astoreyai WW Analyze Skill
Deep analysis workflows for World Weaver memory systems, code quality, and architecture.
Purpose
This skill provides comprehensive analysis capabilities:
- Code Analysis: Audit WW codebase for bugs, patterns, and improvements
- Memory Analysis: Analyze memory contents, patterns, and health
- Architecture Analysis: Evaluate system design and propose improvements
- Performance Analysis: Profile and identify bottlenecks
When to Use
Invoke this skill when:
- User asks "analyze the memory system"
- User wants to understand memory patterns
- Code quality audit is needed
- Performance issues are suspected
- Architecture review is requested
Analysis Workflows
1. Bug Hunting Workflow
Orchestrate specialized bug-hunting agents:
# Run all bug hunters in sequence
paths=(
"src/ww/learning/"
"src/ww/memory/"
"src/ww/storage/"
"src/ww/mcp/"
"src/ww/core/"
)
for path in "${paths[@]}"; do
echo "Analyzing: $path"
done
Agent orchestration:
- ww-bio-auditor - Check biological plausibility
- ww-race-hunter - Find concurrency bugs
- ww-leak-hunter - Detect memory leaks
- ww-hinton-validator - Validate learning theory
- ww-cache-analyzer - Check cache coherence
- ww-trace-debugger - Debug eligibility traces
2. Memory Pattern Analysis
Analyze stored memories for patterns:
# Query memory statistics
mcp__ww-memory__memory_stats()
# Analyze episode distribution
mcp__ww-memory__recall_episodes(
query="*",
limit=1000,
include_metadata=True
)
# Analyze entity graph
mcp__ww-memory__semantic_recall(
query="*",
include_connections=True
)
Output analysis:
- Episode count by outcome (success/failure/partial)
- Entity type distribution
- Relationship density
- Temporal patterns
- Importance distribution
3. Architecture Analysis
Evaluate system architecture:
# File structure analysis
find /home/aaron/ww/src -name "*.py" | wc -l
# Dependency analysis
grep -r "^from ww" /home/aaron/ww/src --include="*.py" | cut -d: -f2 | sort | uniq -c | sort -rn
# Test coverage check
cd /home/aaron/ww && pytest --cov=src/ww --cov-report=term-missing
Architecture metrics:
- Module coupling (import analysis)
- Test coverage by module
- Cyclomatic complexity
- Code duplication
4. Performance Analysis
Profile system performance:
import cProfile
import pstats
# Profile memory operations
profiler = cProfile.Profile()
profiler.enable()
# ... memory operations ...
profiler.disable()
stats = pstats.Stats(profiler)
stats.sort_stats('cumulative')
stats.print_stats(20)
Performance metrics:
- Query latency (p50, p95, p99)
- Memory usage over time
- CPU utilization
- I/O operations
Analysis Report Format
## WW Analysis Report
**Type**: {Bug Hunt | Memory Pattern | Architecture | Performance}
**Date**: {timestamp}
**Scope**: {paths analyzed}
### Summary
{High-level findings}
### Metrics
| Metric | Value | Status |
|--------|-------|--------|
| Files analyzed | N | - |
| Issues found | N | {OK/WARNING/CRITICAL} |
| Test coverage | N% | {OK if >80%} |
### Findings
#### Critical (P0)
{List of critical issues}
#### High (P1)
{List of high priority issues}
#### Medium (P2)
{List of medium priority issues}
### Recommendations
1. {Priority action items}
### Visualizations
{Embedded diagrams or links to generated visualizations}
Integration with Agents
This skill orchestrates bug-hunting agents:
/ww-analyze bugs src/ww/learning/
→ Spawns: ww-bio-auditor, ww-hinton-validator, ww-trace-debugger
/ww-analyze concurrency src/ww/mcp/
→ Spawns: ww-race-hunter, ww-leak-hunter, ww-cache-analyzer
/ww-analyze full src/ww/
→ Spawns: All 6 agents in parallel
MCP Extensions
Proposed MCP endpoints for analysis:
mcp__ww-memory__analyze_patterns - Analyze memory patterns
mcp__ww-memory__analyze_health - Check system health
mcp__ww-memory__analyze_performance - Profile operations
mcp__ww-memory__generate_report - Create analysis report
Quality Checklist
Before completing analysis:
- All target paths scanned
- All agents completed successfully
- Findings categorized by severity
- Recommendations are actionable
- Report saved to /home/aaron/mem/
Error Handling
If analysis fails:
- Log partial results
- Identify failing component
- Continue with remaining analyses
- Report incomplete status