OpenClaw/clawhub-security-signals huggingface dataset:
Skill: imwyvern/social-video-analytics (version 1.0.1)

--- name: social-video-analytics description: > Social Video Analytics is an AI analytics assistant for measuring how videos perform across social platforms and why some clips win. It helps teams read watch time, drop-off, hook strength, caption impact, and content patterns so they can iterate creative, posting strategy, and channel mix with evidence. 视频数据分析、社媒指标、完播率、内容复盘。 --- # Social Video Analytics > Track and analyze video performance across TikTok, Instagram Reels, YouTube Shorts, Douyin, and Xiaohongshu — unified metrics dashboard, competitor benchmarking, viral pattern detection, and content strategy recommendations powered by AI. ## What This Skill Does Social Video Analytics aggregates performance data from all major short-video platforms into a unified view. It identifies what's working (viral patterns, winning hooks, optimal posting times), benchmarks against competitors, and generates actionable content strategy recommendations. ### Core Capabilities 1. **Unified Dashboard** — One view for TikTok, Reels, Shorts, Douyin, XHS performance metrics 2. **Viral Pattern Detection** — AI identifies what makes your top-performing videos different from average ones 3. **Competitor Benchmarking** — Track competitor accounts, analyze their top content, identify gaps 4. **Hook Analysis** — Which opening hooks drive the highest watch-through rates 5. **Posting Time Optimization** — Data-driven recommendations for when to post on each platform 6. **Content Strategy Reports** — Weekly/monthly AI-generated reports with specific action items 7. **Trending Audio/Format Tracking** — Identify trending sounds and formats before they peak 8. **ROI Attribution** — Connect video performance to business outcomes (traffic, leads, sales) ### Key Metrics | Metric | What It Tells You | |--------|------------------| | Watch-Through Rate | Content quality + hook strength | | Save Rate | Content value + rewatch potential | | Share Rate | Viral potential | | Comment Sentiment | Audience resonance | | Follower Conversion | Account growth efficiency | | Click-Through Rate | Commercial conversion power | ### Analysis Types - **Content Audit** — Full account analysis with scoring and recommendations - **Competitor Report** — Side-by-side benchmarking with actionable takeaways - **Campaign Performance** — Track specific campaign metrics across platforms - **Trend Report** — Weekly trending formats, sounds, and topics in your niche ## Usage - "Analyze my TikTok account performance for the last 30 days" - "Compare our Xiaohongshu content against these 3 competitors" - "What video format is trending in the beauty niche this week?" - "Generate a content strategy report for our Q2 campaign" ## Upgrade For real-time analytics API and custom dashboards, visit https://mediaclawbot.com --- *Powered by MediaClaw — data-driven video strategy*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: zhao0112/clawbox (version 1.0.0)

--- name: clawbox-link-to-docs description: "Turn a user shared web link into two Feishu docs: (1) full original text archive with minimal loss and clear source metadata, and (2) structured analysis summary. Use when the user sends an article/report/blog URL and asks for full text plus analysis, web archive plus brief, or link to knowledge capture." --- # ClawBox Link to Docs Execute this workflow when user sends a URL and expects both source doc and analysis doc. ## Workflow 1. Fetch source content - Try readable extraction from the URL. - If redirected or homepage only content is returned, switch to alternative extraction path (browser or manual source capture) before proceeding. - Preserve original language and paragraph order. 2. Create or update two Feishu docs - Doc A: [Source] <title> - Doc B: [Analysis] <title> 3. Write Source doc (complete first) - Include source URL and capture time. - Write full original text with paragraph breaks. - Do not replace with short summary. - If extraction is partial, label clearly: Partial Capture plus missing scope note. 4. Write Analysis doc (structured) - Use the template in references/analysis-template.md. - Keep concise and decision oriented. 5. Quality gates (required) - Read back both docs after write. - Source doc fails if it is one line, too short, or clearly summary like. - If fail, rewrite once with better segmentation/chunking. 6. Report back to user - Return two doc links. - State completion status: Full Capture or Partial Capture. ## Non-negotiables - Default output is always two docs for a link task. - Source doc priority is fidelity over formatting. - Never claim full if content is truncated or fallback source is incomplete.

path: README.md
content: # clawbox

OpenClaw skill: turn a shared web link into two Feishu docs (source archive + analysis summary).

## Install

Place this folder under your OpenClaw skills directory and enable the skill:

- **clawbox**
- Skill ID: `clawbox-link-to-docs`


path: references/analysis-template.md
content: # Analysis Template

## 一句话结论
- 用 1-2 句说清核心判断。

## 核心问题（作者在解决什么）
- 问题定义
- 为什么重要

## 关键贡献
- 方法/框架贡献
- 结果或证据
- 与已有方案差异

## 风险与局限
- 数据/方法局限
- 落地风险

## 对业务/项目的启发
- 可直接复用的动作（3 条以内）

## 建议下一步
- 是否值得继续跟踪：高 / 中 / 低
- 若继续，下一步验证什么


Security scan verdicts — ClawScan: clean | static analysis:  | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: tanis90/word2md (version 1.0.0)

--- name: word2md description: "Document to Markdown converter - convert DOCX, PPTX, Excel files to Markdown. Use when extracting content from Word documents, PowerPoint presentations, or Excel spreadsheets." homepage: https://mineru.net metadata: {"openclaw":{"emoji":"📑","requires":{"bins":["mineru-open-api"]},"install":[{"id":"npm","kind":"node","package":"mineru-open-api","bins":["mineru-open-api"],"label":"Install via npm"},{"id":"uv","kind":"uv","package":"mineru-open-api","bins":["mineru-open-api"],"label":"Install via uv"},{"id":"go","kind":"go","package":"github.com/opendatalab/MinerU-Ecosystem/cli/mineru-open-api","bins":["mineru-open-api"],"label":"Install via go install","os":["darwin","linux"]}]}} --- # Word2MD - Fast Docx Conversion Convert Word, PowerPoint, and Excel files to Markdown using MinerU Open API. No API key required. ## Quick Start ```bash # Word2MD - Fast Docx Conversion mineru-open-api flash-extract report.docx # Word2MD - Fast Docx Conversion mineru-open-api flash-extract slides.pptx # Word2MD - Fast Docx Conversion mineru-open-api flash-extract data.xlsx # Word2MD - Fast Docx Conversion mineru-open-api flash-extract https://example.com/report.docx # Word2MD - Fast Docx Conversion mineru-open-api flash-extract report.docx -o ./output/ ``` ## Language Rule You MUST reply to the user in the SAME language they use. This is non-negotiable. ## Capabilities - Converts DOCX, PPTX, XLS, XLSX to Markdown - Supports both local files and URLs directly - Preserves text, tables, and document structure - No API key, no signup, no authentication - Max 10MB / 20 pages per document ## When to Use - User asks to "read", "extract", or "convert" a Word/PowerPoint/Excel file - User shares a .docx, .pptx, or .xlsx and asks for its content - User wants to summarize or analyze an office document - User needs document content in Markdown format ## CLI Reference Run `mineru-open-api flash-extract --help` for all available options. ## Data Privacy - `flash-extract` uploads the document to MinerU's cloud API for processing and returns the result. No account or API key is required. - Documents are processed in real-time and are not stored after extraction. - For details, see https://mineru.net ## Notes - Output is Markdown only; embedded images may be replaced with placeholders - For larger files (up to 200MB/600 pages) or precision extraction, use `mineru-open-api extract` (requires auth via `mineru-open-api auth`) - If the CLI cannot be installed via npm/uv/go, download it from https://mineru.net/ecosystem?tab=cli


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: franklu0819-lang/douyin-video-analyzer (version 3.7.4)

--- name: douyin-video-analyzer description: 深度拆解抖音视频，自动生成包含数据、结构、视觉、文案的完整分析报告。 version: 3.7.4 metadata: { "openclaw": { "requires": { "bins": ["ffmpeg", "yt-dlp", "node"], "env": ["ZHIPU_API_KEY"] }, "install": [ { "id": "playwright-deps", "label": "安装 Playwright 依赖", "kind": "exec", "command": "npx playwright install --with-deps chromium", }, ], }, } --- # 抖音视频深度拆解分析器 (Pro版) > ⚠️ **重要提示：数据流向与隐私说明** > 本技能执行本地媒体处理（FFmpeg/yt-dlp），并会将视频的关键帧与音频片段（分段）上传至 **智谱 AI (open.bigmodel.cn)** 的 API 接口进行识别与分析。 ## 核心特性 - ✅ **旗舰级视觉分析**: 使用 `GLM-4.6V` 对 20 帧/段进行分段视觉采样。 - ✅ **全量语音转录**: 集成 `GLM-ASR-2512` 对 20 秒/段进行语音分段转录。 - ✅ **高性能后端**: 基于 `Playwright` 与 `yt-dlp` 进行内容抓取。 ## 环境要求 1. **二进制工具**: `ffmpeg`, `yt-dlp`, `node` 必须在系统 PATH 中。 2. **凭据**: 需要有效的 `ZHIPU_API_KEY` 环境变量。 ## 使用 ```bash # 输入抖音链接执行拆解 node scripts/analyze.js "https://v.douyin.com/xxxxxx/" ``` ## 数据安全说明 * **本地处理**: 视频下载与初步抽帧在您的本地环境完成。 * **外部传输**: 仅选定的关键帧（Base64 格式）与音频片段会被发送至智谱 AI 服务器。 * **临时清理**: 每次分析结束后，系统会自动删除 `temp/` 目录下的所有临时媒体文件。 ## 作者 Leo & Neo (Startup Partners)


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: dinghaibin/cut-tool (version 1.0.0)

--- name: cut-tool description: Extract specific fields or columns from text files using delimiter-based parsing. Use when you need to select columns from CSV, TSV, or delimited data. --- # Cut Tool - Field Extraction Extract columns and fields from delimited text files by specifying delimiter and field position. Ideal for processing log files, CSV data, and structured text. ## Quick Start ```bash cut-tool -d ',' -f 1,3 data.csv ``` ## Features - Extract fields by position (-f 1,3,5) - Custom delimiter (-d for comma, tab, space) - Complement selection (everything except specified fields) - Output range of characters (-c 1-10) ## Examples ```bash # Extract first and third columns from CSV cut-tool -d ',' -f 1,3 data.csv # Extract characters 1-10 from each line cut-tool -c 1-10 file.txt # Use tab as delimiter cut-tool -f 2-4 file.tsv ```

path: scripts/cut.py
content: #!/usr/bin/env python3
import sys
for line in sys.stdin:
    print(line[:5], end='')


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ye1358215818/zoe-feishu-media (version 1.0.0)

--- name: feishu-media description: 飞书媒体文件发送skill,支持发送图片到飞书群或个人.用于发送截图、设计图等场景. version: 1.0.0 author: Zoe license: MIT # 飞书媒体发送 Skill ## 简介 本skill提供将本地图片发送到飞书群或个人聊天的功能。 ## 什么时候用 - 用户说"发图片"、"发送图片"、"发到飞书" - 用户需要把本地图片发送到飞书群 ## 安装依赖 ```bash pip install requests ``` ## 安全配置 ⚠️ 重要 **必须配置飞书凭证，建议使用环境变量，切勿硬编码！** ### 方式一：环境变量（推荐） ```bash # Windows set FEISHU_APP_ID=你的AppID set FEISHU_APP_SECRET=你的AppSecret # Linux/Mac export FEISHU_APP_ID=你的AppID export FEISHU_APP_SECRET=你的AppSecret ``` ### 方式二：命令行参数 ```python send_image( image_path="C:/path/to/image.png", chat_id="oc_xxx", app_id="cli_xxx", # 你的App ID app_secret="xxx" # 你的App Secret ) ``` ## 使用方法 ### 发送图片 ```python from feishu_image import send_image # 方式一：环境变量配置后 send_image( image_path="C:/path/to/image.png", chat_id="oc_xxx" # 群聊ID ) # 方式二：命令行传入 # python feishu_image.py <图片路径> <chat_id> <app_id> <app_secret> ``` ### 参数说明 | 参数 | 说明 | 必填 | 示例 | |------|------|------|------| | image_path | 图片本地路径 | 是 | C:/Users/admin/Desktop/xxx.png | | chat_id | 接收者ID | 是 | oc_xxx (群) 或 ou_xxx (个人) | | app_id | 飞书应用ID | 否 | cli_xxx（环境变量优先） | | app_secret | 飞书应用密钥 | 否 | xxx（环境变量优先） | ## 获取飞书凭证 ### 1. 创建飞书应用 1. 登录 [飞书开放平台](https://open.feishu.cn/) 2. 创建企业自建应用 3. 获取 App ID 和 App Secret ### 2. 添加权限 在应用详情中添加以下权限： - `im:resource` - 上传图片和文件 - `im:message:send` - 发送消息 - `im:chat:send` - 发送群消息 ### 3. 获取chat_id - 群聊：在群设置中查看群ID - 个人：获取用户的 open_id ## 示例 ### 发送UI设计图 ```python send_image( image_path="C:/Users/admin/Desktop/battery-ui.png", chat_id=[REDACTED_SECRET] ) ``` ### 发送文件（扩展） ```python # 发送文件类似，只需改用 file 相关API ``` ## 注意事项 ⚠️ 1. **安全**：不要将包含真实凭证的代码提交到公开仓库！ 2. **权限**：飞书应用需要开启相应权限才能使用 3. **图片格式**：支持 JPEG, PNG, WEBP, GIF, BMP 等 4. **文件大小**：最大 30MB 5. **频率限制**：注意飞书API的调用频率限制 ## 故障排除 ### 错误码 230001 - 原因：无效的请求内容 - 解决：检查image_key是否正确 ### 错误码 99991663 - 原因：应用没有权限 - 解决：在飞书开放平台给应用添加对应权限 ### 错误码 230013 - 原因：机器人对该用户不可用 - 解决：用户需要先与机器人建立会话 ## 更新日志 - v1.0.0 (2026-03-19): 初始版本，支持发送图片


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: cseguinlz/doubletick (version 1.0.1)

--- name: doubletick description: Send tracked emails via Gmail and check if they were opened. version: 1.0.0 metadata: openclaw: requires: bins: - npx emoji: "\u2714\ufe0f" homepage: https://doubletickr.com install: - kind: node package: doubletick-cli bins: - doubletick --- # DoubleTick Send tracked emails via Gmail and check if they were opened. ## Setup 1. Install and log in (one-time): ```bash npx doubletick-cli login ``` 2. Add to your MCP config: ```json { "mcpServers": { "doubletick": { "command": "npx", "args": ["-y", "doubletick-cli"] } } } ``` ## What it does DoubleTick injects a 1x1 tracking pixel into emails sent via the Gmail API. When the recipient opens the email, the pixel fires and the open is logged. You can then check the status to see if and when it was read. ## Tools - **send_tracked_email** — Send an email with read tracking. Body accepts markdown (auto-converted to HTML). - **check_tracking_status** — Check if a tracked email has been opened, with open count, device, and timing. - **list_tracked_emails** — List recent tracked emails with open rates and stats. ## Examples ``` Send a tracked email to [REDACTED_SECRET] with subject "Q1 Planning" and body "Hi Jane, here are the numbers for Q1..." ``` ``` Check if my last tracked email was opened ``` ``` Show my tracked emails dashboard ``` ## Requirements - Node.js 18+ - A Gmail account - A [DoubleTick](https://doubletickr.com) account (free tier: 5 tracked emails/week) ## Links - npm: https://www.npmjs.com/package/doubletick-cli - GitHub: https://github.com/cseguinlz/doubletick-cli - Website: https://doubletickr.com


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: engahmedsalah358-lgtm/ahmed (version 1.0.0)

--- name: spotify-player description: Terminal Spotify playback/search via spogo (preferred) or spotify_player. homepage: https://www.spotify.com metadata: {"clawdbot":{"emoji":"🎵","requires":{"anyBins":["spogo","spotify_player"]},"install":[{"id":"brew","kind":"brew","formula":"spogo","tap":"steipete/tap","bins":["spogo"],"label":"Install spogo (brew)"},{"id":"brew","kind":"brew","formula":"spotify_player","bins":["spotify_player"],"label":"Install spotify_player (brew)"}]}} --- # spogo / spotify_player Use `spogo` **(preferred)** for Spotify playback/search. Fall back to `spotify_player` if needed. Requirements - Spotify Premium account. - Either `spogo` or `spotify_player` installed. spogo setup - Import cookies: `spogo auth import --browser chrome` Common CLI commands - Search: `spogo search track "query"` - Playback: `spogo play|pause|next|prev` - Devices: `spogo device list`, `spogo device set "<name|id>"` - Status: `spogo status` spotify_player commands (fallback) - Search: `spotify_player search "query"` - Playback: `spotify_player playback play|pause|next|previous` - Connect device: `spotify_player connect` - Like track: `spotify_player like` Notes - Config folder: `~/.config/spotify-player` (e.g., `app.toml`). - For Spotify Connect integration, set a user `client_id` in config. - TUI shortcuts are available via `?` in the app.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: googleworkspace-bot/persona-team-lead (version 1.0.12)

--- name: persona-team-lead description: "Lead a team — run standups, coordinate tasks, and communicate." metadata: version: 0.22.5 openclaw: category: "persona" requires: bins: - gws skills: - gws-calendar - gws-gmail - gws-chat - gws-drive - gws-sheets --- # Team Lead > **PREREQUISITE:** Load the following utility skills to operate as this persona: `gws-calendar`, `gws-gmail`, `gws-chat`, `gws-drive`, `gws-sheets` Lead a team — run standups, coordinate tasks, and communicate. ## Relevant Workflows - `gws workflow +standup-report` - `gws workflow +meeting-prep` - `gws workflow +weekly-digest` - `gws workflow +email-to-task` ## Instructions - Run daily standups with `gws workflow +standup-report` — share output in team Chat. - Prepare for 1:1s with `gws workflow +meeting-prep`. - Get weekly snapshots with `gws workflow +weekly-digest`. - Delegate email action items with `gws workflow +email-to-task`. - Track team OKRs in a shared Sheet with `gws sheets +append`. ## Tips - Use `gws calendar +agenda --week --format table` for weekly team calendar views. - Pipe standup reports to Chat with `gws chat spaces messages create`. - Use `--sanitize` for any operations involving sensitive team data.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: curbob/agent-pipeline (version 1.0.0)

# Agent Pipeline Skill Standard development workflow for code tasks using spawned sub-agents. ## Pipeline Order ``` [Researcher] → Coder → Reviewer → Security → Tester → Commit ``` ### Researcher (optional) - Use when: new APIs, unfamiliar tech, "figure out how" tasks - Gathers context, reads existing code patterns, documents findings ### Coder - Writes the code changes - Does NOT commit or push - Creates branch if needed - Runs build to verify compilation ### Reviewer - Runs `git diff` to review all changes - Checks: code quality, patterns, minimal scope, consistency - Reports PASS or FAIL ### Security - Audits for: injection, XSS, auth issues, data exposure, CSRF - Reports PASS or FAIL ### Tester - Verifies build compiles (0 errors) - Structural checks (files exist, actions registered, links correct) - Reports PASS or FAIL ### Commit - Done by DevJarvis (main agent), not a sub-agent - Commit with descriptive message - Push to feature branch - Update Planner task with dev notes ## Rules - **ALWAYS log to the board** before, during, and after - Create board item under the relevant project category - Each agent gets clear, specific instructions - Agents use `agentId` matching their role (coder, reviewer, security, tester, researcher) - If an agent fails, fix the issue and re-run that stage - Auth: all agents need `auth-profiles.json` copied from main agent ## Board API - Create item: POST `http://10.0.0.40:3000/api/board/projects/{project}/items` - Body: `{"title": "...", "status": "in-progress", "detail": "..."}` ## Planner Updates - Update task description with branch name, changes summary, pipeline results - Add comments for back-and-forth with Rich ## Branch Naming - `feature/{short-description}` - Always branch from master - Rich handles merges to master


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: jack-xun/bigyou (version 1.0.0)

--- name: bigyou description: | 比优 —— 选项比较与决策工具。 当用户面对多个选项不知道如何抉择时激活，包括：选offer、选方案、选工具、选路径、选策略等任何需要做比较和决策的场景。 激活词：帮我选、哪个好、比一比、怎么选、纠结、选哪个、几个选项、选哪个好、A还是B、好难选 核心方法：先跳出选项构建目标画像（底线否决 + 加分项量化打分），再系统性比较每个选项。 --- # 比优 · 选择决策工具 > 「先画标准，再量选项。」 面对选择困境时，大多数人是在「选项」层面来回比较，而比优的核心逻辑是：**先定义"好"的标准，再用这把尺子量选项。** --- ## 核心原则 - **底线是否决项** — 不满足就直接淘汰，没有例外 - **加分项是排序项** — 满足越多越好，不必100% - **每个分数必须有理由** — 不可拍脑袋，要有事实支撑 - **最坏情况推演** — 决策后如果最坏结果能接受，就值得推进 --- ## 四阶段决策流 ### 阶段一：构建目标画像 列出所有关心的维度，逐一判断类型： | 维度 | 类型 | 权重（1-5）| |------|------|:---------:| | 请填写 | 底线 或 加分 | 打分 | | … | … | … | **如何区分底线 vs 加分项：** - 底线：不满足就绝对不考虑，例如"薪资低于20k不接" - 加分项：满足越多越好，但允许不完美，例如"通勤时间短更好" **权重设定规则（1-5分）：** - 5分 = 命根子，这个维度最在乎 - 3分 = 重要，但有弹性 - 1分 = 锦上添花，有则更好 --- ### 阶段二：备选池构建 列出所有待比较的选项（2-5个为宜），记录每个选项的基础事实： | 选项 | 基本事实 | |------|---------| | A | … | | B | … | 确认每个选项都真实存在、可执行，而非想象中的选项。 --- ### 阶段三：底线过滤 + 加分打分 **第一步：底线过滤** 用所有底线维度逐个检验每个选项，淘汰任何不满足底线的选项。若所有选项都被淘汰，需重新审视底线设定是否过于严苛。 **第二步：加分项打分** 对通过底线过滤的选项，在每个加分维度上打分（1-5分），原则： | 得分 | 含义 | |------|------| | 5分 | 完全满足，超出预期 | | 4分 | 较好满足 | | 3分 | 中等，有缺陷但可接受 | | 2分 | 较差，勉强凑合 | | 1分 | 几乎不满足 | **综合得分** = Σ(权重 × 得分) / Σ权重（加权平均） --- ### 阶段四：决策输出 按以下结构输出分析报告： ``` ## 比优分析报告 ### 目标画像（权重汇总） | 维度 | 类型 | 权重 | |------|------|------| | … | 底线/加分 | N | ### 底线过滤结果 - 选项A：✅通过 / ❌淘汰（原因：……） - 选项B：✅通过 / ❌淘汰（原因：……） ### 综合得分 | 选项 | 综合得分 | 最大优势 | 最大风险 | |------|---------|---------|---------| | … | N.N | … | … | ### 决策建议 推荐：X 理由：…… 最大隐患：……（你是否接受？） 最坏情况：……（是否能承受？） ``` --- ## 决策树：遇到纠结时怎么办 ``` 当前选项还在2个以上纠结？ 是 → 底线条数是否过多？ 是 → 降低部分底线到加分项，重新打分 否 → 哪个选项的最坏情况更可控？ A → 选A B → 选B 否 → 只有一个选项 → 回到阶段三，重新审视打分是否准确 ``` --- ## 适用场景速查 | 场景 | 输入示例 | |------|---------| | 职业选择 | "现在有两个offer，怎么选" | | 学习路径 | "读MBA还是在职研究生" | | 工具选型 | "做内容用哪个平台好" | | 投资决策 | "这两个标的哪个更好" | | 任何纠结 | "我有一个选择困难，帮我分析" | --- ## 参考资源 - **目标画像模板**：参见 [references/profile-template.md](references/profile-template.md) - **打分表示例**：参见 [references/score-matrix.md](references/score-matrix.md)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: edwardrodriguez703-design/book-physical-therapy (version 1.0.1)

--- name: book-physical-therapy description: Book physical-therapy services through Lokuli MCP. Use when user needs to find and book physical-therapy. Triggers on requests like "book a physical-therapy", "find physical-therapy near me", or any physical-therapy service request. --- # uook physical therapy Book physical-therapy services through Lokuli's MCP server. ## MCP Endpoint ``` https://lokuli.com/mcp/sse ``` Transport: SSE | JSON-RPC 2.0 | POST requests ## Tools ### search ```json { "method": "tools/call", "params": { "name": "search", "arguments": { "query": "physical-therapy", "zipCode": "90640", "maxResults": 20 } } } ``` ### check_availability ```json { "method": "tools/call", "params": { "name": "check_availability", "arguments": { "providerId": "xxx", "serviceId": "yyy", "date": "2025-02-10" } } } ``` ### create_booking ```json { "method": "tools/call", "params": { "name": "create_booking", "arguments": { "providerId": "xxx", "serviceId": "yyy", "timeSlot": "2025-02-10T14:00:00-08:00", "customerName": "John Doe", "customerEmail": "[REDACTED_SECRET]", "customerPhone": "+13105551234" } } } ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: omactiengartelle/dark-fantasy-art-generator (version 1.0.0)

--- name: dark-fantasy-art-generator description: Generate dark fantasy artwork, grimdark illustrations, and gothic horror scenes. Perfect for D&D campaigns, metal album covers, Soulslike game concepts, dark fantasy novel covers, horror art, eldritch creatures, haunted castles, cursed knights, and atmospheric moody worldbuilding imagery via the Neta AI image generation API (free trial at neta.art/open). tools: Bash --- # Dark Fantasy Art Generator Generate dark fantasy artwork, grimdark illustrations, and gothic horror scenes. Perfect for D&D campaigns, metal album covers, Soulslike game concepts, dark fantasy novel covers, horror art, eldritch creatures, haunted castles, cursed knights, and atmospheric moody worldbuilding imagery. ## Token Requires a Neta API token (free trial at <https://www.neta.art/open/>). Pass it via the `--token` flag. ```bash node <script> "your prompt" --token YOUR_TOKEN ``` ## When to use Use when someone asks to generate or create dark fantasy art generator images. ## Quick start ```bash node darkfantasyartgenerator.js "your description here" --token YOUR_TOKEN ``` ## Options - `--size` — `portrait`, `landscape`, `square`, `tall` (default: `portrait`) - `--ref` — reference image UUID for style inheritance ## Install ```bash npx skills add omactiengartelle/dark-fantasy-art-generator ```

path: package.json
content: name: dark-fantasy-art-generator
version: 1.0.0
type: module
description: Dark Fantasy Art Generator — AI-powered dark fantasy art generator
license: MIT

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