claude-flow-tbowman01
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Enterprise-grade AI agent orchestration with ruv-swarm integration (Alpha Release)
698 lines • 24.4 kB
JavaScript
/**
* Analyst Agent - Specialized in data analysis and performance optimization
*/
import { BaseAgent } from './base-agent.js';
export class AnalystAgent extends BaseAgent {
constructor(id, config, environment, logger, eventBus, memory) {
super(id, 'analyst', config, environment, logger, eventBus, memory);
}
getDefaultCapabilities() {
return {
codeGeneration: false,
codeReview: true,
testing: false,
documentation: true,
research: false,
analysis: true,
webSearch: false,
apiIntegration: true,
fileSystem: true,
terminalAccess: false,
languages: ['python', 'r', 'sql', 'typescript', 'javascript', 'julia', 'scala', 'matlab'],
frameworks: [
'pandas',
'numpy',
'matplotlib',
'seaborn',
'plotly',
'dask',
'spark',
'tensorflow',
'pytorch',
'scikit-learn',
'jupyter',
'tableau',
],
domains: [
'data-analysis',
'statistical-analysis',
'performance-analysis',
'business-intelligence',
'data-visualization',
'predictive-modeling',
'machine-learning',
'data-mining',
'financial-analysis',
'market-research',
'operations-research',
'quality-assurance',
],
tools: [
'data-processor',
'statistical-analyzer',
'chart-generator',
'report-builder',
'dashboard-creator',
'ml-pipeline',
'data-validator',
'performance-profiler',
'anomaly-detector',
'trend-analyzer',
],
maxConcurrentTasks: 4,
maxMemoryUsage: 2048 * 1024 * 1024, // 2GB
maxExecutionTime: 1200000, // 20 minutes
reliability: 0.9,
speed: 0.8,
quality: 0.95,
};
}
getDefaultConfig() {
return {
autonomyLevel: 0.75,
learningEnabled: true,
adaptationEnabled: true,
maxTasksPerHour: 15,
maxConcurrentTasks: 4,
timeoutThreshold: 1200000,
reportingInterval: 45000,
heartbeatInterval: 12000,
permissions: ['file-read', 'file-write', 'data-access', 'database-read', 'api-access'],
trustedAgents: [],
expertise: {
'data-analysis': 0.95,
'statistical-analysis': 0.92,
visualization: 0.88,
'performance-analysis': 0.9,
'predictive-modeling': 0.85,
'business-intelligence': 0.83,
},
preferences: {
outputFormat: 'detailed',
includeCharts: true,
statisticalTests: 'comprehensive',
confidenceLevel: 0.95,
visualStyle: 'professional',
},
};
}
async executeTask(task) {
this.logger.info('Analyst executing task', {
agentId: this.id,
taskType: task.type,
taskId: task.id,
});
try {
switch (task.type) {
case 'data-analysis':
return await this.analyzeData(task);
case 'performance-analysis':
return await this.analyzePerformance(task);
case 'statistical-analysis':
return await this.performStatisticalAnalysis(task);
case 'visualization':
return await this.createVisualization(task);
case 'predictive-modeling':
return await this.buildPredictiveModel(task);
case 'anomaly-detection':
return await this.detectAnomalies(task);
case 'trend-analysis':
return await this.analyzeTrends(task);
case 'business-intelligence':
return await this.generateBusinessIntelligence(task);
case 'quality-analysis':
return await this.analyzeQuality(task);
default:
return await this.performGeneralAnalysis(task);
}
}
catch (error) {
this.logger.error('Analysis task failed', {
agentId: this.id,
taskId: task.id,
error: error instanceof Error ? error.message : String(error),
});
throw error;
}
}
async analyzeData(task) {
const dataset = task.context?.dataset;
const analysisType = task.context?.type || 'exploratory';
const metrics = task.context?.metrics || ['central_tendency', 'distribution', 'correlation'];
const outputFormat = task.context?.format || 'report';
this.logger.info('Analyzing data', {
analysisType,
metrics,
outputFormat,
});
const analysis = {
dataset: {
name: dataset?.name || 'Unknown',
size: dataset?.size || 0,
columns: dataset?.columns || [],
types: dataset?.types || {},
},
analysisType,
summary: {
rowCount: 0,
columnCount: 0,
missingValues: 0,
duplicateRows: 0,
outliers: 0,
},
descriptiveStats: {},
correlations: {},
distributions: {},
insights: [],
recommendations: [],
visualizations: [],
confidence: 0,
methodology: 'statistical-analysis',
timestamp: new Date(),
};
// Store analysis progress
await this.memory.store(`analysis:${task.id}:progress`, {
status: 'analyzing',
startTime: new Date(),
analysisType,
}, {
type: 'analysis-progress',
tags: ['analysis', this.id, analysisType],
partition: 'tasks',
});
// Simulate data analysis
await this.delay(3000);
analysis.summary = {
rowCount: 10000,
columnCount: 15,
missingValues: 125,
duplicateRows: 23,
outliers: 47,
};
analysis.insights = [
'Strong positive correlation between variables A and B (r=0.85)',
'Variable C shows seasonal patterns with 3-month cycles',
'Data quality is high with only 1.25% missing values',
'Outliers concentrated in Q4 periods, likely due to seasonal effects',
];
analysis.recommendations = [
'Consider log transformation for skewed variables',
'Implement imputation strategy for missing values',
'Investigate Q4 outliers for business context',
'Add more recent data to improve model accuracy',
];
analysis.confidence = 0.88;
// Store final results
await this.memory.store(`analysis:${task.id}:results`, analysis, {
type: 'analysis-results',
tags: ['analysis', 'completed', this.id, analysisType],
partition: 'tasks',
});
return analysis;
}
async analyzePerformance(task) {
const system = task.context?.system;
const metrics = task.context?.metrics || ['response_time', 'throughput', 'error_rate'];
const timeframe = task.context?.timeframe || '24h';
const baseline = task.context?.baseline;
this.logger.info('Analyzing performance', {
system,
metrics,
timeframe,
});
const performance = {
system,
timeframe,
metrics: {},
benchmarks: {},
bottlenecks: [],
trends: [],
recommendations: [],
alertsTriggered: [],
slaCompliance: {
availability: 0,
responseTime: 0,
throughput: 0,
},
comparison: {
baseline: baseline || 'previous_week',
improvements: [],
regressions: [],
},
timestamp: new Date(),
anomalies: [],
insights: [],
visualizations: [],
optimizationPotential: 0,
projectedImprovement: 0,
confidence: 0,
};
// Simulate performance analysis
await this.delay(2500);
performance.metrics = {
averageResponseTime: 245, // ms
p95ResponseTime: 520,
p99ResponseTime: 1200,
throughput: 1250, // requests/min
errorRate: 0.03, // 3%
availability: 99.85, // %
};
performance.bottlenecks = [
{
component: 'Database queries',
impact: 'high',
description: 'N+1 query pattern causing 40% performance degradation',
recommendation: 'Implement query optimization and caching',
},
{
component: 'Memory allocation',
impact: 'medium',
description: 'Large object creation in hot path',
recommendation: 'Use object pooling or lazy initialization',
},
];
performance.slaCompliance = {
availability: 99.85,
responseTime: 92.3,
throughput: 103.5,
};
return performance;
}
async performStatisticalAnalysis(task) {
const data = task.context?.data;
const tests = task.context?.tests || ['normality', 'correlation', 'significance'];
const alpha = task.context?.alpha || 0.05;
const hypothesis = task.context?.hypothesis;
this.logger.info('Performing statistical analysis', {
tests,
alpha,
hypothesis,
});
const statistics = {
tests: {},
hypothesis: hypothesis || 'no_hypothesis',
alpha,
results: {},
interpretation: {},
assumptions: {
normality: false,
independence: false,
homogeneity: false,
},
powerAnalysis: {
power: 0,
sampleSize: 0,
effectSize: 0,
},
conclusions: [],
limitations: [],
timestamp: new Date(),
};
// Simulate statistical analysis
await this.delay(2000);
statistics.results = {
normalityTest: {
statistic: 0.923,
pValue: 0.041,
significant: true,
interpretation: 'Data deviates significantly from normal distribution',
},
correlationTest: {
coefficient: 0.756,
pValue: 0.002,
significant: true,
interpretation: 'Strong positive correlation detected',
},
};
statistics.conclusions.push('Null hypothesis rejected at α = 0.05 level', "Effect size is large (Cohen's d = 0.8)", 'Results are statistically and practically significant');
return statistics;
}
async createVisualization(task) {
const data = task.context?.data;
const chartType = task.context?.type || 'auto';
const style = task.context?.style || 'professional';
const interactive = task.context?.interactive || false;
this.logger.info('Creating visualization', {
chartType,
style,
interactive,
});
const visualization = {
chartType,
style,
interactive,
charts: [],
dashboard: null,
insights: [],
recommendations: [],
exportFormats: ['png', 'svg', 'pdf', 'html'],
accessibility: {
colorBlind: true,
screenReader: true,
highContrast: false,
},
timestamp: new Date(),
};
// Simulate visualization creation
await this.delay(1500);
visualization.charts.push({
type: 'line',
title: 'Trend Analysis Over Time',
description: 'Shows temporal patterns in the data',
dataPoints: 100,
interactive: true,
}, {
type: 'scatter',
title: 'Correlation Matrix',
description: 'Displays relationships between variables',
dataPoints: 500,
interactive: false,
});
visualization.insights.push('Clear upward trend visible in Q3-Q4', 'Seasonal patterns repeat every 3 months', 'Strong correlation between variables X and Y');
return visualization;
}
async buildPredictiveModel(task) {
const data = task.context?.data;
const target = task.context?.target;
const algorithm = task.context?.algorithm || 'auto';
const validation = task.context?.validation || 'k-fold';
this.logger.info('Building predictive model', {
target,
algorithm,
validation,
});
const model = {
algorithm: algorithm === 'auto' ? 'random_forest' : algorithm,
features: [],
target: target || 'default_target',
performance: {
accuracy: 0,
precision: 0,
recall: 0,
f1Score: 0,
rmse: 0,
mse: 0,
},
validation: {
method: validation,
splits: 5,
crossValidation: {
folds: 5,
avgScore: 0,
stdDev: 0,
},
},
predictions: [],
featureImportance: {},
insights: [],
recommendations: [],
modelMetadata: {
parameters: {},
training: {
epochs: 100,
convergence: true,
finalLoss: 0.15,
},
},
confidence: 0,
timestamp: new Date(),
};
// Simulate model building
await this.delay(4000);
model.performance = {
accuracy: 0.87,
precision: 0.85,
recall: 0.89,
f1Score: 0.87,
auc: 0.92,
rmse: 2.34,
};
model.featureImportance = {
feature_1: 0.35,
feature_2: 0.28,
feature_3: 0.22,
feature_4: 0.15,
};
return model;
}
async detectAnomalies(task) {
const data = task.context?.data;
const method = task.context?.method || 'isolation_forest';
const sensitivity = task.context?.sensitivity || 0.1;
const threshold = task.context?.threshold;
this.logger.info('Detecting anomalies', {
method,
sensitivity,
});
const anomalies = {
method,
sensitivity,
threshold: threshold || 'auto',
detected: [],
summary: {
total: 0,
severity: {
low: 0,
medium: 0,
high: 0,
critical: 0,
},
},
patterns: [],
recommendations: [],
falsePositiveRate: 0,
confidence: 0,
timestamp: new Date(),
};
// Simulate anomaly detection
await this.delay(2000);
anomalies.detected.push({
id: 'anom_001',
timestamp: new Date('2024-01-15'),
severity: 'high',
score: 0.95,
description: 'Unusual spike in traffic during off-peak hours',
features: ['traffic_volume', 'time_of_day'],
}, {
id: 'anom_002',
timestamp: new Date('2024-01-16'),
severity: 'medium',
score: 0.72,
description: 'Abnormal response time pattern',
features: ['response_time', 'request_size'],
});
anomalies.summary = {
total: 15,
severity: {
low: 8,
medium: 4,
high: 2,
critical: 1,
},
};
anomalies.confidence = 0.83;
return anomalies;
}
async analyzeTrends(task) {
const data = task.context?.data;
const timeframe = task.context?.timeframe || '3-months';
const granularity = task.context?.granularity || 'daily';
const forecast = task.context?.forecast || false;
this.logger.info('Analyzing trends', {
timeframe,
granularity,
forecast,
});
const trends = {
timeframe,
metrics: [],
trends: [],
correlations: {},
patterns: {
seasonal: false,
cyclical: false,
trending: false,
},
forecasts: forecast ? [] : [],
insights: [],
recommendations: [],
confidence: 0,
timestamp: new Date(),
};
// Simulate trend analysis
await this.delay(2500);
trends.trends.push({
metric: 'user_engagement',
direction: 'increasing',
slope: 0.15,
significance: 0.92,
period: 'Q4-2023',
}, {
metric: 'conversion_rate',
direction: 'stable',
slope: 0.02,
significance: 0.23,
period: 'Q4-2023',
});
trends.patterns = {
seasonal: true,
cyclical: false,
trending: true,
};
trends.confidence = 0.89;
return trends;
}
async generateBusinessIntelligence(task) {
const domain = task.context?.domain || 'general';
const metrics = task.context?.metrics || ['revenue', 'growth', 'efficiency'];
const timeframe = task.context?.timeframe || 'quarterly';
const audience = task.context?.audience || 'executive';
this.logger.info('Generating business intelligence', {
domain,
metrics,
timeframe,
audience,
});
const intelligence = {
scope: domain,
timeframe,
kpis: {},
trends: [],
insights: [],
recommendations: [],
actionItems: [],
riskFactors: [],
opportunities: [],
marketAnalysis: {
competitors: [],
positioning: '',
threats: [],
opportunities: [],
},
financialProjections: {
revenue: [],
costs: [],
profitability: [],
},
confidence: 0,
timestamp: new Date(),
};
// Simulate business intelligence generation
await this.delay(3500);
intelligence.kpis = {
revenue_growth: 12.5,
customer_acquisition_cost: 45.3,
lifetime_value: 1250.0,
churn_rate: 5.2,
market_share: 15.7,
};
intelligence.insights = [
'Customer acquisition costs decreased by 18% due to improved targeting',
'Premium tier adoption increased 35% following feature updates',
'Seasonal patterns show consistent Q4 revenue spikes',
];
intelligence.recommendations = [
'Increase marketing budget allocation to high-performing channels',
'Develop retention strategies for at-risk customer segments',
'Accelerate premium feature development to capture market demand',
];
intelligence.confidence = 0.91;
return intelligence;
}
async analyzeQuality(task) {
const subject = task.context?.subject;
const criteria = task.context?.criteria || ['accuracy', 'completeness', 'consistency'];
const standards = task.context?.standards || 'industry';
const benchmark = task.context?.benchmark;
this.logger.info('Analyzing quality', {
subject,
criteria,
standards,
});
const quality = {
codeQuality: {
complexity: 0,
maintainability: 0,
testCoverage: 0,
technicalDebt: 0,
},
issues: [],
patterns: [],
recommendations: [],
visualizations: [],
overallScore: 0,
confidence: 0,
timestamp: new Date(),
};
// Simulate quality analysis
await this.delay(2000);
quality.codeQuality = {
complexity: 3.2,
maintainability: 0.87,
testCoverage: 0.91,
technicalDebt: 0.23,
};
quality.overallScore = 0.91;
quality.issues.push({
category: 'completeness',
severity: 'medium',
description: 'Missing values in 13% of records',
impact: 'Affects downstream analysis accuracy',
});
quality.patterns.push('High complexity in authentication module');
quality.recommendations.push('Implement automated testing coverage');
quality.confidence = 0.89;
return quality;
}
async performGeneralAnalysis(task) {
this.logger.info('Performing general analysis', {
description: task.description,
});
// Default to data analysis
return await this.analyzeData(task);
}
async delay(ms) {
return new Promise((resolve) => setTimeout(resolve, ms));
}
getAgentStatus() {
return {
...super.getAgentStatus(),
specialization: 'Data Analysis & Performance Optimization',
analyticsCapabilities: [
'Statistical Analysis',
'Data Visualization',
'Performance Analysis',
'Predictive Modeling',
'Anomaly Detection',
'Business Intelligence',
],
supportedFormats: ['CSV', 'JSON', 'Parquet', 'SQL', 'Excel'],
statisticalMethods: ['Descriptive', 'Inferential', 'Multivariate', 'Time Series'],
currentAnalyses: this.getCurrentTasks().length,
averageAnalysisTime: '10-20 minutes',
lastAnalysisCompleted: this.getLastTaskCompletedTime(),
preferredTools: ['Python', 'R', 'SQL', 'Jupyter'],
};
}
}
export const createAnalystAgent = (id, config, environment, logger, eventBus, memory) => {
const tempAgent = new AnalystAgent(id, {}, {}, logger, eventBus, memory);
const defaultConfig = tempAgent.getDefaultConfig();
const defaultEnv = {
runtime: 'deno',
version: '1.40.0',
workingDirectory: './agents/analyst',
tempDirectory: './tmp/analyst',
logDirectory: './logs/analyst',
apiEndpoints: {},
credentials: {},
availableTools: ['data-processor', 'statistical-analyzer', 'chart-generator', 'report-builder'],
toolConfigs: {
dataProcessor: { chunkSize: 10000, parallel: true },
chartGenerator: { style: 'professional', dpi: 300 },
reportBuilder: { format: 'pdf', includeCharts: true },
},
};
return new AnalystAgent(id, { ...defaultConfig, ...config }, { ...defaultEnv, ...environment }, logger, eventBus, memory);
};
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