OpenClaw/clawhub-security-signals huggingface dataset:
Skill: cleanbinggmail/xhs-big-text-post (version 1.0.0)

--- name: xhs-big-text-post description: 小红书大字图全流程制作技能。当用户需要制作小红书配图（带文字的大图、卖点图）时触发。完整流程：理解需求 → 精炼文案 → 用 AI 生成底图 → 叠加文字 → 通过飞书发出。所有图片存 /workspace/xhs/。 author: cleanbing ([REDACTED_SECRET]) (AI生产力廖老师) --- # XHS 大字图制作 ## 工作目录 `/workspace/xhs/`（所有图片存此处） ## 流程（5步） ### Step 1 — 理解需求 用户发来一段文字，用最简洁的方式提炼核心文案，控制在20字以内。 > 原则：一个卖点，一句话，一个认知 ### Step 2 — 设计视觉隐喻 根据文案设计视觉概念（不要直接照搬字面）。 **常见模式：** | 主题 | 视觉隐喻 | |------|---------| | 对比/不是一回事 | 左右分栏，箭头区隔 | | 警告/禁止 | 红色警告框，叉号 | | 层级/阶梯 | 蛋糕/阶梯/金字塔 | | 变身/进化 | 破壳/蜕变/光效 | | 组合/打包 | 拼图/盒子/容器 | ### Step 3 — 生成底图 调用 `image_synthesize`，描述画面： ``` 小红书配图风格，[描述视觉概念]，深色背景，整体简约高级 ``` 输出路径：`/workspace/xhs/xhs_[序号]_base.png` ### Step 4 — 叠加文字 用 `images_understand` 确认底图效果。 然后调用 `image_synthesize`，以底图为参考图（input_files），在图片中下方叠加文字。 输出路径：`/workspace/xhs/xhs_[序号]_final.png` ### Step 5 — 发送飞书 调用 `message(action=send, channel=feishu, media=图片CDN路径)`，把最终成品发给廖老师。 ## 参考文档 详细工具说明和工作流见 `references/workflow.md`

path: references/workflow.md
content: # 小红书大字图 — 详细工作流

## 工具链

| 步骤 | 工具 | 作用 |
|------|------|------|
| 生成配图 | `image_synthesize` | 根据文字描述生成意境图 |
| 分析图片 | `images_understand` | 确认生成效果是否符合预期 |
| 叠加文字 | `image_synthesize` + input_files | 把文字和底图合成为成品 |
| 发送图片 | `message(channel=feishu, action=send, media)` | 通过飞书发出 |

## Prompt 模板

### 生成底图
```
小红书配图风格，[视觉隐喻描述]，深色背景，整体简约高级，文字字体圆润有质感
```

### 叠加文字
在 `image_synthesize` 的 `requests` 中：
- `prompt`：在图片中下方的醒目位置叠加文字「[文案]」，字体大、白色描边
- `input_files`：底图路径

## 廖老师三张图设计单

### 图1：K2.5降智主题
- **文案**：「K2.5 = 降智打击？」
- **风格**：脑雾/断片风格，深色模糊背景，人物面部表情缺失或打码感
- **输出**：`/workspace/xhs/xhs_01_final.png`

### 图2：抽风终止主题
- **文案**：「抽风？按一下就停」
- **风格**：急刹/红色警告风格，红色闪电或叉号，急刹车视觉
- **输出**：`/workspace/xhs/xhs_02_final.png`

### 图3：三层记忆主题
- **文案**：「记忆三层，你在哪层」
- **风格**：层级蛋糕/结构图风格，三层蛋糕或三层书架，清晰分层
- **输出**：`/workspace/xhs/xhs_03_final.png`

## 执行顺序
1. 依次执行图1→图2→图3
2. 每张图：底图生成 → images_understand确认 → 叠加文字 → 发送飞书
3. 三张全部发完后，统一回复廖老师「三张图已全部发出」


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: seulkilu/meeting-to-action-items (version 1.1.0)

--- name: meeting-to-action-items description: 将非结构化的会议记录、聊天记录、语音转写文本转化为结构化的可执行项目状态 — 包含分类后的待办事项、负责人、截止时间、决策记录、风险标记和里程碑排期。用户说出"整理会议记录"、"生成会议待办"、"从聊天记录提取任务"等请求时激活。 --- # Meeting to Action Items 将非结构化对话转化为**项目状态**（Project State）——即 Linear、Notion、飞书等项目管理工具使用的结构化输出格式。采用四段式布局，便于快速扫读：会议摘要 → 待办事项 → 决策与风险 → 下期里程碑。 ## 工作流程 ### 1. 接收输入 支持来源：会议纪要、聊天记录（微信/Slack/飞书/Discord）、语音转写文本、要点笔记。 触发短语： - "帮我整理会议记录" / "生成会议待办" - "从聊天记录提取任务" / "总结这个会议" - "meeting action items" / "extract tasks" / "convert to action items" ### 2. 解析与分析 可使用 `scripts/parse_meeting.py` 进行自动提取，或按以下规则手动分类每条句子： | 类型 | 标记词 | |------|--------| | **任务** | 负责/完成/做/搞定/跟进 + 人名；"will handle / needs to" | | **决策** | 决定/确定/确认/同意；"agreed / confirmed / approved" | | **风险** | 但/不过/担心/风险/还没；"blocker / stuck / pending" | | **讨论** | 考虑/建议/觉得/想；中性表述 | 以会议日期为基准解析相对日期： - 明天 → 基准日 + 1天，下周一 → 下个周一，本周五 → 本周五 - 下周三前 → 解析为下周三的截止时间 ### 3. 输出结构 ``` Meeting Summary {1-3 行，概括核心议题与关键约束。不使用 emoji。} Action Items Owner Task Deadline Pri ─── ──── ─────── ─── {负责人} {具体可交付物} {日期} {P0/P1/P2} {负责人} {具体可交付物} {日期} {P0/P1/P2} Decisions - {决策事项 1} - {决策事项 2} Risks - {风险/阻塞 1} - {风险/阻塞 2} Next Milestones - {日期}: {里程碑} - {日期}: {里程碑} ``` ### 4. 格式规则 | 维度 | 规则 | |------|------| | 优先级 | P0 = 阻塞/关键，P1 = 高优（本迭代），P2 = 正常，P3 = 低优 | | 段落顺序 | 摘要 → 待办事项 → 决策 → 风险 → 下期里程碑 | | 负责人列 | 使用真实姓名（非"张三"→"张"，而是"张三"）；去掉称谓（张姐→张） | | 截止时间列 | 可解析时使用 YYYY-MM-DD；无法确定时标记为"待确认" | | 任务描述 | 以动词开头（封装、完成、确认、提交……） | | 决策表述 | 以主题开头，后接结论："门磁数据 → 变化触发上报" | | 风险表述 | 每行一条风险，包含影响说明（如"→ 全量延迟 3 天"） | | 摘要语气 | 客观、第三人称，不使用感叹号或 emoji | | 对齐方式 | 使用空格对齐列，不使用 markdown 表格（纯文本） | | 语言适配 | 输入为中文则输出全中文，但段落标题保持英文 | ### 5. 优先级推断规则 - **P0** — 依赖阻塞、链路断裂、关键数据缺失 - **P1** — 马上/赶紧/立刻/urgent/重要/本迭代硬性截止 - **P2** — 大部分迭代任务的默认优先级 - **P3** — 有空/不着急/sometime/no rush ### 6. 相对日期解析 以会议日期为基准。示例（基准日 = 2026-05-21 周四）： | 表达式 | 解析结果 | |--------|---------| | 明天 | 2026-05-22 | | 下周一 | 2026-05-25 | | 下周三 | 2026-05-27 | | 本周五 | 2026-05-22 | | 周五前 | 2026-05-22 | | 下周五 | 2026-05-29 | | 周五 | 本周五（若会议日在周五前），否则下周五 | | 下下周二 | 2026-06-02 | | 端午节后 | 标记为"端午假期后"（准确日期依年份而定） | | today / tomorrow | 当天 / 基准日 + 1天 | | by Friday | 按最近一个周五解析 | 规则：当星期几的引用不带"上/本/这/下"前缀，且会议日在当周该日之前时，优先按本周处理；若当周该日已过，则默认为下周。 ## 参考文档 详细实体识别模式、边界情况和优先级分类见 `references/patterns.md`。 ## 脚本工具 `scripts/parse_meeting.py` 支持批量提取，包含相对日期解析和优先级推断。使用 `--json` 参数输出结构化 JSON 供程序消费；直接运行则输出格式化文本。支持中英文混合输入和多人对话场景。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: deanleefumu/group-summarizer (version 1.0.0)

--- name: group_summarizer description: "Synthesizes member-parent markdown reports into a continuous narrative." metadata: openclaw: emoji: 🦞 version: "1.0.0" --- # group_summarizer Read all markdown files in "~/openclaw_agent/src/temp_sync/" recursively. ## Logic 1. **Reading**: Read all markdown files in "~/openclaw_agent/src/temp_sync/" recursively. 2. **Grouping**: Categorize all content by the parent folder name (which represents the Team Member). 3. **Synthesis**: For each member, treat multiple days of reports as a single, continuous progress narrative. Do not repeat daily headers; blend them into a coherent update. 4. **Exclusion**: Do not include any meta-talk (e.g., "Here is the summary...") or technical details about how the file was generated. Provide only the synthesized content. 5. **Formatting**: - Use `## [Member Name]` as the primary headers. - Use standard markdown for the body. ## Output - **Target Path**: "~/openclaw_agent/src/latest_summaries/daily/{{date}}.md" - **Date Variable**: Use the current UTC+8 date in `YYYY-MM-DD` format. ## Usage Examples - "Run group_summarizer" - "Generate the daily report using group_summarizer"


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: kostja94/404-page-generator (version 1.0.1)

--- name: 404-page-generator description: When the user wants to create, optimize, or audit 404 error pages. Also use when the user mentions "404 page," "404 error," "error page," "page not found," "broken link page," "404 design," "custom 404," "404 redirect," "404 page UX," or "404 recovery." For sitewide page planning, use website-structure. metadata: version: 1.0.1 --- # Pages: 404 Error Page Guides 404 error page design for UX, conversion recovery, and brand consistency. **When invoking**: On **first use**, if helpful, open with 1-2 sentences on what this skill covers and why it matters, then provide the main output. On **subsequent use** or when the user asks to skip, go directly to the main output. ## Initial Assessment **Check for project context first:** If `.claude/project-context.md` or `.cursor/project-context.md` exists, read it for brand voice, key pages, and Section 12 (Visual Identity). Identify: 1. **Site structure**: Key pages to link (homepage, popular pages, search) 2. **Brand tone**: Friendly, professional, playful 3. **Conversion goal**: Recover lost visitors, drive to key pages ## Best Practices ### Clear Error Messaging - **User-friendly**: Neutral message explaining the page wasn't found - **Optional 404 display**: Can show "404" but avoid blaming the user - **Empathetic tone**: Acknowledge the error gracefully; turn frustration into opportunity ### Navigation and Redirection | Element | Purpose | |---------|---------| | **Site navigation** | Header/footer so users know they're still on your site | | **Search** | Help users find what they need | | **Popular pages** | Links to homepage, features, pricing, blog | | **Similar URLs** | Suggest corrections for common typos | | **Avoid auto-redirect** | Unless confident of user intent | ### Design and Branding - **Consistent design**: Same header, footer, colors as rest of site (brand-visual-generator) - **Avoid confusion**: Users should not think they've left your domain - **Mobile responsive**: Test on all devices ### Conversion Opportunities 404 pages can drive conversions by: - Showcasing popular products or features - Featuring testimonials or social proof - Offering special promotions or value - Linking to mobile app or newsletter ### Technical - **Track 404s**: Monitor broken links, fix or redirect - **Accessibility**: Maintain WCAG standards - **HTTP status**: Ensure proper 404 response code ## Output Format - **Copy** options (headline, message, CTA) - **Link structure** (what to include) - **Design** checklist - **SEO**: Typically noindex; ensure canonical if needed ## Related Skills - **homepage-generator**: Primary escape route - **brand-visual-generator**: Typography, colors for consistent 404 design - **indexing**: noindex for 404 if desired - **title-tag, meta-description, page-metadata**: 404 page metadata


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ivangdavila/fitness (version 1.0.1)

--- name: "Fitness" description: "Auto-learns your fitness patterns. Absorbs data from wearables, conversations, and achievements." version: "1.0.1" changelog: "1.0.1: Preferences now persist across skill updates" --- ## Auto-Adaptive Fitness Tracking This skill auto-evolves. Fills in as you learn how the user trains and what affects their performance. **Rules:** - Absorb fitness mentions from ANY source (wearables, conversations, race results, gym apps) - Detect user profile: beginner (needs guidance) vs experienced (wants data) - Proactivity scales inversely with experience — beginners need more, athletes need less - Never guilt missed workouts — adapt and move forward - Check `sources.md` for data integrations, `profiles.md` for user types, `coaching.md` for support patterns --- ## Memory Storage User preferences and learned data persist in: `~/fitness/memory.md` **Format for memory.md:** ```markdown ### Sources <!-- Where fitness data comes from. Format: "source: reliability" --> <!-- Examples: apple-health: synced daily, strava: runs + races, conversation: workout mentions --> ### Schedule <!-- Detected training patterns. Format: "pattern" --> <!-- Examples: MWF strength 7am, Sat long run, Sun rest --> ### Correlations <!-- What affects their performance. Format: "factor: effect" --> <!-- Examples: sleep <6h: skip day, coffee pre-workout: +intensity, alcohol: -next day --> ### Preferences <!-- How they want fitness tracked. Format: "preference" --> <!-- Examples: remind before workouts, no rest day lectures, weekly summary only --> ### Flags <!-- Signs to watch for. Format: "signal" --> <!-- Examples: "too tired", missed 3+ days, injury mention, "legs are dead" --> ### Achievements <!-- PRs, milestones, events. Format: "achievement: date" --> <!-- Examples: bench 100kg: 2024-03, first marathon: 2024-10, 30 day streak: 2024-11 --> ``` *Empty sections = no data yet. Observe and fill.*


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: teweitao/traffic-data (version 1.0.0)

--- name: traffic-data description: Query traffic data - real-time road conditions, traffic incidents, SCATS intersection data metadata: {"clawdbot":{"emoji":"🚦","requires":{"bins":["node"],"env":["BAIDU_MAP_KEY","GAODE_MAP_KEY","SCATS_API_KEY"]}}} --- # Traffic Data Skill 查询交通数据，包括实时路况、交通事件、SCATS路口数据等。 ## 环境配置 在 `.env` 文件中添加： ```bash # 百度地图API（可选） BAIDU_MAP_KEY=your-baidu-map-api-key # 高德地图API（可选） GAODE_MAP_KEY=your-gaode-map-api-key # SCATS数据API（如果有） SCATS_API_KEY=your-scats-api-key ``` ## 功能 ### 1. 实时路况查询 ```bash node skills/traffic-data/road.js <城市> <道路名称> ``` ### 2. 交通事件查询 ```bash node skills/traffic-data/incident.js <城市> ``` ### 3. SCATS路口数据查询 ```bash node skills/traffic-data/scats.js <路口编号> ``` ## 示例 ```bash # 查询广州实时路况 node skills/traffic-data/road.js 广州 广州大道 # 查询广州交通事件 node skills/traffic-data/incident.js 广州 # 查询SCATS路口状态 node skills/traffic-data/scats.js 001 ``` ## API申请 - 百度地图开放平台：https://lbsyun.baidu.com/ - 高德地图开放平台：https://lbs.amap.com/


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

--- name: Designer description: Create logos, interfaces, and visual systems with principles of hierarchy, branding, and usability. metadata: {"clawdbot":{"emoji":"🎨","os":["linux","darwin","win32"]}} --- # Design Rules ## Logo & Brand - Simple enough to work at 16px favicon — complexity disappears at small scale - Recognizable in one color — gradients are enhancements, not crutches - Test on dark and light backgrounds — logos need both versions - 3-5 brand colors maximum — primary, secondary, accent, plus neutrals - Consistency builds recognition — every touchpoint reinforces or dilutes brand ## Visual Hierarchy - Size and weight guide attention — most important element most prominent - One focal point per screen — competing elements confuse users - Whitespace groups, separates, and elevates — it's not empty space - Alignment creates invisible order — grids make everything feel intentional - Contrast for emphasis, similarity for grouping — break patterns deliberately ## Typography - Two typefaces maximum — heading and body is enough - Body text 16px minimum on web — smaller is unreadable - Line height 1.4-1.6, line length 45-75 characters — readability basics - Pair fonts with contrast — serif with sans-serif, not two similar fonts ## Color - 60-30-10 rule: dominant, secondary, accent — prevents chaos - Warm colors advance, cool recede — use for depth and emphasis - Contrast ratio 4.5:1 for text — accessibility isn't optional - Test with colorblind simulators — 8% of men have color vision deficiency ## Layout - 8px grid for spacing — consistency without overthinking - Rule of thirds for visual interest — off-center is more dynamic - Negative space is active design — crowded layouts feel cheap - Mobile first, then scale up — constraints breed creativity - Touch targets 44px minimum — fingers are imprecise ## Icons & Illustration - Consistent stroke weight across icon set — mixing weights looks broken - Meaning clear without labels — ambiguous icons need text - Simple illustrations scale better — detail gets lost small - Style matches brand personality — playful vs corporate look different ## UX Principles - Don't make users think — obvious beats clever - Feedback for every action — users need confirmation - Error prevention over error messages — disable invalid actions - Design empty states, errors, loading first — they're most of the experience - Five users find 80% of usability issues — test early and often ## Design Systems - Components, not pages — reusable library - Tokens for colors, spacing, type — change once, update everywhere - Name by function: "primary-action" not "blue-button" - Document when to use, not just how it looks ## Presenting & Handoff - Show work in context — mockups in devices, logos on cards - Explain why, not just what — tell the decision story - All states: hover, active, disabled, error, loading — devs will ask - Specs in relative units — responsive needs flexibility - Review the build — implementation is the real design


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: wu-uk/lake-warming-attribution-meteorology-driver-classification (version 0.1.0)

--- name: meteorology-driver-classification description: Classify environmental and meteorological variables into driver categories for attribution analysis. Use when you need to group multiple variables into meaningful factor categories. license: MIT --- # Driver Classification Guide ## Overview When analyzing what drives changes in an environmental system, it is useful to group individual variables into broader categories based on their physical meaning. ## Common Driver Categories ### Heat Variables related to thermal energy and radiation: - Air temperature - Shortwave radiation - Longwave radiation - Net radiation (shortwave + longwave) - Surface temperature - Humidity - Cloud cover ### Flow Variables related to water movement: - Precipitation - Inflow - Outflow - Streamflow - Evaporation - Runoff - Groundwater flux ### Wind Variables related to atmospheric circulation: - Wind speed - Wind direction - Gust speed - Atmospheric pressure ### Human Variables related to anthropogenic activities: - Developed area - Agriculture area - Impervious surface - Population density - Industrial output - Land use change rate ## Derived Variables Sometimes raw variables need to be combined before analysis: ```python # Combine radiation components into net radiation df['NetRadiation'] = df['Longwave'] + df['Shortwave'] ``` ## Grouping Strategy 1. Identify all available variables in your dataset 2. Assign each variable to a category based on physical meaning 3. Create derived variables if needed 4. Variables in the same category should be correlated ## Validation After statistical grouping, verify that: - Variables load on expected components - Groupings make physical sense - Categories are mutually exclusive ## Best Practices - Use domain knowledge to define categories - Combine related sub-variables before analysis - Keep number of categories manageable (3-5 typically) - Document your classification decisions


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: mike47512/lofter-community (version 0.1.0)

--- name: "LOFTER" description: "提供二次元同人创作、摄影插画展示及兴趣社区指引。" --- # LOFTER ## 筛选项 - 内容分级（全年龄/青少年/付费/独播） - 清晰度/帧率（4K/1080P/杜比音效） - 题材领域（热剧/动漫/游戏竞技/文化博主） ## 返回字段 - 播放进度云记录/离线存量/下载速度 - 互动弹幕控制/倍速播放/画面比例调整 - 会员到期提醒/付费单集状态/推荐精准度 ## 示例请求 - “如何快速使用 LOFTER 处理 [具体场景任务] ？” - “LOFTER 的 [核心功能] 有哪些最新玩法/优惠信息？” ## 更新频率 - 跟随版权更新进度、流行趋势与算法热度每日迭代 ## 注意事项 - 观看请注意用眼健康；青少年建议开启青少年模式限制在线时长。


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: lyq8479/liufeng-greeting-skill (version 1.0.1)

------WebKitFormBoundaryt84l59ct13 Content-Disposition: form-data; name="file"; filename="SKILL.md" Content-Type: text/markdown --- name: liufeng-greeting-skill description: 柳峰专属问候技能 - 当用户说"你好"、"hello"、"嗨"、"在吗"时，回复问候语并附带当前日期和时间 author: 大白 (柳峰的AI助手) created: 2026-03-11 version: 1.0.1 homepage: https://clawhub.com/skills/liufeng-greeting-skill license: MIT metadata: {"clawdbot":{"emoji":"👋","requires":{"bins":["node"]},"install":[{"id":"clawhub","kind":"clawhub","slug":"liufeng-greeting-skill","label":"Install via ClawHub"}]}} --- # liufeng-greeting-skill 柳峰专属的个性化问候技能。当检测到用户发送问候消息时，自动回复个性化的问候语并显示当前日期和时间。 ## 🎯 功能特性 - **智能问候检测**：识别多种问候语变体（中文、英文、混合） - **个性化回复**：根据柳峰的偏好定制回复风格 - **时间显示**：自动显示当前日期和时间（中国标准时间） - **随机回复**：每次使用不同的问候语，避免重复 - **简单易用**：无需复杂配置，开箱即用 - **多平台支持**：适用于所有OpenClaw支持的聊天渠道 ## 🚀 快速开始 ```bash # 通过ClawHub安装 clawhub install liufeng-greeting-skill # 或手动安装到技能目录 cp -r liufeng-greeting-skill ~/.openclaw/workspace/skills/ ``` ## 触发关键词 当用户消息包含以下任意关键词时触发： - 你好 - hello - 嗨 - 在吗 - hi - 早上好 - 下午好 - 晚上好 ## 回复格式 ``` [个性化问候语] 当前时间：[YYYY-MM-DD HH:mm:ss] 时区：Asia/Shanghai (中国标准时间) ``` ## 示例 用户输入："你好" 技能回复： ``` 柳峰，你好！🌄 大白在这里随时为你服务。 当前时间：2026-03-11 00:10:25 时区：Asia/Shanghai (中国标准时间) ``` ## 安装与使用 1. 技能已自动安装到 OpenClaw 技能目录 2. 无需额外配置，立即生效 3. 在任意聊天渠道中发送问候语即可触发 ## 自定义配置 如需修改问候语风格，编辑 `greeting.js` 文件中的回复模板。 ## 更新日志 ### v1.0.0 (2026-03-11) - 初始版本发布 - 支持基本问候语检测 - 集成日期时间显示功能 - 个性化回复模板 ------WebKitFormBoundaryt84l59ct13--


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: supertilico2001/anygen-task-download (version 1.0.0)

--- name: anygen-task-download version: 1.0.0 description: "AnyGen: Download artifacts from a completed task." metadata: requires: bins: ["anygen"] env: ["ANYGEN_API_KEY"] install: - id: node kind: node package: "@anygen/cli" bins: ["anygen"] cliHelp: "anygen task +download --help" --- # task +download > **PREREQUISITE:** Read [`../anygen-shared/SKILL.md`](../anygen-shared/SKILL.md) for auth, global flags, and security rules. Download artifacts from a completed task. ## Usage ```bash anygen task +download --task-id <id> --output-dir <dir> ``` ## Flags | Flag | Required | Description | |------|----------|-------------| | `--task-id` | ✓ | Task ID | | `--output-dir` | — | Local directory to save files (default: current directory) | | `--file <name...>` | — | Download specific file(s) by name (repeatable) | | `--thumbnail` | — | Download thumbnail image instead of main files | ## Examples ```bash # Download all output files anygen task +download --task-id xxx # Download specific file(s) by name anygen task +download --task-id xxx --file report.pptx anygen task +download --task-id xxx --file report.pptx --file data.xlsx # Download thumbnail (for preview) anygen task +download --task-id xxx --thumbnail # Specify output directory anygen task +download --task-id xxx --output-dir ./output ``` ## Output Returns JSON with downloaded file paths: ```json { "status": "completed", "task_id": "xxx", "files": [ { "file": "./report.pptx", "name": "report.pptx" }, { "file": "./data.xlsx", "name": "data.xlsx" } ], "task_url": "https://..." } ``` ## Tips - The task must be in `completed` state. Use `task get --wait` first if needed. - Without `--file`, all output files are downloaded. - Use `--thumbnail` first to show a preview, then download the main files when user requests it. - For smart_draw tasks, diagram files are automatically rendered to PNG. - File names for `--file` come from the `output.files[].name` field in the task get response. > [!CAUTION] > This is a **write** command (writes files to disk) — confirm the output directory with the user. ## See Also - [`anygen-workflow-generate`](../anygen-workflow-generate/SKILL.md) — Full content generation workflow


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