v1.5.1 · MIT
A collection of skills for Rails development and consulting with an emphasis on learning, communication, and client success.
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.
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.
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.
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.
v0.8.0 · no license
Agent skills distilled from the hard-won lessons of world-renowned programmers, in the spirit of "97 Things Every Programmer Should Know"
v1.2.0 · MIT
SE-CoVe: Software Engineering Chain-of-Verification plugin for Claude Code
— · Apache-2.0
Cross-platform toolkit to enhance Claude Code with multi-LLM consensus, 8 specialist agents, semantic knowledge search, and one-command install.
— · MIT
PhD Research Skills for Claude Code: paper reproduction, experiment design, paper review, result comparison and more.
v0.3.2 · MIT
Claude Code plugin that transforms vague requirements into detailed implementation plans via research, interviews, and multi-LLM review.
v0.3.1 · MIT
让 Agent 的工作方式可组合、可审查、可持续改进,最终实现 Agent Self Evoling。 DeepSeek Harness Web plugin with composable task controls and isolated, human-reviewed self-evolution.
— · MIT
ARIS ⚔️ (Auto-Research-In-Sleep) — Lightweight Markdown-only skills for autonomous ML research: cross-model review loops, idea discovery, and experiment automation. No framework, no lock-in — works with Claude Code, Codex, OpenClaw, or any LLM agent.
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
— · no license
ProMentor 是一个 AI Coding Agent Skill。装上它,你的 AI 编程助手立刻化身为导师——扫描项目架构、生成阶梯式 Chapter、带你手写核心逻辑、自动判题、AI Code Review。
v1.2.0 · Apache-2.0
A meta-skill that designs domain-specific agent teams, defines specialized agents, and generates the skills they use.
v0.9.2 · Apache-2.0
A local detector for AI-writing patterns. Scores every prose file your agent saves. Python standard library only, no network, no model.
v0.49.0 · MIT OR Apache-2.0
Linter for AI agent configurations. Validates SKILL.md, CLAUDE.md, hooks, MCP, and more.
v1.1.0 · MIT
USPTO patent creation system with MCP server + Claude Code plugin. Hybrid RAG search over MPEP/USC/CFR, BigQuery access to 76M+ patents, automated 35 USC 112 compliance checks, prior art search, diagram generation. GPU-accelerated with skills and autonomous agents.
v1.10.10 · MIT
Local-first agent plan annotator
v1.16.0 · MIT
Make any repo AI-first - write sustainable code from the start, or refactor a legacy codebase to prepare it for agent-driven development.Building blocks for Claude Code: subagents, slash commands, hooks, and workflow patterns. Copy what you need. A working developer's stack for Claude Code.
— · no license
Feed your agent papers and half-formed ideas — it links them into a system design you can defend. Markdown keeps the record; a visual canvas makes it readable. An Agent Skill for Claude Code & any SKILL.md-compatible agent.
v1.1.13 · MIT
The linter for your agent harness. Works with Claude Code, Codex, and Cursor.
v5.2.4 · MIT
Your AI can write the UI. This makes sure it writes your UI. A deterministic scanner scores your design system 0-100 against 34 public repos, writes rules for Claude, Cursor, Copilot and Windsurf with --apply, and runs as a local MCP server with --mcp.
v0.3.0 · MIT
Claude Code plugin for autonomous AI research — multi-agent loops take a bare topic all the way to running experiments, with no human-written experimental code.