@sethdouglasford/claude-flow
Version:
Claude Code Flow - Advanced AI-powered development workflows with SPARC methodology
337 lines (312 loc) • 14.9 kB
JavaScript
import { logger } from "../../core/logger.js";
/**
* Analysis Strategy - Enhanced with Meta-Learning DGM and Pattern Synthesis
* Based on the Analysis Swarm Pattern from swarm documentation
*
* Features:
* - Meta-learning for pattern discovery
* - Pattern synthesis across multiple sources
* - Advanced statistical analysis
* - Knowledge graph construction
* - Insight synthesis and validation
*/
export class AnalysisStrategy {
async decomposeObjective(objective) {
logger.info(`Decomposing analysis objective: ${objective.description}`);
const timestamp = Date.now();
const tasks = [];
const dependencies = new Map();
// Task 1: Data Collection and Meta-Learning Setup
const dataCollectionTaskId = `data-collection-${timestamp}`;
tasks.push({
id: {
id: dataCollectionTaskId,
swarmId: objective.id,
sequence: 1,
priority: 1,
},
name: "Data Collection with Meta-Learning Setup",
description: "Systematic data collection enhanced with meta-learning capabilities",
status: "created",
priority: "high",
type: "research",
requirements: {
capabilities: ["data_collection", "meta_learning", "pattern_recognition"],
tools: ["memory", "web_search", "file_operations", "data_processing"],
permissions: ["read", "write"],
},
constraints: {
dependencies: [],
dependents: [],
conflicts: [],
},
input: { objective: objective.description, phase: "data_collection" },
instructions: `
Execute advanced data collection with meta-learning:
1. **Meta-Learning DGM Setup** (/.claude/commands/synthesis/meta-learning-dgm.md):
- Initialize Deep Generative Models for pattern discovery
- Set up multi-scale analysis framework
- Configure adaptive learning parameters
- Establish baseline pattern libraries
2. **Comprehensive Data Collection**:
- Gather structured and unstructured data sources
- Apply batch Read operations for efficient data ingestion
- Implement quality validation and cleaning pipelines
- Create data lineage tracking
3. **Initial Pattern Discovery**:
- Apply unsupervised learning for initial pattern detection
- Use meta-learning to identify recurring structures
- Store preliminary patterns in Memory under "initial-patterns-${objective.id}"
4. **Data Preparation**:
- Normalize and standardize data formats
- Create feature engineering pipelines
- Establish data quality metrics
- Prepare for advanced analysis phases
**Memory Coordination**: Store collected data and initial patterns
**Success Criteria**: Comprehensive dataset with initial pattern discovery completed
`,
context: { strategy: "analysis", coordination: "data_collection" },
createdAt: new Date(),
updatedAt: new Date(),
attempts: [],
statusHistory: [],
});
// Task 2: Pattern Synthesis and Multi-Source Analysis
const patternSynthesisTaskId = `pattern-synthesis-${timestamp}`;
tasks.push({
id: {
id: patternSynthesisTaskId,
swarmId: objective.id,
sequence: 2,
priority: 1,
},
name: "Pattern Synthesis and Multi-Source Analysis",
description: "Advanced pattern synthesis using multiple analytical approaches",
status: "created",
priority: "high",
type: "analysis",
requirements: {
capabilities: ["pattern_synthesis", "statistical_analysis", "machine_learning"],
tools: ["memory", "analysis_tools", "ml_frameworks", "visualization"],
permissions: ["read", "write"],
},
constraints: {
dependencies: [],
dependents: [],
conflicts: [],
},
input: { objective: objective.description, phase: "pattern_synthesis" },
instructions: `
Execute multi-dimensional pattern synthesis:
1. **Pattern Synthesizer Protocol** (/.claude/commands/synthesis/pattern-synthesizer.md):
- Apply cross-domain pattern extraction
- Use meta-pattern recognition across multiple sources
- Implement adaptive pattern validation
- Create pattern hierarchy and relationships
2. **Statistical Analysis Framework**:
- Deploy multiple statistical approaches in parallel
- Use clustering algorithms for pattern grouping
- Apply time-series analysis for temporal patterns
- Implement correlation and causation analysis
3. **Meta-Learning Enhancement**:
- Use DGM for deep pattern discovery
- Apply transfer learning from similar domains
- Implement adaptive model selection
- Create ensemble pattern detection
4. **Cross-Validation and Synthesis**:
- Validate patterns across multiple methodologies
- Synthesize findings into coherent framework
- Identify meta-patterns and higher-order structures
- Store validated patterns in Memory
**Memory Coordination**: Read initial patterns, store synthesized results
**Success Criteria**: Validated pattern library with cross-methodology confirmation
`,
context: { strategy: "analysis", coordination: "pattern_synthesis" },
createdAt: new Date(),
updatedAt: new Date(),
attempts: [],
statusHistory: [],
});
// Task 3: Advanced Analytics and Insight Generation
const advancedAnalyticsTaskId = `advanced-analytics-${timestamp}`;
tasks.push({
id: {
id: advancedAnalyticsTaskId,
swarmId: objective.id,
sequence: 3,
priority: 2,
},
name: "Advanced Analytics and Insight Generation",
description: "Deep analytical processing with insight synthesis",
status: "created",
priority: "high",
type: "analysis",
requirements: {
capabilities: ["advanced_analytics", "insight_generation", "predictive_modeling"],
tools: ["memory", "ml_frameworks", "statistical_tools", "visualization"],
permissions: ["read", "write", "compute"],
},
constraints: {
dependencies: [],
dependents: [],
conflicts: [],
},
input: { objective: objective.description, phase: "analytics" },
instructions: `
Execute advanced analytical processing:
1. **Multi-Modal Analysis**:
- Apply supervised and unsupervised learning techniques
- Use ensemble methods for robust predictions
- Implement anomaly detection and outlier analysis
- Create predictive models with uncertainty quantification
2. **Insight Generation Framework**:
- Use pattern synthesis results for insight discovery
- Apply causal inference techniques
- Generate actionable recommendations
- Create confidence intervals and reliability measures
3. **Knowledge Graph Construction**:
- Build entity-relationship networks from patterns
- Create semantic mappings between concepts
- Implement graph-based reasoning
- Store knowledge structures in Memory
4. **Validation and Testing**:
- Cross-validate insights against held-out data
- Test predictions against known outcomes
- Implement robustness testing
- Create reliability metrics for insights
**Memory Coordination**: Use pattern synthesis results, store analytical insights
**Success Criteria**: Validated insights with confidence measures and actionable recommendations
`,
context: { strategy: "analysis", coordination: "advanced_analytics" },
createdAt: new Date(),
updatedAt: new Date(),
attempts: [],
statusHistory: [],
});
// Task 4: Visualization and Reporting
const visualizationTaskId = `visualization-${timestamp}`;
tasks.push({
id: {
id: visualizationTaskId,
swarmId: objective.id,
sequence: 4,
priority: 2,
},
name: "Visualization and Comprehensive Reporting",
description: "Create comprehensive visualizations and analytical reports",
status: "created",
priority: "normal",
type: "documentation",
requirements: {
capabilities: ["data_visualization", "report_generation", "storytelling"],
tools: ["memory", "visualization_tools", "reporting_frameworks"],
permissions: ["read", "write", "create"],
},
constraints: {
dependencies: [],
dependents: [],
conflicts: [],
},
input: { objective: objective.description, phase: "visualization" },
instructions: `
Create comprehensive visualizations and reports:
1. **Interactive Visualization Suite**:
- Create multi-dimensional data visualizations
- Build interactive dashboards for pattern exploration
- Implement drill-down capabilities for detailed analysis
- Use progressive disclosure for complex insights
2. **Narrative Report Generation**:
- Synthesize all analytical findings into coherent narrative
- Create executive summary with key insights
- Include methodology documentation and validation results
- Provide actionable recommendations with implementation guidance
3. **Knowledge Artifact Creation**:
- Generate reusable pattern libraries
- Create analytical templates for future use
- Build knowledge base entries for discovered insights
- Store artifacts for organizational learning
4. **Stakeholder Communication**:
- Create audience-specific report versions
- Design presentation materials for different technical levels
- Include uncertainty quantification and limitations
- Provide next steps and follow-up recommendations
**Memory Coordination**: Integrate all previous analysis results
**Success Criteria**: Comprehensive report suite with actionable insights and reusable artifacts
`,
context: { strategy: "analysis", coordination: "visualization_reporting" },
createdAt: new Date(),
updatedAt: new Date(),
attempts: [],
statusHistory: [],
});
// Task 5: Meta-Analysis and Knowledge Integration
const metaAnalysisTaskId = `meta-analysis-${timestamp}`;
tasks.push({
id: {
id: metaAnalysisTaskId,
swarmId: objective.id,
sequence: 5,
priority: 1,
},
name: "Meta-Analysis and Knowledge Integration",
description: "Integrate findings into organizational knowledge base with meta-learning updates",
status: "created",
priority: "high",
type: "integration",
requirements: {
capabilities: ["meta_analysis", "knowledge_integration", "organizational_learning"],
tools: ["memory", "knowledge_base", "meta_learning_frameworks"],
permissions: ["read", "write", "integrate"],
},
constraints: {
dependencies: [],
dependents: [],
conflicts: [],
},
input: { objective: objective.description, phase: "integration" },
instructions: `
Execute meta-analysis and knowledge integration:
1. **Meta-Analysis Framework**:
- Compare findings with historical analyses
- Identify recurring patterns across projects
- Update meta-learning models with new insights
- Create cross-project pattern libraries
2. **Knowledge Base Integration**:
- Update organizational knowledge graphs
- Create linkages to existing knowledge structures
- Implement version control for evolving insights
- Establish citation and provenance tracking
3. **Learning System Updates**:
- Update meta-learning DGM with new patterns
- Refine analytical methodologies based on outcomes
- Create feedback loops for continuous improvement
- Update pattern recognition algorithms
4. **Future Analysis Enhancement**:
- Create templates for similar future analyses
- Update analytical toolchains with lessons learned
- Establish benchmarks for future comparison
- Document methodology improvements
**Memory Coordination**: Integrate all analysis artifacts into persistent knowledge base
**Success Criteria**: Updated knowledge systems with enhanced capabilities for future analyses
`,
context: { strategy: "analysis", coordination: "meta_integration" },
createdAt: new Date(),
updatedAt: new Date(),
attempts: [],
statusHistory: [],
});
// Set up dependencies
dependencies.set(dataCollectionTaskId, []);
dependencies.set(patternSynthesisTaskId, [dataCollectionTaskId]);
dependencies.set(advancedAnalyticsTaskId, [patternSynthesisTaskId]);
dependencies.set(visualizationTaskId, [advancedAnalyticsTaskId]);
dependencies.set(metaAnalysisTaskId, [advancedAnalyticsTaskId, visualizationTaskId]);
logger.info(`Created ${tasks.length} analysis tasks with dependencies`);
return {
tasks,
dependencies,
estimatedDuration: 240, // Total estimated duration
};
}
}
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