v0.16.0 · MIT
Superpowers & Beads task memory for AI coding agents - supports Claude Code, Codex, OpenCode, Cursor, Gemini CLI, GitHub Copilot CLI, Kimi Code, Antigravity, Factory Droid, and Pi.
— · 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
v1.25.0 · no license
Official SonarQube MCP Server for code quality and security in AI agents
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.
v0.1.0 · 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.
v1.5.0 · MIT
AI code reviews grounded in 12 classic engineering books — decay risk diagnostics with book citations, severity labels, and 6 analysis modes including full-sweep auto-fix
v4.3.0 · MIT
The harness layer for Claude Code — a reference implementation of harness engineering with hook-enforced dual review, state-machine gates that survive context compaction, and fail-closed safety where it counts. Quality gates that AI can't skip.
v0.49.0 · MIT OR Apache-2.0
Linter for AI agent configurations. Validates SKILL.md, CLAUDE.md, hooks, MCP, and more.
v0.7.1 · MIT
Claude Code plugin: universal radial-tree exploration engine. One tree skill + swappable presets (brainstorm / attack / design / code-audit) for divergent ideation, adversarial critique, and design-space exploration. 12 framings × hard-ban-on-incomplete-leaves × stable convergence.
v3.6.1 · MIT
Primes your project for peak Claude Code performance
v1.5.0 · MIT
AI code reviews grounded in 12 classic engineering books — decay risk diagnostics with book citations, severity labels, and 6 analysis modes including full-sweep auto-fix
v2.12.1 · MIT
dsh plugin that turns the iterate skill into an autonomous closed-loop harness: plan -> parallel review xN -> atomic fixes -> validate -> loop -> auto-stop, plus a dry-run pure-review mode with multi-round convergence and a meta-review that audits the rep
v0.4.0 · MIT
dsh插件 - 立刻审查agent对文件的修改,查看diff。a dsh plugin - review files that an agent just changed,you can see the diff
v0.8.1 · MIT
Comprehensive multi-agent code review for Claude Code
— · 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.
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.
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.
— · 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 · MIT
SE-CoVe: Software Engineering Chain-of-Verification plugin for Claude Code
v0.2.2 · MIT
Multi-model AI code review server using OpenRouter - get diverse perspectives from multiple LLMs in parallel