Skill UI

AFL++ is an advanced fork of AFL that runs multi-core campaigns, supports diverse mutations, and targets large C/C++ codebases with stable tooling via Docker or host builds.

This comprehensive guide details the architecture of memory for intelligent agents. It covers the distinction between short-term context, long-term vector storage, and advanced cognitive models (CoALA framework: semantic, episodic, procedural). Learn how to implement robust memory retrieval mechanisms, select appropriate vector databases (Pinecone, Qdrant, etc.), and utilize embedding models to build truly context-aware, persistent AI applications.

Aggregates performance metrics from applications, systems, databases, caches, queues, and external services into a central platform, helping teams define a consistent taxonomy, choose aggregation tools, and create dashboards and alerts for faster monitoring and troubleshooting.

Guides a structured walkthrough for onboarding into unfamiliar codebases by covering quick overviews, stack detection, directory mapping, pattern spotting, workflow checks, and summary reporting so teams can answer "how does this work" with confidence.

This comprehensive skill guides the systematic optimization of end-to-end application performance across the entire stack. It covers establishing performance baselines through deep profiling (CPU, Memory, I/O), assessing observability gaps, optimizing databases, tuning backend services and APIs, and enhancing frontend rendering. Ideal for complex, scalable applications that require measurable performance improvements, reliability enhancements, and adherence to modern user experience standards.

This comprehensive skill guides the systematic optimization of end-to-end application performance across the entire stack. It covers establishing performance baselines through deep profiling (CPU, Memory, I/O), assessing observability gaps, optimizing databases, tuning backend services and APIs, and enhancing frontend rendering. Ideal for complex, scalable applications that require measurable performance improvements, reliability enhancements, and adherence to modern user experience standards.

Automates database archival workflows by identifying historical records in PostgreSQL or MySQL, applying retention policies, and migrating data to archive tables or cold storage (S3/Azure Blob/GCS) with compression and compliance tracking.

Automates database archival workflows by identifying historical records in PostgreSQL or MySQL, applying retention policies, and migrating data to archive tables or cold storage (S3/Azure Blob/GCS) with compression and compliance tracking.

Prepares codebases for Trail of Bits security reviews by setting goals, running static analysis, expanding test coverage, pruning dead code, documenting build scope/access, and generating flowcharts, user stories, glossaries, and other reviewer-ready artifacts.

Prepares codebases for Trail of Bits security reviews by setting goals, running static analysis, expanding test coverage, pruning dead code, documenting build scope/access, and generating flowcharts, user stories, glossaries, and other reviewer-ready artifacts.

This SDK provides the management plane capabilities for Azure Cosmos DB within the .NET ecosystem. It allows developers to programmatically provision and manage core infrastructure resources, such as Cosmos DB accounts, databases, containers, throughput settings, and perform resource lifecycle operations using Azure Resource Manager (ARM). It is designed for infrastructure automation and setup tasks.

This skill provides the Azure Resource Manager SDK for .NET, enabling developers to programmatically manage PostgreSQL Flexible Server deployments on Azure. It covers core cloud automation workflows such as creating the server, setting up databases, configuring firewall rules, and updating server parameters using C# code. Ideal for cloud infrastructure management and CI/CD pipelines.