Agent Skills Hub

constant-time-testing

Detects timing side channels in cryptographic code to prevent secret data leakage. Essential for auditing sensitive implementations.

context-degradation

Diagnose, isolate, and mitigate LLM context failures like lost-in-middle, poisoning, distraction, and context clash to improve agent reliability.

seo-audit

Perform comprehensive SEO audits covering technical foundations, crawlability, on-page optimization, and content E-E-A-T to improve search rankings and organic performance.

receiving-code-review

Rigorous, non-performative code review reception for AI agents, prioritizing technical verification and YAGNI over passive agreement.

subagent-driven-development

Execute implementation plans using isolated subagents for each task, featuring a rigorous two-stage review process for spec compliance and code quality.

code-review

Perform comprehensive code reviews with a focus on security vulnerabilities, performance optimization, maintainability, and code correctness.

code-quality-review

Holistic, multi-dimensional code review skill providing prioritized, actionable feedback on correctness, security, performance, design, and accessibility.

iam

Manage AWS Identity and Access Management: configure users, roles, policies, and permissions for secure cloud environments.

memory-systems

Guides agent memory system implementation, compares frameworks (Mem0, Zep, Letta, LangMem, Cognee), and designs persistence architectures for cross-session knowledge retention.

mcp-builder

A guide for building high-quality MCP (Model Context Protocol) servers in Python or TypeScript to integrate external APIs and services into LLM workflows.