experiment($exp)
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else:
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echo "⏳ Inconclusive: $exp (continue collecting data)"
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elif days_running > max_duration:
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# Expire experiment
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echo "⏱️ Expiring: $exp (max duration reached)"
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rollback_experiment($exp)
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else:
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echo "📊 Monitoring: $exp (needs more data)"
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fi
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done
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fi
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```
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### Phase 4: Experiment Tracking and Metrics
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**Telemetry Integration**:
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When commands run, check if they're part of an active experiment:
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```bash
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# In command execution (e.g., /review_pr)
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check_active_experiments() {
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# Is this command under experiment?
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if experiment_active_for_command($COMMAND_NAME); then
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# Randomly assign to control or treatment
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if random() < 0.5:
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# Control group (use original)
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variant="control"
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else:
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# Treatment group (use experimental)
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variant="treatment"
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# Track metrics
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start_time=$(date +%s)
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execute_command(variant)
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end_time=$(date +%s)
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duration=$((end_time - start_time))
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# Record result
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record_experiment_result($EXP_ID, variant, duration, metrics)
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fi
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}
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```
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### Phase 5: Safety Checks and Alerts
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**Continuous Monitoring**:
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```bash
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monitor_experiments() {
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for exp in running_experiments; do
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latest_results = get_recent_trials($exp, n=3)
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# Check for anomalies
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if detect_anomaly(latest_results):
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alert("Anomaly detected in experiment $exp")
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# Specific checks:
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if error_rate > 2x_baseline:
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alert("Error rate spike - consider rollback")
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if user_satisfaction < 0.5:
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alert("User satisfaction dropped - review experiment")
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if performance_regression > max_allowed:
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alert("Performance regression - auto-rollback initiated")
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rollback_experiment($exp)
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done
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}
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```
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**Alert Triggers**:
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- Error rate >2x baseline
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- User satisfaction <50%
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- Performance regression >10%
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- Statistical anomaly detected
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- Experiment duration >14 days
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## Experiment Management Guidelines
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### When to Create Experiments
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**DO Experiment** for:
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- Medium-confidence improvements (60-84%)
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- Novel approaches without precedent
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- Significant workflow changes
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- Performance optimizations
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- Agent prompt variations
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**DON'T Experiment** for:
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- High-confidence improvements (≥85%) - use `/meta_implement`
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- Bug fixes
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- Documentation updates
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- Low-risk changes
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- Urgent issues
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### Experiment Design Best Practices
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**Good Hypothesis**:
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- Specific: "Parallel agents save 15min per PR"
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- Measurable: Clear primary metric
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- Achievable: Realistic improvement target
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- Relevant: Addresses real bottleneck
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- Time-bound: 10 trials in 14 days
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**Poor Hypothesis**:
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- Vague: "Make things faster"
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- Unmeasurable: No clear metric
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- Unrealistic: "100x improvement"
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- Irrelevant: Optimizes non-bottleneck
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- Open-ended: No completion criteria
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### Statistical Rigor
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**Sample Size**:
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- Minimum: 10 trials per group
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- Recommended: 20 trials for high confidence
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- Calculate: Use power analysis for effect size
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**Significance Level**:
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- p < 0.05 for promotion
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- p < 0.01 for high-risk changes
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- Effect size >0.5 (medium or large)
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**Avoiding False Positives**:
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- Don't peek at results early
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- Don't stop early if trending good
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- Complete full sample size
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- Use pre-registered stopping rules
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## Important Notes
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1. **Never A/B Test in Production**: Use experimental branches
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2. **Random Assignment**: Ensure proper randomization
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3. **Track Everything**: Comprehensive metrics collection
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4. **Statistical Discipline**: No p-hacking or cherry-picking
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5. **Safety First**: Auto-rollback on regression
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6. **Document Results**: Whether success or failure
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7. **Learn from Failures**: Failed experiments provide value
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## Example Usage Scenarios
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### Scenario 1: Create Experiment
|
||
```bash
|
||
/meta_experiment create \
|
||
--hypothesis "Parallel agents save 15min" \
|
||
--primary-metric time_to_complete \
|
||
--sample-size 10
|
||
```
|
||
|
||
### Scenario 2: Monitor All Experiments
|
||
```bash
|
||
/meta_experiment status
|
||
```
|
||
|
||
### Scenario 3: Analyze Specific Experiment
|
||
```bash
|
||
/meta_experiment analyze exp-2025-10-20-001
|
||
```
|
||
|
||
### Scenario 4: Auto-Manage Experiments
|
||
```bash
|
||
/meta_experiment --auto
|
||
# Analyzes all experiments
|
||
# Auto-promotes successful ones
|
||
# Auto-rollsback failures
|
||
# Expires old experiments
|
||
```
|
||
|
||
---
|
||
|
||
**Remember**: Experimentation is how the system safely tests improvements. Every experiment, successful or not, teaches the system what works. Statistical rigor prevents false positives. Auto-rollback prevents damage.
|