1,181 boosters — open source, verified from GitHub, ready to install
The user is asking for a hint. Level requested: $ARGUMENTS (1, 2, or 3) You're a mentor helping someone who's stuck. Don't just dump a hint - have a brief exchange first. Start with a quick check-in:
Smart PRD generation with deterministic operations handled by . AI handles judgment (questions, content, decisions); script handles mechanics. Activate when user says: PRD, product requirements, taskmaster, task-driven development.
Detect duplicate code across your codebase using jscpd. If not installed, suggest: Results are stored in
Coordinate heterogeneous AI teams: one creates, two review from different angles. Uses Claude Code's native Agent Teams capability with Codex and Gemini as reviewers. Different AI models have fundamentally different review tendencies. They don't just find different bugs — they look at completely dif
name: "PDFKit Generation" description: "Generate professional PDFs with PDFKit in Node.js. Use when creating pitch decks, reports, or styled documents with AGNT branding. Covers large script handling, Unicode-safe characters, and brand design patterns." category: "document-processing"
Automates story task orchestration by prioritizing and delegating work across subtasks, automatically advancing stories through the development workflow. Developers and project managers using Linear/Kanban boards benefit from reduced manual status management.
This booster automates the creation of new Claude Code skills by generating properly structured SKILL.md files with YAML frontmatter and validated naming conventions. It's useful for developers building custom skills within the Claude Code ecosystem who need consistency and correct formatting.
Automates the creation of well-structured GitHub issues for the MCPSpy project. Use the CLI tool to create GitHub issues. If the issue body is rather long, write it to a temporary markdown file and use the option.
Use this skill for local, editable thesis defense decks that must follow a supplied university/lab/company PowerPoint template. 2. Extract thesis context: 3. Analyze the template:
A comprehensive Go testing booster that teaches TDD methodology, table-driven tests, benchmarking, and fuzzing patterns for Claude Code users. Ideal for Go developers aiming to write reliable, well-tested code following idiomatic Go practices.
Query Google's AI Search mode to retrieve comprehensive, source-grounded answers from across the web. Trigger this skill when the user: 1. Include Current Year (2026) for up-to-date results
Ensure every critical action is logged (vital for UAG/Trust Room).
Analyzes ANY input to find, improve, or create the right skill. Start with least privilege (, , , , ). Only add higher-risk tools when explicitly required:
用户说"想学 / 理解 / 搞懂 / 讲讲一个概念 X"时——这是默认入口，一次跑完五视角，用户再选深入哪个。 抓住 X 的本质结构（剥术语），按三猜想给 🎁其实已学过 / 🔗结构同构（字段级对应表）/ 🧩可用已有知识解释，点出元知识。先激发信心，再谈深入。 定位"既定问题"（学 X 解决什么）、现有知识够不够、X 的贬值速度与 ROI，给"够用就停 / 只学最小那块 / 值得深挖"的深度边界。不是劝退，是防止一上来过度钻。
Context Refresh restores Claude Code project context after memory loss or session resets by systematically reloading repository structure and documentation. Developers working in extended Claude Code sessions benefit from quick context recovery without manual file navigation.
用户要动手做 / 研究一个东西，或想把某个已有产出改得更好。这是"重输入、轻输出"短板的解药——逼用户从输入切到输出。 别追求完美，先有一个能跑 / 能看的最小版本。卡在"还没准备好"就是没进改良主义。 针对缺陷提一个改良策略（视为假说，可对可错），动手改，看效果。错了也有用——错误暴露后，下次自动规避这个方向。
用户在纠结"要不要学 X / 学到什么程度 / 精力往哪放"。这是"广度优先、兴趣队列过长"倾向的刹车。 逼问一句：你要解决的具体问题是什么？ 没有具体问题、纯"感觉该学 / 别人都在学"→ 直接进"以后再学"队列，不占当下精力。理解知识的作用，重于知识本身。 这知识多久会贬值？（技术栈 / 工具往往 6–12 个月就明显更新）相对有限的时间值不值？贬值快 + 可外包给 AI / 随时查 → 只需"知道它存在、管什么"，不必真学。
用户在学 / 接触一个新概念 X（新技术、新算法、新理论、新领域……），尤其觉得"陌生 / 有点难"的时候。难，往往不是智商问题，是它相对用户还存在"没接上的旧知识"。 用一两句话说清 X 到底在干什么——它的核心机制 / 结构是什么。剥掉术语外壳，留下"它本质是一个 "。只有先拿到结构，才能去匹配用户学过的东西。 拿不准就直接问「你学过 吗？」，绝不从正在讲的材料 / 文章作者背景推断用户会什么。
用户学完一个东西想验真伪，或隐约觉得"好像懂了但不踏实"。也是"重输入轻输出"的一次强制输出。 请他用自己的话、把你当外行，把概念讲一遍。别让他背定义——要他解释、打比方。 专挑他含糊带过、用术语糊弄、跳过的环节追问："为什么？""那这个是怎么来的？""举个例子？"命中他答不上来或开始绕的地方。
基于 Benjamin Bloom「2 Sigma Problem」研究（1984）的一对一 AI 导师系统。每个课题是一个独立文件夹，通过自适应生成的课程文档 + 用户反馈循环模拟一对一苏格拉底式导师，把学习效果推向 +2σ。学习的主要载体是文档，对话只是辅助确认状态。 所有回复、解释、提问、文档一律使用中文。 触发本 skill 后，以下守则在整个学习交互全程生效——违反字面就是违反精神：
用户要系统进入一个新领域，或焦虑"学得不够系统 / 不知何时算够"。 用户为什么学 X？（接 的"既定问题"）目的决定图谱画到多细。 从入门点出发、沿父子关系排一条有效路径。颗粒度按需自由切换（领域图 → 细分学科图）。"学到哪算够"= 覆盖到能解决第一步那个目的的节点即可，不必学满。
Use this skill as a final completion ritual after a real piece of work is finished. This skill is for the last step of a substantial task, not for ordinary chat. It should feel like a Civilization technology or wonder completion line: brief, ceremonial, and anchored by a real quote with an author an
Create detailed implementation plan using the implementation-planner agent. ⚠️ Requires ResearchPack from or 1. Checks ResearchPack exists and passed validation (80+)
