Which specialized agents does this skill orchestrate?

It orchestrates several specialized agents including ww-bio-auditor for biological plausibility, ww-race-hunter for concurrency issues, and ww-leak-hunter for memory leak detection.

How does it assist with memory pattern analysis?

It utilizes MCP extensions to query memory statistics, analyze episode distribution, and evaluate the entity graph for relationship density and temporal patterns.

What is the primary purpose of the ww-analyze skill?

The ww-analyze skill provides deep analysis workflows for World Weaver systems, focusing on code quality, memory health, system architecture, and performance profiling.

What kind of report does the skill generate?

It generates a standardized Markdown report that includes a high-level summary, key metrics (like test coverage), findings categorized by severity (P0-P2), and actionable recommendations.

World Weaver System Analyzer

Name: World Weaver System Analyzer
Author: astoreyai

byastoreyai

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Analytics & Monitoring

Performs deep-dive audits, performance profiling, and memory pattern analysis for World Weaver ecosystems.

About

The World Weaver System Analyzer is a specialized diagnostic skill designed to maintain the integrity and performance of complex AI memory frameworks. It orchestrates a suite of specialized agents—including bio-auditors and concurrency hunters—to validate learning theories, detect memory leaks, and audit system architecture. By combining file-system analysis with real-time memory profiling, it provides developers with actionable insights and comprehensive reports to optimize the health and efficiency of AI-assisted workflows.

Key Features

Automated architectural auditing and module coupling metrics
Orchestrated multi-agent bug hunting for concurrency and memory leaks
1 GitHub stars
High-precision performance profiling and bottleneck identification
Deep semantic memory analysis and entity relationship mapping
Standardized Markdown reporting with severity-categorized findings

Use Cases

Identifying performance bottlenecks in high-latency memory retrieval operations
Performing comprehensive architectural reviews of World Weaver implementations
Auditing complex AI memory systems for biological plausibility and cache coherence

About

Key Features

Automated architectural auditing and module coupling metrics
Orchestrated multi-agent bug hunting for concurrency and memory leaks
1 GitHub stars
High-precision performance profiling and bottleneck identification
Deep semantic memory analysis and entity relationship mapping
Standardized Markdown reporting with severity-categorized findings

Use Cases

Identifying performance bottlenecks in high-latency memory retrieval operations
Performing comprehensive architectural reviews of World Weaver implementations
Auditing complex AI memory systems for biological plausibility and cache coherence