OpenClaw/clawhub-security-signals huggingface dataset:
Skill: bytesagain1/biomass (version 1.0.0)

--- name: "biomass" version: "1.0.0" description: "Biomass energy reference — feedstock properties, combustion, gasification, pyrolysis, and pelletization. Use when evaluating biomass resources, designing conversion systems, or calculating heating values." author: "BytesAgain" homepage: "https://bytesagain.com" source: "https://github.com/bytesagain/ai-skills" tags: [biomass, combustion, gasification, pyrolysis, pellet, renewable, energy] category: "energy" --- # Biomass — Biomass Energy Reference Quick-reference skill for biomass energy conversion, feedstock properties, and thermochemical processes. ## When to Use - Evaluating biomass feedstocks for energy production - Comparing combustion, gasification, and pyrolysis pathways - Calculating heating values and moisture impact - Designing biomass boilers or gasifier systems - Understanding pelletization and densification ## Commands ### `intro` ```bash scripts/script.sh intro ``` Overview of biomass energy — types, conversion pathways, and global context. ### `feedstocks` ```bash scripts/script.sh feedstocks ``` Biomass feedstock properties: heating values, moisture, ash content, and composition. ### `combustion` ```bash scripts/script.sh combustion ``` Biomass combustion principles — grate, fluidized bed, stoker, and efficiency factors. ### `gasification` ```bash scripts/script.sh gasification ``` Gasification technology — updraft, downdraft, fluidized bed, syngas composition. ### `pyrolysis` ```bash scripts/script.sh pyrolysis ``` Pyrolysis processes — slow, fast, flash — and product distributions (biochar, bio-oil, syngas). ### `pellets` ```bash scripts/script.sh pellets ``` Pelletization — feedstock preparation, press types, quality standards (ENplus). ### `emissions` ```bash scripts/script.sh emissions ``` Biomass emission profiles — particulates, NOx, CO, and mitigation strategies. ### `sizing` ```bash scripts/script.sh sizing ``` Plant sizing calculations — fuel consumption, storage, boiler capacity rules of thumb. ### `help` ```bash scripts/script.sh help ``` ### `version` ```bash scripts/script.sh version ``` --- *Powered by BytesAgain | bytesagain.com | [REDACTED_SECRET]*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: 27555402-spec/cn-daily-tools (version 1.0.0)

--- name: cn-daily-tools description: 中文日常工具集：天气预报、汇率查询、新闻摘要、快递追踪。适合中文用户日常使用，无需API密钥，开箱即用。Use when Chinese users need weather forecasts, currency exchange rates, news summaries, or package tracking in Chinese. --- # 中文日常工具集 CN Daily Tools 开箱即用的中文实用工具，无需任何API密钥。 ## 包含工具 ### 🌤 天气查询 ``` 查天气：北京明天 查天气：上海本周 查天气：[任意城市] [时间范围] ``` - 支持全国所有城市 - 实时温度、湿度、风速 - 3天/7天预报 - 体感温度和穿衣建议 ### 💱 汇率换算 ``` 汇率：100美元换人民币 汇率：1000元换日元 汇率：[金额][货币]换[目标货币] ``` - 实时汇率（误差<1%） - 支持USD/EUR/JPY/KRW/HKD/GBP等主流货币 - 历史趋势简析 ### 📰 新闻摘要 ``` 今日新闻：科技 今日新闻：财经 今日新闻：[领域] ``` - 抓取最新中文新闻 - AI提炼3句话摘要 - 附原文链接 ### 📦 快递追踪 ``` 查快递：顺丰 123456789 查快递：[快递公司] [单号] ``` - 支持顺丰、圆通、中通、韵达、京东等主流快递 - 实时物流状态 - 预计到达时间 ## 使用示例 **用户：** 北京明天天气怎么样？要带伞吗？ **助手：** > 🌤 北京明天（周二）：晴转多云 > 最高气温 12°C，最低 3°C > 💨 北风3级，湿度45% > ☂️ 降雨概率：8%，**不需要带伞** > 👔 穿衣建议：厚外套+毛衣 --- **用户：** 500美元现在能换多少人民币？ **助手：** > 💱 实时汇率（2024-03-09 23:00） > 1 USD = 7.2156 CNY > **500 USD = 3,607.80 CNY** > 📈 近7日：人民币小幅升值0.3% ## 特点 - ✅ 纯中文交互 - ✅ 零配置，开箱即用 - ✅ 无需任何API密钥 - ✅ 回答简洁，直接给结论 - ✅ 适合移动端使用


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: opoojkk/wechat-mp-to-notion (version 0.1.0)

--- name: wechat-mp-to-notion description: Fetch WeChat public account (微信公众号) articles from mp.weixin.qq.com links and save them into Notion as structured pages. Use when the user wants to archive a 微信公众号 article to Notion, save a WeChat mp article into a Notion page/database, or convert a public WeChat article into a readable Notion document. --- # WeChat MP article → Notion Save a public WeChat article into Notion. ## What this skill does - Fetch article HTML from `https://mp.weixin.qq.com/s/...` - Extract title / author / publish date - Parse article body into readable text blocks - Preserve image positions with external image URLs where possible - Create a new Notion page under a target page or in a target database ## Use Run the script: ```bash python3 /root/.openclaw/workspace/skills/wechat-mp-to-notion/scripts/save_wechat_mp_to_notion.py <wechat_url> <notion_parent_id> [--parent-type page|database] ``` Examples: ```bash python3 /root/.openclaw/workspace/skills/wechat-mp-to-notion/scripts/save_wechat_mp_to_notion.py "https://mp.weixin.qq.com/s/xxxx" 32c51492e44780c48647f03febbc6c84 python3 /root/.openclaw/workspace/skills/wechat-mp-to-notion/scripts/save_wechat_mp_to_notion.py "https://mp.weixin.qq.com/s/xxxx" c5f3656c2e8741e88f53a00faf66355e --parent-type database ``` ## Notes - Require `NOTION_API_KEY` - The target Notion page/database must be shared with the `openclaw` integration - Prefer `page` parent when archiving long-form articles - Prefer `database` parent when the user explicitly wants one row per article - If the article is very long, the script chunks content into multiple append requests


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: humanagent/xmtp-cli (version 1.0.0)

--- name: xmtp-cli description: > Run and script the XMTP CLI for testing, debugging, and interacting with XMTP conversations, groups, and messages. Use when the user needs init, send, list, groups, debug, sync, permissions, or content commands from the CLI. license: MIT metadata: author: xmtp version: "1.0.0" --- # XMTP CLI Use the `xmtp` command to test, debug, and interact with XMTP conversations, groups, and messages from the command line. This skill is the entry point; use the sub-skills below for specific CLI tasks. ## When to apply - Testing or debugging XMTP from the command line - Sending messages or creating and managing groups - Listing or finding conversations, members, and messages - Syncing conversations and messages - Managing group permissions - Demonstrating content types (text, markdown, attachment, transaction, deeplink, miniapp) ## Sub-skills | Sub-skill | Use when | |-----------|----------| | **setup** | Initialize CLI and configure env (init, env variables) | | **groups** | Create DM or group, update group metadata | | **send** | Send messages to address or group | | **list** | List conversations, members, messages; find by address or inbox | | **debug** | Get info, resolve address, inspect inbox | | **sync** | Sync conversations or sync all | | **permissions** | List/info group permissions, update permissions | | **content** | Demo text, markdown, attachment, transaction, deeplink, miniapp | | **debugging** | Enable CLI debug logging (XMTP_FORCE_DEBUG env) | ## How to use 1. Pick the sub-skill that matches the task (e.g. send message → **send**). 2. Read that sub-skill’s `SKILL.md` and its `rules/` for step-by-step guidance. ## Install ```bash npm install -g @xmtp/cli # or pnpm add -g @xmtp/cli # or yarn global add @xmtp/cli ``` ## Run without install ```bash npx @xmtp/cli <command> <arguments> # or pnpx / yarn dlx ``` ## Help ```bash xmtp --help xmtp <command> --help ``` Full documentation: [docs.xmtp.org](https://docs.xmtp.org)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: inuyashayang/zhihu-post (version 1.0.0)

--- name: zhihu-post description: 在知乎发布内容（专栏文章、想法/动态）。使用 Chrome Browser Relay 控制用户的 Chrome 浏览器完成发布。触发词：知乎发帖、发知乎、知乎文章、知乎想法、zhihu post、发布到知乎、帮我发知乎。 --- # 知乎发帖技能 ## 内容类型 | 类型 | URL | 适用场景 | |------|-----|----------| | 专栏文章 | https://zhuanlan.zhihu.com/write | 长文、深度内容 | | 想法（动态） | https://www.zhihu.com/ | 短内容、随感、≤1000字 | ## 前置条件 用户需在 **Windows Chrome** 中完成以下操作： 1. 打开目标 URL（文章或首页） 2. 确保已登录知乎 3. 点击 OpenClaw Browser Relay 工具栏图标 attach tab（badge 亮起） ## 发布专栏文章流程 ### Step 1：确认 tab 连接 ``` browser(action=tabs, profile="chrome") ``` 确认知乎编辑器页面在列表中。 ### Step 2：获取页面快照 ``` browser(action=snapshot, profile="chrome", interactive=True) ``` ### Step 3：填写标题 知乎文章标题 input selector： ``` input[placeholder*="标题"] ``` 或用 aria ref 点击标题区域，用 `browser(action=act, request={kind:type, ref:<ref>, text:"..."})` 填入。 也可用 JS： ```javascript const titleInput = document.querySelector('textarea[placeholder], input[placeholder*="标题"]'); titleInput.value = "你的标题"; titleInput.dispatchEvent(new Event('input', {bubbles: true})); ``` ### Step 4：填写正文 知乎文章编辑器是 **Draft.js**，selector： ``` .notranslate.public-DraftEditor-content[contenteditable="true"] ``` ⚠️ Draft.js **不能直接赋值 innerHTML**，必须用 `execCommand`： ```javascript const el = document.querySelector('[contenteditable="true"]'); el.focus(); document.execCommand('selectAll'); document.execCommand('insertText', false, '你的正文内容（纯文本）'); ``` 如需多段落，插入 `\n\n` 分隔段落。如需加粗/标题等富文本格式，点击工具栏对应按钮后再执行 insertText。 ### Step 5：发布 快照找到「发布」按钮 ref，点击： ``` browser(action=act, profile="chrome", request={kind:click, ref:<发布按钮ref>}) ``` 或 JS： ```javascript // 找包含"发布"文字的按钮 [...document.querySelectorAll('button')].find(b => b.textContent.includes('发布文章'))?.click() ``` ### Step 6：确认发布成功 页面跳转到文章详情页（URL 含 `zhuanlan.zhihu.com/p/`）即为成功。 --- ## 发布想法（动态）流程 想法是知乎首页的短内容框，限制约 1000 字。 ### Step 1：打开想法输入框 在 https://www.zhihu.com/ 快照后，点击「写想法」或顶部输入框触发编辑器展开。 ### Step 2：填写内容 想法编辑器 selector（contenteditable div）： ```javascript const editor = document.querySelector('.PublishThought-editor .DraftEditor-contentWrapper [contenteditable="true"]'); // 或尝试： const editor = document.querySelector('[contenteditable="true"][data-contents]'); editor.focus(); ``` 用 `browser(action=act, request={kind:type, ref:<ref>, text:"..."})` 输入，或 JS 设置。 ### Step 3：发布 点击「发布」按钮（通常右下角）。 --- ## 注意事项 - **首次使用前**先手动在 Chrome 登录知乎，避免发布时弹出验证码 - 内容含图片时：先将图片复制到 `/tmp/openclaw/uploads/`，再用 `browser(action=upload)` 上传 - 知乎对新账号有发布频率限制，连续发布间隔 ≥ 3 分钟 - 发布完成后截图 `browser(action=screenshot)` 留存证据 ## 参考文件 - `references/dom-selectors.md` — 知乎各页面 DOM 结构速查


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (HIGH)
Skill: itsfabioroma/transcribee (version 1.2.1)

--- name: transcribee description: Transcribe YouTube videos and local audio/video files with speaker diarization. Use when user asks to transcribe a YouTube URL, podcast, video, or audio file. Outputs clean speaker-labeled transcripts ready for LLM analysis. --- # Transcribee Transcribe YouTube videos and local media files with speaker diarization via ElevenLabs. ## Usage ```bash # YouTube video transcribee "https://www.youtube.com/watch?v=..." # Local video transcribee ~/path/to/video.mp4 # Local audio transcribee ~/path/to/podcast.mp3 ``` **Always quote URLs** containing `&` or special characters. ## Output Transcripts save to: `~/Documents/transcripts/{category}/{title}-{date}/` | File | Use | |------|-----| | `transcription.txt` | Speaker-labeled transcript | | `transcription-raw.txt` | Plain text, no speakers | | `transcription-raw.json` | Word-level timings | | `metadata.json` | Video info, language, category | ## Supported Formats - **Audio:** mp3, m4a, wav, ogg, flac - **Video:** mp4, mkv, webm, mov, avi - **URLs:** youtube.com, youtu.be ## Dependencies ```bash brew install yt-dlp ffmpeg ``` ## Troubleshooting | Error | Fix | |-------|-----| | `yt-dlp not found` | `brew install yt-dlp` | | `ffmpeg not found` | `brew install ffmpeg` | | API errors | Check `.env` file in transcribee directory |

path: package.json
content: {
    "name": "transcribee",
    "version": "1.0.0",
    "description": "Transcribe audio from YouTube videos",
    "main": "index.js",
    "scripts": {
        "test": "echo \"Error: no test specified\" && exit 1"
    },
    "keywords": [],
    "author": "",
    "license": "ISC",
    "packageManager": "pnpm@10.10.0",
    "dependencies": {
        "@anthropic-ai/sdk": "^0.67.0",
        "dotenv": "^16.5.0",
        "elevenlabs": "^1.57.0",
        "tsx": "^4.19.4"
    },
    "devDependencies": {
        "@types/node": "^22.15.12"
    }
}


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: amdf01-debug/sw-form-cro (version 1.0.0)

# Form CRO Skill ## Trigger Optimise forms for higher completion rates. **Trigger phrases:** "optimise this form", "form conversion", "reduce form abandonment", "form UX", "checkout form" ## Process 1. **Analyse**: Current fields, completion rate, drop-off points 2. **Reduce**: Remove unnecessary fields, consolidate where possible 3. **Restructure**: Multi-step if >4 fields, logical grouping, progress indicators 4. **Optimise**: Labels, placeholders, validation, error handling, autofill 5. **Test**: Recommend A/B tests for changes ## Key Principles - Every field you remove increases completion by ~5-10% - Multi-step forms convert 86% higher than single-step (for 5+ fields) - Inline validation > submit-and-see-errors - Social auth reduces friction by 50%+ for signups - "Continue" > "Submit" > "Next" for button text - Show "X of Y steps" for multi-step forms - Save partial progress for long forms - Required vs optional: mark optional, not required (fewer fields feel lighter) ## Output Redesigned form specification with: field list, order, validation rules, error messages, multi-step breakdown (if applicable), and button copy.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: goog/hnews (version 1.0.0)

# hnews — Markdown → 科技新闻网页 将 Markdown 格式的新闻列表（如 Hacker News 快照）转换为暗色主题的精美 HTML 网页。 ## 使用方式 ```bash python scripts/hnews.py INPUT.md -o OUTPUT.html ``` - `INPUT.md` — Markdown 新闻文件（支持 `N. [Title](url) (source)` 格式） - `-o OUTPUT.html` — 输出 HTML 路径（默认同目录下同名 `.html`） - `--title "自定义标题"` — 页面标题（默认从文件推断） ## Markdown 输入格式 ```markdown # Hacker News Front Page *Snapshot: 2026-03-29 ~14:35 CST* 1. [Title Here](https://example.com) (example.com) 123 points | author | 45 comments 2. [Another Title](https://foo.com) (foo.com) 67 points | someone | 8 comments ``` ## 触发条件 用户说「生成新闻网页」「hnews」时使用 ## 依赖 - Python 3.6+（无第三方依赖）


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: tengcareergmail/skill-setup-flow (version 1.0.0)

# skill-setup-flow 为已安装的 OpenClaw 技能创建标准化的设置流程。 ## 用途 当用户说： - "为 X 技能创建设置流程" - "初始化 X 技能的配置" - "设置 X 技能" - "启用 X 技能" 此技能提供标准化的设置步骤，包括： 1. 读取技能的 SKILL.md 了解需求 2. 创建必要的目录结构 3. 创建配置文件模板 4. 更新 SOUL.md/AGENTS.md 集成说明 5. 记录设置日志 ## 使用方法 ```bash # 为已安装的技能创建设置流程 为 self-improving 创建设置流程 初始化 proactive-agent 配置 设置 weather 技能 ``` ## 设置流程模板 ### 步骤 1: 读取技能文档 ``` 读取 ~/skills/{skill-name}/SKILL.md ``` ### 步骤 2: 创建目录结构 ```bash mkdir -p ~/skills/{skill-name}/config mkdir -p ~/skills/{skill-name}/data ``` ### 步骤 3: 创建配置文件 - `config.md` — 配置选项 - `state.md` — 运行时状态 - `memory.md` — 技能特定记忆 ### 步骤 4: 集成到核心文件 - 更新 `SOUL.md` 添加技能特定行为 - 更新 `AGENTS.md` 添加使用说明 - 更新 `MEMORY.md` 记录设置事件 ### 步骤 5: 记录设置日志 在 `skills/{skill-name}/setup-log.md` 记录： - 设置日期 - 配置选项 - 创建的文件 - 更新的核心文件 ## 示例：Self-Improving Agent 设置 ```markdown # Self-Improving Agent 设置流程 ## 1. 读取技能文档 ✅ 读取 ~/skills/self-improving/SKILL.md ## 2. 创建目录 ✅ ~/self-improving/ ├── memory.md (HOT tier) ├── corrections.md (纠正日志) ├── index.md (索引) ├── heartbeat-state.md (心跳状态) ├── projects/ (项目特定) ├── domains/ (领域特定) └── archive/ (归档) ## 3. 创建核心文件 ✅ memory.md — 初始偏好和规则 ✅ corrections.md — 纠正日志模板 ✅ index.md — 文件索引 ✅ heartbeat-state.md — 心跳跟踪 ## 4. 集成到核心文件 ✅ SOUL.md — 添加 Self-Improving 章节 ✅ AGENTS.md — 添加使用说明和命令 ## 5. 设置完成 - 日期：2026-04-04 - 状态：✅ 完成 - 测试：添加首条偏好记录 ``` ## 通用设置流程 对于任何技能，遵循以下模式： ### A. 配置型技能（需要 API 密钥等） 1. 创建 `config.md` 模板 2. 列出所需配置项 3. 提供配置示例 4. 测试连接 ### B. 数据型技能（需要数据存储） 1. 创建数据目录 2. 创建索引文件 3. 创建数据模板 4. 设置自动保存 ### C. 行为型技能（改变 AI 行为） 1. 更新 SOUL.md 2. 添加触发条件 3. 创建行为日志 4. 设置反馈循环 ### D. 工具型技能（提供新工具） 1. 创建工具配置文件 2. 添加使用示例 3. 更新 TOOLS.md 4. 创建快速参考 ## 设置日志模板 ```markdown # {Skill Name} 设置日志 ## 设置日期 {YYYY-MM-DD} ## 技能信息 - 来源：{clawhub/github/本地} - 版本：{version} - 路径：~/skills/{skill-name}/ ## 创建的文件 - [ ] {file1} - [ ] {file2} ## 更新的核心文件 - [ ] SOUL.md - [ ] AGENTS.md - [ ] MEMORY.md - [ ] TOOLS.md ## 配置选项 - 选项 1: {value} - 选项 2: {value} ## 测试结果 - [ ] 基本功能测试通过 - [ ] 集成测试通过 - [ ] 心跳测试通过（如适用） ## 下一步 - [ ] 配置心跳（如需要） - [ ] 添加自定义规则 - [ ] 测试学习流程 ``` ## 快速命令 创建技能时自动包含： ```markdown ## 🚀 快速启动 1. **基础设置** - `为 {skill} 创建设置流程` 2. **配置** - `配置 {skill} 的 {选项}` 3. **测试** - `测试 {skill} 功能` 4. **查看状态** - `{skill} 状态` ``` ## 最佳实践 1. **最小化初始配置** - 只创建必要的文件 2. **提供默认值** - 所有配置都有合理的默认值 3. **记录一切** - 设置日志要详细 4. **可逆操作** - 提供卸载/重置方法 5. **测试导向** - 设置完成后立即测试 ## 相关文件 - `~/skills/{skill-name}/SKILL.md` — 技能文档 - `~/skills/{skill-name}/setup-log.md` — 设置日志 - `~/skills/{skill-name}/config.md` — 配置选项 - `~/skills/{skill-name}/state.md` — 运行状态 ## 示例调用 ``` 为 self-improving 创建设置流程 为 proactive-agent 创建设置流程 为 weather 创建设置流程 为 news-summary 创建设置流程 ``` --- _此技能提供标准化的技能设置流程，确保每个技能都能正确配置和集成。_


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: ckchzh/daily-journal (version 1.0.0)

--- name: daily-journal description: "Daily Journal — Daily Journal — write, reflect, remember. Personal daily-use tool for tracking and organizing your life. Use when you need Daily Journal capabilities." runtime: python3 license: MIT --- # Daily Journal Daily Journal — write, reflect, remember ## Why This Skill? - Designed for everyday personal use - No external dependencies or accounts needed - Data stored locally — your privacy, your data - Simple commands, powerful results ## Commands - `write` — [text] Write today's entry - `today` — View today's entry - `yesterday` — View yesterday - `list` — [n] List recent entries (default 10) - `search` — <query> Search all entries - `mood` — <1-5> [note] Log mood (1=bad, 5=great) - `streak` — Writing streak - `gratitude` — [text] Gratitude log - `prompt` — Random writing prompt - `stats` — Journal statistics - `export` — [format] Export (md/json/html) - `info` — Version info ## Quick Start ```bash daily_journal.sh help ``` > **Note**: This is an original, independent implementation by BytesAgain. Not affiliated with or derived from any third-party project. --- Powered by BytesAgain | bytesagain.com | [REDACTED_SECRET]


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: cngvc/ai-engineering-interview (version 1.0.1)

--- name: ai-engineering-interview description: Generates high-signal AI Engineering / LLM Engineer interview questions by topic, level, and role. Covers LLM fundamentals, prompt engineering, RAG, vector DBs, agents, fine-tuning (LoRA/QLoRA), evals, observability, safety, and production systems. Trigger for requests like "give me interview questions on RAG", "quiz me on agents", "what are senior-level fine-tuning questions", or "interview questions for an AI engineer role". version: "1.0.1" --- Generate high-signal interview questions for AI Engineer / LLM Engineer roles. Ask (or infer) the topic and level, then output exactly ONE complete question with a one-line note on what it's testing. --- ## Topics LLM Fundamentals · Prompt Engineering · RAG Architecture · AI Agents · Fine-Tuning (LoRA/QLoRA) · Evaluation & Evals · LLM Observability · AI Safety & Guardrails · Production LLM Systems · LLM System Design · Multimodal AI · LLMOps · Edge AI · AI Governance · Embeddings · Real-Time AI ## Levels - **Screening** — Can they reason about LLMs as a component beyond "just call the API"? - **Mid** — Full pipeline thinking: RAG, evals, agents, cost/latency trade-offs - **Senior** — System design, failure modes, fine-tuning decisions, multi-agent, AI safety - **Staff** — Platform thinking, LLM serving infra, eval-as-infrastructure, build-vs-buy ## Output Format For the question: ``` Q: [Question — scenario-based, trade-off, failure mode, or design. Never pure definition.] Tests: [one line — what the interviewer is probing] ``` Prefer one question that can't be answered by Wikipedia + 5 minutes of reading. Do not add follow-up questions. ## Scheduled Daily Variant For the cron-triggered daily interview drill: - Generate exactly ONE senior-level AI Engineer or LLM Engineer question. - Rotate across the topic list in this skill instead of repeating the same cluster. - No markdown tables. - Use the default `Q:` and `Tests:` format. ## Reference Files - `skills/ai-engineering-interview/references/question-bank.md` — Curated questions by topic and level with expected answer shape, strong and weak signals, and possible follow-up prompts. Read this when the user wants a broader bank or asks for examples in a specific AI domain. - `skills/ai-engineering-interview/references/competencies.md` — Interview rubric for scoring systems thinking, production judgment, safety awareness, and communication depth. Read this when calibrating difficulty or explaining what a strong answer looks like.


Security scan verdicts — ClawScan: clean | static analysis: suspicious | VirusTotal:  | SkillSpector: clean (LOW)
Skill: codeofaxel/kiln (version 1.1.6)

# Kiln AI agent control of 3D printers — <!-- KILN_MCP_CAPABILITY_COUNT:OLD --> 815 MCP capabilities + <!-- KILN_CLI_COUNT:OLD --> 222 CLI commands. ## What it does Kiln gives AI agents (Claude, GPT, OpenClaw, custom) safe, direct control of 3D printers through the Model Context Protocol. One install and your agent can search model marketplaces, generate designs from text/sketches, slice STL/3MF files, queue prints, monitor progress with camera snapshots, manage multi-printer fleets, and estimate costs — all from a single conversation. ## Supported printers - **Bambu Lab** — A1, A1 Mini, P1S, P1P, X1C (MQTT + FTP) - **OctoPrint** — Any printer running OctoPrint - **Moonraker/Klipper** — Voron, Ender, etc. - **Creality** — K1/K2/Hi/Ender V3 KE-class printers when local Moonraker is reachable; older Marlin models via OctoPrint or serial - **Prusa Link** — MK4, Mini, XL - **Elegoo** — Saturn, Mars, Centauri Carbon (SDCP V2 + V3) ## Install ```bash pip install kiln3d ``` Or with pipx: ```bash pipx install kiln3d ``` ## Key capabilities - **<!-- KILN_MCP_TOOL_COUNT:OLD --> 808 MCP tools** for full printer lifecycle control - **<!-- KILN_CLI_COUNT:OLD --> 222 CLI commands** for human and agent use - **Model search** across MyMiniFactory, Cults3D, Thangs, GrabCAD, Etsy - **Text/sketch-to-3D generation** with multiple provider backends - **Auto-slicing** via PrusaSlicer or OrcaSlicer - **Fleet management** with multi-site, multi-printer support - **Safety enforcement** — pre-flight checks, G-code validation, temp limits, confirmation gates - **Print monitoring** with camera snapshots and failure detection - **Cost estimation** from G-code and Bambu 3MF metadata - **Material intelligence** — catalog, compatibility, substitution recommendations ## Links - [GitHub](https://github.com/codeofaxel/Kiln) - [PyPI](https://pypi.org/project/kiln3d/) - [Website](https://kiln3d.com) - [Docs](https://kiln3d.com/docs)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)