OpenClaw/clawhub-security-signals huggingface dataset:
Skill: whitezhiiii/pixel-desktop-pet (version 11.0.0)

--- name: desktop-pet description: Deploy a pixel-art desktop pet (桌面宠物) with four explorable scenes, care mechanics, and walk animations. Use when user asks to create a desktop pet, virtual pet, pixel pet, tamagotchi-like app, or says "桌面宠物", "桌宠", "desktop pet". Requires Python 3.11+ with Tkinter and Pillow. --- # Desktop Pet — 南波万の小家园 A pixel-art desktop pet built with Python + Tkinter. Features a mini floating character on the desktop and a full home-garden mode with four explorable scenes. ## Features - **Mini mode**: Transparent floating pixel character with walk animation (128px, white oval background) - **Garden mode**: 850×560 window with four scenes connected by doorways - 🏠 Indoor (1F) — cozy room with furniture - 🪜 Upstairs (2F) — mirrored warm-tone layout - 🌿 Outdoor — front yard with fence - 🌳 Forest — large woodland area - **Care system**: Hunger / Mood / Cleanliness / Health with decay and recovery - **Controls**: Arrow keys or WASD to walk, E to interact with doorways, T for debug mode - **Right-click menu**: Feed 🍔 / Bathe 🛁 / Play 🎮 / Medicine 💊 ## Deployment 1. Copy `scripts/desktop_pet.py` to target directory 2. Copy all files from `assets/` into an `assets/` subdirectory next to `desktop_pet.py` 3. Run: ```bash cd <target-dir> pip install Pillow TK_SILENCE_DEPRECATION=1 python3 desktop_pet.py ``` ### File structure after deployment ``` target-dir/ ├── desktop_pet.py └── assets/ ├── room_bg.png # Indoor scene (1F) ├── upstairs_bg.png # Upstairs scene (2F, mirrored + warm) ├── exterior_bg.png # Outdoor scene ├── forest_bg.png # Forest scene ├── walk_right.gif # Character walk right (8 frames, 32×32) └── walk_left.gif # Character walk left (8 frames, 32×32) ``` ### macOS notes - Uses `overrideredirect(True)` + `systemTransparent` for floating mini window - PhotoImage doesn't render on transparent canvas → white oval workaround - Set `TK_SILENCE_DEPRECATION=1` to suppress Tk warnings ## Scene transitions | From | Trigger | To | |---|---|---| | Indoor 1F | Walk to staircase (top-left) + E | Upstairs 2F | | Indoor 1F | Walk to carpet (bottom-right) + E | Outdoor | | Upstairs 2F | Walk to staircase (top-right) + E | Indoor 1F | | Outdoor | Walk to left edge + E | Forest | | Outdoor | Walk to door + E | Indoor 1F | | Forest | Walk to right edge + E | Outdoor | ## Customization - **Backgrounds**: Replace PNG files in `assets/` (850×526px recommended) - **Character**: Replace GIF files (32×32 per frame, 8 frames) - **Care decay rate**: Search `frame%600` in code (higher = slower decay) - **Collision walls**: Edit `_INDOOR_WALLS` / `_OUTDOOR_WALLS` arrays - **Window sizes**: `W_M, H_M` for mini mode, `W_G, H_G` for garden mode ## Credits - Character sprites: [Cozy People](https://pixelfight.itch.io/) (free pixel art) - Background tiles: [CraftPix](https://craftpix.net/freebies/) (free top-down pixel art)


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: cindypapa/seedream-ppt-maker (version 1.1.0)

--- name: seedream-ppt-maker description: Seedream图片PPT制作器 - 宝玉布局框架 + Seedream 5.0文生图，自动生成图片PPT。支持交互式分步确认流程，减少反复修改节省API配额。 version: 1.1.0 metadata: openclaw: homepage: https://github.com/Cindypapa/baoyu-seedream-ppt requires: skills: - baoyu-infographic bins: - python3 --- # seedream-ppt-maker - Seedream图片PPT制作器 基于宝玉布局框架 + Seedream 5.0，一键生成图片PPT。支持 **交互式分步确认流程**，减少反复修改，节省API配额。 --- ## ⚠️ 安装后必须执行 ```bash # 检查依赖和 API Key 配置 python3 check_install.py # 如果缺少依赖： clawhub install baoyu-infographic pip install python-pptx requests # 如果缺少 API Key： # 编辑 ~/.openclaw/config.json，添加： # {"volces": {"apiKey": "你的火山方舟API Key"}} ``` --- ## 🎯 触发条件 用户提到以下关键词时触发： - "生成PPT" - "图片PPT" - "Seedream PPT" - "用 seedream-ppt-maker" --- ## 🔄 收到请求后先确认 **⚠️ 重要：收到用户请求后，必须先询问生成模式！** ``` 用户：帮我生成XX的PPT 助手回复： 收到！请选择生成模式： 1️⃣ **交互式分步生成**（推荐） - 分析内容 → 推荐3种布局×风格组合 → 你确认 - 生成提示词 → 你确认 → 开始生成 - 适合重要内容，减少反复修改，节省API配额 2️⃣ **一键快速生成** - 直接生成，无需确认 - 适合简单内容或已有明确布局/风格需求 请回复 "1" 或 "2"，或直接告诉我布局和风格（如：bento-grid + chalkboard） ``` --- ## 📋 交互式流程（推荐） ### 步骤 1：分析内容，推荐方案 收到用户选择交互式后： ```bash python3 interactive.py content.md --step analyze ``` 输出推荐方案，等待用户确认。 ### 步骤 2：生成提示词 用户确认方案后： ```bash python3 interactive.py content.md --step prompts --layout 布局 --style 风格 ``` 展示提示词预览，等待用户确认。 ### 步骤 3：生成图片并整合PPT 用户确认提示词后： ```bash python3 interactive.py --step generate --config output/项目名/config.json ``` --- ## 🚀 一键快速生成 用户选择一键生成或已有明确布局/风格时： ```bash python3 baoyu_seedream_ppt.py content.md --layout 布局 --style 风格 --aspect landscape ``` --- ## 🎨 布局 × 风格推荐 ### 国风/传统文化内容 | 布局 | 风格 | 说明 | |------|------|------| | linear-progression | aged-academia | 时间线 + 复古 sepia 古朴 | | bento-grid | morandi-journal | 多主题 + 莫兰迪手绘温和 | | dense-modules | craft-handmade | 高密度 + 手绘纸艺 | ### 科技/产品介绍 | 布局 | 风格 | 说明 | |------|------|------| | bento-grid | chalkboard | 多主题 + 黑板风清爽 | | dashboard | technical-schematic | 数据展示 + 工程蓝图 | ### 对比/评测 | 布局 | 风格 | 说明 | |------|------|------| | binary-comparison | chalkboard | A vs B + 黑板风清晰 | | comparison-matrix | bold-graphic | 多因素对比 + 漫画粗线 | --- ## ⚙️ 配置 火山方舟 API Key： ```json // ~/.openclaw/config.json { "volces": { "apiKey": "你的API Key" } } ``` --- ## 📦 输出 ``` output/项目名/ ├── config.json # 项目配置 ├── prompts/ # 每页提示词 ├── images/ # 生成的 PNG 图片 └── 项目名_布局_风格.pptx # 最终 PPT ``` --- ## 🔗 链接 - GitHub: https://github.com/Cindypapa/baoyu-seedream-ppt - 宝玉布局系统: https://github.com/JimLiu/baoyu-skills - 火山方舟: https://www.volcengine.com


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: warm-wm/volcengine-video-generate (version 1.0.0)

--- name: volcengine-video-generate description: Using volcengine video_generate.py script to generate video, need to provide filename and prompt, optional provide first frame image (URL or local path). license: Complete terms in LICENSE.txt --- # Volcengine Video Generate ## Scenarios When you need to generate a video based on a text description, use this skill. It supports controlling the starting frame of the video using a first frame image (either a URL or a local file path). ## Steps 1. Prepare the target filename (e.g., `output.mp4`) and a clear, specific `prompt`. 2. (Optional) Prepare the first frame image, which can be a HTTP URL or a local file path (the script will automatically convert it to Base64). 3. Run the script `python scripts/video_generate.py <filename> "<prompt>" [first_frame]`. Before running, navigate to the corresponding directory. 4. The script will output the video URL to the console and automatically download the video to the specified file. ## Authentication and Credentials - First, it will try to read the `MODEL_VIDEO_API_KEY` or `ARK_API_KEY` environment variables. - If not configured, it will try to use `VOLCENGINE_ACCESS_KEY` and `VOLCENGINE_SECRET_KEY` to get the Ark API Key. ## Output Format - The console will output the generated video URL. - The video file will be downloaded to the specified path. ## Examples **Pure Text Generation:** ```bash python scripts/video_generate.py "cat.mp4" "a lovely cat" ``` **With First Frame Image (URL):** ```bash python scripts/video_generate.py "dog_run.mp4" "a lovely dog is running on the grass" "https://example.com/dog_start.png" ``` **带首帧图片生成（本地文件）：** ```bash python scripts/video_generate.py "my_video.mp4" "a person is running in the street" "/path/to/local/image.jpg" ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: leooooooow/creator-payment-terms-template (version 1.0.0)

--- name: creator-payment-terms-template description: Draft clear creator payment terms and payment expectation templates before campaign launch. Use when teams need to reduce payout disputes and awkward payment conversations. --- # Creator Payment Terms Template ## Skill Card - **Category:** Creator Operations - **Core problem:** Payment ambiguity causes friction, disputes, and delays in creator campaigns. - **Best for:** Teams standardizing creator payout expectations before launch. - **Expected input:** Collaboration model, deliverables, acceptance rules, payment timing, extra terms. - **Expected output:** Payment structure summary + risk points + copy-ready terms. ## Workflow 1. Identify payment model. 2. Tie payment timing to acceptance milestones. 3. Flag ambiguous or risky clauses. 4. Produce a creator-facing payment terms template. ## Output format 1. Payment structure summary 2. Risk points 3. Copy-ready payment terms 4. Short creator-facing explanation ## Quality and safety rules - Keep language clear and practical. - Reduce disputes, do not replace legal contracts. - Link money terms to delivery and acceptance. ## License Copyright (c) 2026 **Razestar**. This skill is provided under **CC BY-NC-SA 4.0** for non-commercial use. You may reuse and adapt it with attribution to Razestar, and share derivatives under the same license. Commercial use requires a separate paid commercial license from **Razestar**. No trademark rights are granted.


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

--- name: dingtalk-cli description: 当用户提到钉钉知识库、钉钉文档、读取/写入文档、知识库目录、文档成员、`.axls` 表格、workbook、dingtalk doc、wiki workspace 时使用。通过本地 `dingtalk-cli` 命令调用钉钉开放平台 API，适合 agent 直接执行。 --- # dingtalk-cli `dingtalk-cli` 是面向 agent 的钉钉文档 CLI，覆盖： - 知识库列表与详情 - 节点查询、URL 反查 - 文档创建、正文读取、Markdown 覆盖写入、删除 - `.axls` 钉钉表格的 workbook/sheet/range 读取 - 文档成员添加、更新、移除 ## 安装 ```bash pip install dingtalk-cli ``` 如需从源码开发安装： ```bash pip install -e . ``` ## 启动前配置 优先使用命令保存配置： ```bash dingtalk-cli auth setup \ --app-key <APP_KEY> \ --app-secret <APP_SECRET> \ --operator-union-id <UNION_ID> ``` 如果只有 `userId`： ```bash dingtalk-cli auth setup \ --app-key <APP_KEY> \ --app-secret <APP_SECRET> \ --operator-user-id <USER_ID> ``` 配置会写到 `~/.dingtalk-cli/config.json`。也可用环境变量覆盖： - `DINGTALK_APP_KEY` - `DINGTALK_APP_SECRET` - `DINGTALK_OPERATOR_ID` - `DINGTALK_CLI_CONFIG_DIR` 注意： - 写操作必须带真实用户身份 `operatorId`，且应为 `unionId` - 输出中不会打印完整凭证，只显示脱敏值 ## 常用命令 ```bash # 看知识库 dingtalk-cli workspace list --all # 用 URL 反查节点 dingtalk-cli node resolve-url "https://alidocs.dingtalk.com/i/nodes/xxx" # 读取文档正文 dingtalk-cli doc read --url "https://alidocs.dingtalk.com/i/nodes/xxx" # 覆盖写入文档 dingtalk-cli doc overwrite --doc-key <DOC_KEY> --content-file /abs/path/content.md --yes # 若立即删除刚创建的文档，优先使用 create 返回的 workspace_id + node_id dingtalk-cli doc delete --workspace-id <WORKSPACE_ID> --node-id <NODE_ID> --yes # 读取 .axls 表格 dingtalk-cli workbook read --node-id <NODE_ID> --range A1:Z80 # 添加成员 dingtalk-cli member add --node-id <NODE_ID> --member-id <USER_ID> --role editor ``` ## Agent 使用约定 - 优先使用 `--json` - 对破坏性命令显式传 `--yes` - 读取普通文档用 `doc` - 读取 `.axls` 用 `workbook` - 若 `doc read` 返回“目标节点是 `.axls`”，不要重试同一命令，直接切到 `workbook` - 对创建命令返回的结果，立即读写时优先使用 `doc_key` - 对创建后立即删除的场景，优先使用返回的 `workspace_id + node_id` ## 错误提示 - `MissingoperatorId`：未配置 operator unionId - `paramError`：把 userId 当成 unionId 传了 - `Forbidden.AccessDenied.AccessTokenPermissionDenied`：应用权限不够 - `Target document should be doc.`：目标不是普通文档，通常应改走 `workbook`

path: __init__.py
content: __all__ = []


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: dwhite-oss/fly-io (version 1.0.0)

--- name: fly-io description: Deploy and manage applications on Fly.io using the flyctl CLI and Machines API. Use when asked to deploy an app, scale machines, check app status, view logs, manage secrets, create/destroy machines, set environment variables, run commands in a container, or manage Fly Postgres databases. Requires flyctl installed and authenticated via `fly auth login`. --- # Fly.io Skill Fly.io is managed via the `flyctl` CLI (alias: `fly`) and the Machines REST API. ## Auth ```bash fly auth login # opens browser fly auth token # print current token export FLY_API_TOKEN=$(fly auth token) ``` ## App Management ```bash fly apps list # list all apps fly status -a <app-name> # app health + machine states fly info -a <app-name> # app details, IPs, regions fly open -a <app-name> # open in browser ``` ## Deploy ```bash fly deploy # deploy from current dir (uses fly.toml) fly deploy --image registry/image:tag # deploy a specific image fly deploy --remote-only # build remotely (no local Docker needed) fly deploy -a <app-name> # target specific app ``` ## Logs ```bash fly logs -a <app-name> # live log stream fly logs -a <app-name> --no-tail # recent logs, no follow ``` ## Secrets ```bash fly secrets set MY_KEY=value -a <app-name> fly secrets list -a <app-name> fly secrets unset MY_KEY -a <app-name> ``` ## Scaling ```bash fly scale count 3 -a <app-name> # set machine count fly scale memory 512 -a <app-name> # set RAM (MB) fly scale vm shared-cpu-2x -a <app-name> # change VM size fly scale show -a <app-name> # current scale ``` ## Machines ```bash fly machine list -a <app-name> fly machine status <machine-id> -a <app-name> fly machine restart <machine-id> -a <app-name> fly machine stop <machine-id> -a <app-name> fly machine destroy <machine-id> -a <app-name> ``` ## Run a Command (one-off) ```bash fly ssh console -a <app-name> # interactive shell fly ssh console -a <app-name> -C "ls -la /app" # run single command ``` ## Postgres ```bash fly postgres create --name myapp-db # create Postgres cluster fly postgres connect -a myapp-db # psql shell fly postgres attach myapp-db -a <app-name> # attach DB to app (sets DATABASE_URL) ``` ## VM Sizes `shared-cpu-1x` (256MB), `shared-cpu-2x` (512MB), `performance-1x` (2GB), `performance-2x` (4GB) ## Regions `iad` (Virginia), `ord` (Chicago), `lax` (LA), `sea` (Seattle), `ams` (Amsterdam), `fra` (Frankfurt), `sin` (Singapore), `syd` (Sydney) ## Tips - `fly.toml` is the app config — always check it before deploying - `fly deploy --strategy rolling` for zero-downtime deploys - Health checks in `fly.toml` under `[checks]` block


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: mupengi-bot/decision-log (version 1.0.1)

--- name: decision-log description: Decision recording + result tracking skill author: 무펭이 🐧 --- # decision-log Skill for recording important decisions and automatically tracking results after 30 days. ## Features - Record decision content, rationale, alternatives, expected results - Auto-review results after 30 days (cron integration) - File save: `memory/decisions/YYYY-MM-DD-{slug}.md` ## Usage Trigger keywords: - "record decision" - "decision log" - "made this decision" - "why did I do this" Example: ``` Record decision: Decided to separate Instagram bot account Rationale: Distribute main account ban risk Alternatives: Use main account, manual operation Expected results: Increased safety, increased management complexity ``` ## Output Format ```markdown # Decision: {title} **Date**: YYYY-MM-DD **Status**: Decided / Review Pending / Results Confirmed ## Decision Content ... ## Rationale - ... - ... ## Alternatives Considered 1. **Alternative 1**: ... - Pros: ... - Cons: ... 2. **Alternative 2**: ... ## Expected Results - Positive: ... - Negative: ... ## Actual Results (Auto-update after 30 days) _Review date: YYYY-MM-DD_ --- **Decision date**: YYYY-MM-DD | **Review date**: YYYY-MM-DD (scheduled) ``` ## Auto-review (cron) After 30 days, automatically: 1. Compare expected vs actual results 2. Extract lessons learned 3. Generate insights for similar future decisions ## Event Bus Integration Publish event when recording decision: - Path: `events/decision-YYYY-MM-DD.json` - Format: ```json { "type": "decision-logged", "timestamp": "2026-02-14T12:00:00Z", "title": "Decision title", "reviewDate": "2026-03-16", "filePath": "memory/decisions/2026-02-14-slug.md" } ``` --- **decision-log** | 무펭이 🐧


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: aaureli/air-train-ev (version 0.1.0)

--- name: air-train-ev description: Unified travel + mobility skill: (1) flight pricing with Amadeus (flight offers), (2) public transport/train journey planning with Navitia (journeys, departures), and (3) find nearby EV charge points using Open Charge Map. Use when Alessandro asks for flight prices, train itineraries/schedules, or EV charging stations. --- # Air + Train + EV ## Credentials (env) Do not hardcode keys in scripts. ### Flights (Amadeus) - `AMADEUS_CLIENT_ID` - `AMADEUS_CLIENT_SECRET` - Optional: `AMADEUS_HOST` (default `https://api.amadeus.com`) ### Trains / public transport (Navitia) - `NAVITIA_TOKEN` - Optional: `NAVITIA_HOST` (default `https://api.navitia.io`) - Optional: `NAVITIA_COVERAGE` (default `sandbox`) ### EV charge points (Open Charge Map) - `OPENCHARGEMAP_API_KEY` - Optional: `OPENCHARGEMAP_HOST` (default `https://api.openchargemap.io`) ## Flights — quick usage ```bash python3 skills/air-train-ev/scripts/flight_offers.py \ --origin ZRH --destination IST \ --departure 2026-03-10 \ --adults 1 --travel-class ECO \ --non-stop true \ --included-airlines PC,VF,TK \ --max 6 ``` Output formatting is fixed: - Dates/times: `DD/MM/YY HH:MM` - EUR prices use `€` ## Train journeys — quick usage ```bash python3 skills/air-train-ev/scripts/navitia.py coverage python3 skills/air-train-ev/scripts/navitia.py places --q "Strasbourg" python3 skills/air-train-ev/scripts/navitia.py journeys --from "Strasbourg" --to "Rennes" --datetime "2026-03-07T08:00:00" --count 5 ``` ## EV charge points — quick usage ```bash python3 skills/air-train-ev/scripts/ev_charge_points.py \ --lat 48.5839 --lon 7.7455 \ --km 5 --max 10 ``` Notes: - This uses Open Charge Map `GET /v3/poi/`. - Returned results include operator/title, address, distance (when available), connector types, and coordinates.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: bytesagain1/quant-finance (version 2.0.3)

--- name: "quant-finance" version: "2.0.3" description: "Reference tool for devtools — covers intro, formulas, regulations and more. Quick lookup for Quant Finance concepts, best practices, and implementation patte..." author: "BytesAgain" homepage: "https://bytesagain.com" source: "https://github.com/bytesagain/ai-skills" tags: [quant,finance, reference] category: "devtools" --- # Quant Finance Reference tool for devtools — covers intro, formulas, regulations and more. Quick lookup for Quant Finance concepts, best practices, and implementation patte... No API keys or credentials required. ## Commands | Command | Description | |---------|-------------| | `intro` | intro reference | | `formulas` | formulas reference | | `regulations` | regulations reference | | `risks` | risks reference | | `instruments` | instruments reference | | `strategies` | strategies reference | | `glossary` | glossary reference | | `checklist` | checklist reference | ## Output Format All commands output plain-text reference documentation via heredoc. No external API calls, no credentials needed, no network access. --- *Powered by BytesAgain | bytesagain.com | [REDACTED_SECRET]*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ericlooi504/csv-data-processor (version 1.0.0)

--- name: csv-data-processor description: CSV and delimited data processing toolkit for transforming, filtering, merging, validating, and converting data files. Use when the user wants to: (1) View, filter, or sort CSV files, (2) Merge or join multiple CSV files, (3) Convert between CSV, JSON, and SQL, (4) Clean and validate CSV data (remove duplicates, fix encoding), (5) Extract columns or apply transformations, (6) Generate statistics and summaries, (7) Handle TSV, pipe-delimited, or custom delimiter files. --- # CSV Data Processor Transform, filter, clean, and convert delimited data files. No external dependencies — uses Python csv/JSON stdlib only. ## Quick Start ```bash # View first 10 rows python3 skills/csv-data-processor/scripts/csv_view.py data.csv # Filter rows python3 skills/csv-data-processor/scripts/csv_filter.py data.csv --where "age > 25" # Convert to JSON python3 skills/csv-data-processor/scripts/csv_convert.py data.csv --to json ``` ## Common Commands ### Preview Data ```bash python3 skills/csv-data-processor/scripts/csv_view.py data.csv --head 10 --stats ``` ### Filter Rows ```bash python3 skills/csv-data-processor/scripts/csv_filter.py data.csv --where "price > 100" --sort price --limit 20 ``` ### Merge Files ```bash python3 skills/csv-data-processor/scripts/csv_merge.py sales_2024.csv sales_2025.csv --output combined.csv ``` ### Join on Column ```bash python3 skills/csv-data-processor/scripts/csv_join.py left.csv right.csv --on user_id --output joined.csv ``` ### Clean Data ```bash python3 skills/csv-data-processor/scripts/csv_clean.py dirty.csv --dedupe --fill-missing N/A --output clean.csv ``` ### Statistics ```bash python3 skills/csv-data-processor/scripts/csv_stats.py data.csv --numeric age,revenue ``` ## Scripts | Script | Purpose | |--------|---------| | `csv_view.py` | Preview, head/tail, summary stats | | `csv_filter.py` | Filter rows, sort, select columns | | `csv_merge.py` | Concatenate multiple CSV files | | `csv_join.py` | Join/merge on shared columns | | `csv_convert.py` | Convert CSV ↔ JSON ↔ SQL | | `csv_clean.py` | Dedupe, fill missing, fix encoding | | `csv_stats.py` | Numeric stats, value counts, histograms | ## Key Options All scripts accept: - `--delimiter` — Field delimiter (default: `,` for CSV, `\t` for TSV) - `--encoding` — File encoding (default: utf-8) - `--has-header` / `--no-header` — Column header handling - `--output` / `-o` — Output file path


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal:  | SkillSpector: suspicious (MEDIUM)
Skill: wangm-a3/global-customer-agent (version 1.0.4)

--- name: 跨境客户Agent slug: global-customer-agent description: 7×24小时36语种客户接待。自动理解多语种需求，智能推荐商品，生成多语种报价，让全球客户都能获得专业服务体验，适合有多语种客户的外贸企业和独立站运营。 tags: - 客户服务 - 多语种 - 客户接待 - 跨境电商 - 自动回复 --- # 跨境客户Agent ## 记忆点 **7×24小时36语种客户接待** ## 核心功能 ### 1. 多语种即时响应 - 36个语种自动识别和回复 - 专业术语准确翻译 - 本地化表达习惯 ### 2. 全天候客户接待 - 7×24小时不间断服务 - 毫秒级响应速度 - 夜间询盘零遗漏 ### 3. 智能需求理解 - 自动提取客户需求关键词 - 产品匹配推荐 - 库存状态实时查询 ### 4. 多语种报价生成 - 支持36语种报价单 - 自动汇率计算 - 规格参数本地化 ## 客户生命周期管理 | 阶段 | 核心诉求 | 运营策略 | |-----|---------|---------| | 潜在客户 | 了解产品 | 多渠道曝光 | | 询盘客户 | 快速响应 | 24h内专业回复 | | 报价客户 | 透明价格 | 详细方案+案例 | | 成交客户 | 信任建立 | 定期跟进 | | 复购客户 | 便捷体验 | 个性化推荐 | ## 语种支持 | 语种 | 区域 | 优先级 | |-----|------|-------| | 英语 | 全球通用 | P0 | | 西班牙语 | 拉美/西班牙 | P0 | | 法语 | 非洲/法国 | P0 | | 德语 | 欧洲 | P1 | | 葡萄牙语 | 巴西/葡萄牙 | P1 | | 俄语 | 俄语区 | P1 | | 阿拉伯语 | 中东 | P1 | | 日韩语 | 东亚 | P1 | | 其他28语种 | 东南亚/南亚 | P2 | ## 响应模板 ```markdown ## 询盘响应 [自动识别语种] Thank you for your inquiry about [产品]. We have [规格] in stock. Price: $XX/unit, MOQ: XX units. Delivery: XX days to [目的港]. ## 报价生成 [自动生成多语种报价单] - Product specs - Pricing (FOB/CIF/EXW) - Payment terms - Delivery timeline ``` ## 关键指标 - 响应时间：<1分钟 - 语种识别准确率：>95% - 询盘转化率：>15% - 客户满意度：>90%


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