Multi-registry skill discovery and installation for AI coding agents — search 9 sources, score, paginate, and install agent skills with security labels

Practices, protocols, and skills for AI-driven software development. Skills and safety hooks for Claude Code, Codex, OpenCode, Cursor, Antigravity, and any agent supporting the Agent Skills standard.

An agentic skills framework & bundle-plugin engineering toolkit that works.

Official Monte Carlo toolkit for AI coding agents. Skills and plugins that bring data and agent observability — monitoring, triaging, troubleshooting, health checks — into Claude Code, Cursor, and more.

Give your AI coding agent a personality. Composable persona + style + skills for Claude Code, Codex, Gemini CLI & OpenClaw. Ships Tech Persona Card v1.0 spec.

A collection of agent plugins for improving productivity, automating workflows, and making AI coding agents work better together.

Active Directory pentest methodology for Claude Code: skills, agents and slash commands for internal AD red-team work (Kerberoasting, ADCS ESC1-17, DCSync, ACL abuse, NTLM relay, delegation), with per-technique OPSEC/telemetry notes. Drives netexec, impacket, certipy, bloodyAD, BloodHound CE.

A Claude Code plugin for Siemens TIA Portal engineering automation.

Offensive security toolkit for Claude Code covering red team, exploit dev, AD attacks, EDR bypass, mobile pentest

CVE hunting harness for Claude Code - 20 skills, 5-agent team, systematic vulnerability research with false positive elimination

AI plugin to enhance and accelerate Flutter & Dart development, built by Very Good Ventures

The community-driven Claude Code plugin marketplace for MSPs — 70+ plugins across PSA, RMM, security, documentation, and accounting, plus cross-vendor industry workflow packs (ops, security, finance, compliance, sales, DevOps, cloud infra) that compose whatever tools you have connected.

Find and repair substance defects in AI-assisted prose, code, docs, and agent output. Reports defects, never authorship. Structural tests over model judgement, because LLM judges agree with human slop labels at chance.