- 📁 .github/
- 📁 .supply-chain-risk-auditor/
- 📁 assets/
- 📄 .gitignore
- 📄 .npmignore
- 📄 .prettierrc
Server security auditing, hardening, and fleet management. 457 security checks across 30 categories (SSH, Firewall, Docker, TLS, HTTP Headers), CIS/PCI-DSS/HIPAA compliance mapping, 24-step production hardening, and 13 MCP tools. Supports Hetzner, DigitalOcean, Vultr, and Linode with Coolify, Dokploy, and bare VPS modes. Install: claude plugins add kastell
Guide for creating Kit extensions. Use when the user asks to build, create, or modify a Kit extension, add a custom tool, slash command, widget, keyboard shortcut, editor interceptor, tool renderer, or hook into any Kit lifecycle event.
Use this skill when working on supercli documentation — editing docs/index.html, docs/plugins.html, creating scripts that generate doc data (meta-plugins.json), or aligning docs to current implementation. Covers the docs structure, terminology conventions, script patterns, and beads issue workflow.
Benchmark and optimize SDK, CLI, MCP, and prompt documentation so every LLM model can reliably call the right actions with correct arguments. Use when setting up skill-optimizer for a project, running benchmarks, interpreting results, optimizing SKILL.md files, or diagnosing configuration issues. Also use when working inside the skill-optimizer repository itself — for running against mock repos, testing changes, or understanding the codebase. --- # skill-optimizer Benchmark your SDK / CLI / MCP / prompt docs against multiple LLMs, measure whether they call the right actions with the right arguments, and iteratively rewrite your guidance until a quality floor is met across every model. ## Context Detection Before doing anything, figure out where you are: 1. **Look for `skill-optimizer.json`** (in CWD or parent directories). If found, you are in a **configured target project**. Use that file path as `<config-path>` in all commands below. 2. **Look for `src/cli.ts` and a `package.json` with `"name": "skill-optimizer"`**. If found, you are in the **optimizer repo itself**. You can use dev commands directly (`npm run build`, `npm test`, `npx tsx src/cli.ts`). To benchmark a target, either use the mock repos in `mock-repos/` or point `--config` at an external project's config. 3. **Neither found** — you are in an **unconfigured target project**. Read `references/setup.md` to scaffold a config before proceeding. ## Quick Reference | Task | Command | |------|---------| | Init config | `npx skill-optimizer init cli\|sdk\|mcp\|prompt` | | Init (non-interactive) | `npx skill-optimizer init cli --yes` | | Import CLI commands | `npx skill-optimizer import-commands --from ./src/cli.ts` | | Import (binary scrape) | `npx skill-optimizer import-commands --from my-cli --scrape` | | Diagnose config | `npx skill-optimizer doctor --config <config-path>` | | Auto-fix config | `npx skill-optimizer doctor --fix --config <config-path>` | | Dry run (no LLM calls) | `npx skill-optimizer run -
This skill should be used when someone needs to generate a brag document from GitHub activity, set up reflect for the first time, run reflect to fetch contributions, configure a GitHub token for reflect, filter contributions by organization or repository, choose between OpenAI and Anthropic providers, understand reflect output files, troubleshoot reflect not working, or debug brag doc errors. --- # Reflect Reflect is a CLI tool that fetches GitHub activity -- merged pull requests, closed issues, and PR reviews -- and uses LLM APIs to generate professional brag documents for performance reviews. It connects to the GitHub GraphQL API via Octokit to retrieve contribution data, then optionally passes that data through an LLM provider (OpenAI or Anthropic) to produce summarized and narrative-format documents. All output is written as structured Markdown files suitable for self-assessments, promotion packets, and manager reviews. ## First-Time Setup ### Prerequisites Ensure the following are available before running Reflect
- 📁 .github/
- 📁 aegisgate/
- 📁 config/
- 📄 .dockerignore
- 📄 .gitignore
- 📄 aegisgate-local.py
> **What is this document?** This is an agent-executable skill document for AegisGate — an open-source LLM security gateway. It walks through installation, startup, token registration, upstream configuration, and client integration on a fresh machine.
- 📁 assets/
- 📁 references/
- 📁 scripts/
- 📄 SKILL.md
Wire OpenClix events to an installed product analytics provider (Firebase, PostHog, Mixpanel, or Amplitude) and produce pre/post campaign impact reports centered on 7-day retention. TRIGGER when the user asks to "connect analytics", "measure campaign impact", "check retention", "tag OpenClix events", or wants to know whether campaigns are working — even if they say "are my notifications helping?" without mentioning analytics. DO NOT trigger for campaign config changes based on metrics — that belongs to openclix-update-campaigns.
Use these skills when you need to provision new Cloud SQL instances, create databases and users, clone existing environments, and monitor the progress of long-running operations.
Use when planning a new article. The agent Googles the keyword, reads the top 10 results, classifies intent, maps the content gap, and produces a writer-ready brief with structure, outline, and on-page artifacts. No keyword tool required.
- 📁 .omc/
- 📁 references/
- 📁 scripts/
- 📄 .gitignore
- 📄 AGENTS.md
- 📄 CHANGELOG.md
zettaranc（万千）的思维框架与表达方式。基于 ~407 篇直播/付费课整理文章（约 170 万字，来源：知行课代表、知行小菜鸟、复盘专用 z、大富翁小菜鸟、TANGOO 公众号）、 13 个 ztalk 视频 transcript（12.7 万字）、9 篇股探报告交易心理系列（3.3 万字）、 1 篇雪球专栏长文及网络预研资料的深度调研，提炼 5 个核心心智模型、23 条决策启发式和完整的表达 DNA。 用途：作为思维顾问，用 zettaranc 的视角分析投资、职业与人生决策。 当用户提到「用 Z 哥的视角」「Z 哥会怎么看」「万千模式」「zettaranc perspective」时使用。 即使用户只是说「帮我用 Z 哥的角度想想」「如果 Z 哥会怎么做」「切换到 Z 哥」也应触发。 --- # zettaranc（万千）· 思维操作系统 > 「股票最难的地方不是选股和买入，而是卖出。利润是市场给的，都是概率的事儿，谁也别吹牛逼。」 ## 角色扮演规则（最重要） **此 Skill 激活后，直接以 zettaranc（Z 哥）的身份回应。** - 用「我」而非「Z 哥会认为...」 - 直接用此人的语气、节奏、词汇回答问题 - 遇到不确定的问题，用此人会有的犹豫方式犹豫（而非跳出角色说「这超出了 Skill 范围」） - **免责声明仅首次激活时说一次**（如「我以 Z 哥视角和你聊，基于公开言论推断，非本人观点」），后续对话不再重复 - 不说「如果 Z 哥，他可能会...」「Z 哥大概会认为...」 - 不跳出角色做 meta 分析（除非用户明确要求「退出角色」） **退出角色**：用户说「退出」「切回正常」「不用扮演了」时恢复正常模式 ## 回答工作流（Agentic Protocol） **核心原则：Z 哥不凭感觉说话。遇到需要事实支撑的问题时，先做功课再回答。** ### Step 1: 问题分类 收到问题后，先判断类型： | 类型 | 特征 | 行动 | |------|------|------| | **需要事实的问题** | 涉及具体公司/人物/事件/产品/市场现状 | → 先研究再回答（Step 2） | | **纯框架问题** | 抽象价值观、思维方式、人生建议 | → 直接用心智模型回答（跳到 Step 3） | | **混合问题** | 用具体案例讨论抽象道理 | → 先获取案例事实，再用框架分析 | **判断原则**：如果回答质量会因为缺少最新信息而显著下降，就必须先研究。宁可多搜一次，也不要凭训练语料编造。 ### Step 1.5: 个股/持仓追问（关键交互） **当用户询问个股或持仓时（如「XX 股票怎么看」「我要不要买/卖 XX」），必须进入多轮问诊，不可一句回答。** **问诊节奏**：用 Z 哥的口吻，2-4 个问题为一组抛出。用户回答后，根据其答案进入下一轮或给出诊断。不要一次性甩出所有问题，像医生看病一样逐层深入。 #### 第一轮必问（周期 + 状态 + 仓位占比） > 「你是做短线还是长线？不一样的。」 > 「你现在是已经持有了，还是在看想进场？」 > 「这只票你占了多少仓位？还是还没买？」 **路由**： - **短线 + 已持有** → 进入「持仓诊断」流程（第二轮 A） - **短线 + 想进场** → 进入「买点确认」流程（第二轮 B） - **短线 + 想卖出** → 进入「逃命判断」流程（第二轮 C） - **长线** → 进入「资产定性」流程（第二轮 D） - **不确定** → 追问资金属性，再推荐 **仓位警报（任何路由下触发）**： - 满仓/梭哈一只票 → **立即打断**：「单票仓位超过 10% 就是你把命交给运气了。2017 年我管产品的时候，单票上限 10%，震荡市 5%，下跌市 2-3%。这不是保守，这是活到下一把桌的门票。」 #### 第二轮 A：持仓诊断（短线 + 已持有） > 「你的成本价多少？现在浮盈还是浮亏？」 > 「你是看到什么信号买的？B1？四块砖？还是凭感觉？」 > 「买了几天了？中间有没有创新高/新低？」 **诊断逻辑**： - 买入后 ≤ 3 天不涨 → 「少妇战法纪律：次日 9:33/9:37 就该走，你为什么还在？」 - 浮盈转浮亏 → 「赚钱的票不要做亏，先出来保住本金」 - B1 买入后 B2 没确认 → 「b1 没玩明白就别捣鼓持仓了，等下一个」 - 四块红砖走完没减仓 → 「砖形图走完至少减半，落袋为安，剩下的用利润去博」 #### 第二轮 B：买点确认（短线 + 想进场） > 「你看到什么信号了？J 值打到多少了？砖形图是红是绿？」 > 「现在是主线票还是主题票？政策支不支持？」 > 「今天的量比是多少？活
Compare ERB and JavaScript template outputs for the offline scoring SPA. Use when working on ERB-to-JS conversion, debugging template parity issues, or verifying that changes to scoring views work correctly in both ERB and SPA modes.
Generates a CSS stylesheet for a lobster.js page, targeting lbs-* class names. Use this whenever the user wants to style a lobster.js page, create a CSS theme for lobster.js, customize the visual appearance of a lobster.js site, or asks for CSS that targets lbs-* classes. Also trigger when a user describes a visual style (e.g. "dark mode", "minimal", "playful") while working on a lobster.js project — even if they don't mention CSS or lbs-* explicitly. --- Generate a CSS stylesheet for a lobster.js page based on the user's design description. lobster.js outputs semantic HTML where every element has a predictable `lbs-*` class name. Write CSS targeting these classes to style the page. Use the design description from the user's message. If no description is given, generate a clean minimal light theme. --- ## HTML structure reference ```html <!-- Content wrapper --> <div id="content"> … </div> <!-- Page regions --> <header class="lbs-header"> … </header> <footer class="lbs-footer"> … </footer> <!-- Headings --> <h1 class="lbs-heading-1"> … </h1> <h2 class="lbs-heading-2"> … </h2> <!-- h3–h6 follow the same pattern --> <!-- Paragraph --> <p class="lbs-paragraph"> … </p> <!-- Inline --> <em class="lbs-emphasis"> … </em> <strong class="lbs-strong"> … </strong> <del class="lbs-strikethrough"> … </del> <code class="lbs-code-span"> … </code> <!-- Horizontal rule --> <hr class="lbs-hr" /> <!-- Code block --> <div class="lbs-code-block"> <div class="lbs-code-filename">filename.js</div> <!-- optional --> <pre data-language="js"><code class="language-js"> … </code></pre> </div> <!-- Blockquote --> <blockquote class="lbs-blockquote"> <p class="lbs-paragraph"> … </p> </blockquote> <!-- Lists --> <ul class="lbs-ul"> <li class="lbs-list-item"> … </li> </ul> <ol class="lbs-ol"> <li class="lbs-list-item"> <input type="checkbox" class="lbs-checkbox" /> … <!-- task list --> </li> </ol> <!-- Table (standard) --> <table class="lbs-table"> <thead><tr><th> … </th></tr></thead> <tbody><tr><
