— · no license
A set of Claude Code and GitHub Copilot plugins providing the AI Literacy framework's complete development workflow — harness engineering, agent orchestration, literate programming, CUPID code review, and the three enforcement loops
— · MIT
Claude Code plugin for building LLM-maintained Obsidian wikis from raw research — compile, query, lint, and evolve your personal knowledge base. Inspired by Karpathy's knowledge base workflow.
v3.0.0 · MIT
Plan iron, verify real. Claude Code plugin: ironclad planning with independent verification chain. Turns any input into bulletproof plans, executes with TDD, verifies with agents that never saw the executor's work.
v1.0.6 · MIT
A virtual pet companion for your AI — Designed to provide in-context code review feedback with personality. Grow with your buddy and level up together. Works with Claude Code, Codex, CursorCLI, Github CopilotCLI, OpenClaw, and any MCP compatible clients
v1.5.1 · MIT
A collection of skills for Rails development and consulting with an emphasis on learning, communication, and client success.
v0.6.10 · MIT
Logic-first AI code review via semi-formal execution tracing (Premises → Trace → Divergence → Trigger → Remedy). Catches behavioral bugs, type-contract breaches & async hazards that linters miss. Six skills · Claude Code · Codex CLI · Gemini CLI.
— · MIT
Full-cycle delivery pipeline for coding agents: a mandatory built-in intake grill, then 10 gated stages (docs, brainstorm+decompose, spec, plan, build, tests, lint/deploy, post-deploy, docs/wiki, acceptance). Every stage's doctrine ships inside the skill
v3.0.0 · MIT
Don't buy software. Get the work done. GreatCTO ships AI autopilots that run a whole business function — medical coding, legal docs, procurement, accounting, IT, tax — from intake to outcome. A qualified human signs only the judgment calls. Live connectors, built-in compliance.
v0.24.1 · MIT
Lint your AI agent context files, MCP server configs, and session data against your actual codebase
v3.1.0 · MIT
Reverse engineer anything with agents, from app behavior down to native binaries.
— · Apache-2.0
Ghidra MCP Server — 200+ MCP tools for AI-powered reverse engineering. GUI plugin + headless server, lazy tool loading, convention enforcement, batch operations, Ghidra Server integration, and Docker deployment.
v1.15.1 · MIT
Quality gate for AI/Codex-generated pull requests: blocks TODO leftovers, leaked secrets, sloppy commits and red CI before they reach main.
— · Apache-2.0
Use Gemini from Claude Code to review code or delegate tasks.
— · GPL-3.0
All-in-One malware analysis tool.
v0.9.0-beta.93 · MIT
Evidence-backed TS/JS repo structure lens for Claude Code
v3.0.1 · MIT
Deterministic, local-first memory and guardrails for AI coding agents with no LLM in the hot path.
v2.8.0 · MIT
frontend-craft is a universal frontend plugin that brings the same opinionated engineering standards to all 15 AI coding assistants.
v6.5.2 · MIT
One command. Full stack. Zero compromise. — All-in-one Claude Code skill with 33 modes, 6-layer security, 23 hooks, and 75% token savings. Works on Codex, Cursor, Manus, Windsurf.
v1.25.0 · no license
Official SonarQube MCP Server for code quality and security in AI agents
v0.12.1 · MIT
MCP server that runs CLI AI coding agents (Claude Code, Codex, opencode, Antigravity) as sub-agents from any MCP client, with background sessions and structured code review
v2.4.3 · MIT
Safety-first context & orchestration engine for AI coding agents. MCP server with mandatory research pipeline, knowledge graph, impact analysis, decision memory, and safety guard — works with any MCP client.
— · Apache-2.0
Local code intelligence MCP server and CLI for AI coding agents
v1.33.0 · MIT
Claude Code plugin for structured, AI-driven software development. Orchestrates complex workflows -- from issue assessment to staged implementation to review and merge -- giving developers the speed of full automation with the control of human-in-the-loop checkpoints.
v0.1.0 · MIT License
Enables AI agents to perform read-only static analysis of Node.js backend projects, detecting database query anti-patterns, async bottlenecks, connection pooling mistakes, and dependency hygiene issues while returning structured evidence-backed findings.