OpenClaw/clawhub-security-signals huggingface dataset:
Skill: 1477009639zw-blip/authorized-session-scrape (version 1.0.0)

--- name: authorized-session-scrape description: Continue searching and extracting within a user-authorized local browser session after the user logs in. Use for pagination, site search, tab-by-tab extraction, and post-login discovery without bypassing access controls. --- # Authorized Session Scrape Use this skill when the user has legitimate access and the work should continue inside their own logged-in browser session. ## Hard Rule Do not bypass login or session controls. This skill begins only after: - the user confirms they want to proceed - the login flow is opened locally - the user completes sign-in themselves ## Best Use Cases - account-only pages - post-login search or filtering - paginated dashboards or content libraries - collections where one page is not enough - workflows where plain fetch misses the authenticated state ## Workflow ### 1. Open the user session - use the local browser path - navigate to the target area - if needed, prompt the user to finish login in the browser ### 2. Verify real access Before scraping deeply, confirm: - account home or target page is visible - search box, filters, or result list are actually present - content is not still hidden behind a modal or loading shell ### 3. Expand within the site Once logged in: - use the site's own search - apply filters, sorting, and date ranges when helpful - open multiple relevant items or tabs - continue through pagination until results become repetitive or low-value ### 4. Extract systematically Track internally: - which sections were searched - what filters were applied - which pages or tabs produced useful signal - where the session still limits access ### 5. Summarize with provenance Distinguish: - facts seen in public pages - facts seen only after authenticated login - what still requires manual action by the user ## Output Pattern Return: 1. where you searched inside the logged-in session 2. what filters or navigation paths were used 3. what the strongest results were 4. what remains partial or unavailable 5. what next click path you would use if continuing


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

--- name: mathgraphs version: 1.0.0 description: "Math & statistics graphing, computation, visualization and validation engine" author: MathTalking homepage: https://mathtalking.com mcp_servers: - url: https://mathtalking.com/api/mcp transport: streamable-http tags: - math - graphing - statistics - visualization - education - plot - geometry --- # Math & Statistics Graphing Engine You have access to an interactive math and statistics graphing engine via MCP. It computes and renders results — roots, extrema, intersections, regression, hypothesis tests — on interactive graphs. ## When to use this skill - User asks to **graph**, **plot**, or **visualize** any math - User needs to **verify** a mathematical result visually - You computed an answer and want to **show** it, not just describe it - Data needs statistical visualization (histogram, regression, distribution fit) - Geometry needs precise rendering (triangles, circles, constructions) ## Tools ### `plot_graph` — Math Visualization Plot functions, points, segments, labels, and shapes. Auto-computes roots, extrema, and intersections. Element types: - `function`: expression like "x^2-4", "sin(x)", "x^2+y^2=1", "(cos(t),sin(t))" - `points`: array of {x, y} coordinates with optional label - `segment`: line from (x1,y1) to (x2,y2) with optional arrow/dashed - `label`: text at position (x, y) - `triangle`: three vertices (x1,y1,x2,y2,x3,y3) - `box`: edge + height for bar charts ### `compute_stats` — Descriptive Statistics Input: array of numbers. Returns mean, median, std, min, max, quartiles. ### `add_histogram` — Histogram Input: array of numbers. Auto-bins and draws bars. ### `add_regression` — Regression Input: array of {x,y} points. Fits linear/quadratic/exponential/power. Returns R². ### `fit_distribution` — Distribution Fitting Input: array of numbers. Fits normal/uniform/exponential. Returns best fit. ### `test_hypothesis` — Hypothesis Test Input: data groups + test type. Returns p-value with visual rejection region. ## Important - All tools return an **interactive URL** — always share it with the user - The graph is **live**: user can zoom, pan, add functions, adjust sliders - Results are **computed from the graph**, not generated — no hallucinated curves - Supports 9 languages: en, zh, zh-TW, ja, ko, es, fr, de, pt-BR


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: savior1987/cs-uapipro-hot-zhihu (version 1.0.0)

--- name: cs-uapipro-hot-zhihu description: | 抓取知乎热榜，使用 UAPIPRO API（https://uapis.cn）。当用户询问"知乎热榜"、"知乎热搜"、"知乎热门"时触发。 需要环境变量 UAPIPRO_API_KEY。 --- # 知乎热榜 - UAPIPRO API 通过 UAPIPRO API 获取知乎热榜数据。 ## 快速使用 直接通过 exec 调用脚本： ```bash # 获取完整热榜 python3 scripts/zhihu_hot.py # 获取前10条 python3 scripts/zhihu_hot.py 10 # JSON格式输出 python3 scripts/zhihu_hot.py --json python3 scripts/zhihu_hot.py 10 --json ``` ## API 信息 - **接口**: `GET https://uapis.cn/api/v1/misc/hotboard?type=zhihu` - **认证**: `Authorization: Bearer <UAPIPRO_API_KEY>` - **返回**: 知乎热榜列表，每条包含 index、title、url、hot_value、extra(可选 desc/image/label) - **更新**: 约几分钟一次 ## 脚本用法 ```bash python3 zhihu_hot.py [N] [--json|-j] ``` - `N` - 返回前N条（不传则返回全部，约50条） - `--json` - 以 JSON 格式输出（用于程序调用） ## 返回字段说明 | 字段 | 说明 | |------|------| | index | 排名序号 | | title | 问题标题 | | url | 问题链接 | | hot_value | 热度值，如"634 万热度" | | extra.desc | 问题描述/摘要 | | extra.image | 配图URL（相对路径） | | extra.label | 标签，如"新" | ## Python 调用示例 ```python import subprocess import json import os # 调用脚本获取JSON输出 result = subprocess.run( ["python3", "scripts/zhihu_hot.py", "10", "--json"], capture_output=True, text=True, env={**os.environ, "UAPIPRO_API_KEY": os.environ.get("UAPIPRO_API_KEY", "")} ) data = json.loads(result.stdout) for item in data["list"]: print(item["index"], item["title"]) ``` ## 老大输出格式偏好 标题：XXX（热度：XXX） 原文链接：XXX 每次回复知乎热榜时统一使用此格式。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: sky-lv/skylv-schedule-interpreter (version 1.0.0)

--- name: skylv-cron-parser slug: skylv-cron-parser version: 1.0.0 description: "Parses, validates, and explains cron expressions. Converts between human-readable and cron format. Triggers: parse cron, validate cron, cron expression." author: SKY-lv license: MIT tags: [automation, tools] keywords: [automation, tools] triggers: cron-parser --- # Cron Expression Parser ## Overview Parses, validates, and explains cron expressions. ## Cron Format * * * * * | | | | | | | | | day of week | | | month | | day of month | hour minute ## Common Patterns * * * * * Every minute 0 * * * * Every hour 0 0 * * * Daily at midnight 0 9 * * 1-5 Weekdays at 9 AM */5 * * * * Every 5 minutes 0 0 1 * * First day of month ## Popular Crontab @yearly = 0 0 1 1 * @monthly = 0 0 1 * * @weekly = 0 0 * * 0 @daily = 0 0 * * * @hourly = 0 * * * * ## Validation Minute: 0-59, Hour: 0-23, Day: 1-31, Month: 1-12, Weekday: 0-6 Use - for ranges (1-5), , for lists (1,3,5), / for steps (*/5)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: leowing/umeng-app-analysis (version 1.0.0)

--- name: umeng-app-analysis description: 友盟+ App 数据分析工具，通过友盟 Open API 查询移动应用统计数据。支持全部应用统计（App列表、数量、汇总数据）和单个应用详细分析（活跃/新增用户、启动次数、留存率、使用时长、渠道/版本维度、自定义事件及参数分析）。当用户需要查询友盟App统计数据、分析应用指标、获取用户行为数据时使用。认证信息从环境变量 UMENG_API_KEY 和 UMENG_API_SECURITY 读取。 --- # 友盟+ App 分析 ## 环境变量要求 | 变量名 | 用途 | |---|---| | `UMENG_API_KEY` | 接口鉴权 key（无默认值） | | `UMENG_API_SECURITY` | 接口鉴权密钥（无默认值） | ```bash pip install requests ``` ## 执行方式 所有 API 通过 `scripts/umeng.py` 调用，输出 JSON： ```bash # 将 SKILL_DIR 替换为 skill 的实际安装路径 SKILL_DIR=/path/to/umeng-app-analysis python3 $SKILL_DIR/scripts/umeng.py <command> [参数] ``` **输出约定**：成功返回 JSON 对象；失败 exit 1，并输出 `{"error": "..."}` 结构。 ## 快速上手 ```bash # 全部 App 汇总 python3 umeng.py get-all-app-data # 单个 App 昨日数据 python3 umeng.py get-yesterday-data --appkey 123456 # 新增用户时间段统计 python3 umeng.py get-new-users --appkey 123456 --start-date 2024-01-01 --end-date 2024-01-31 ``` 完整命令列表见 [references/commands.md](references/commands.md)。 ## Gotchas - **`--appkey` 是整数**，不能加引号传字符串，否则签名校验失败 - **`get-app-list` 需要 access_token**，其他接口不需要；忘传会导致接口报权限错误 - **`--channels` / `--versions` 是逗号分隔字符串**（如 `"渠道A,渠道B"`），由 API 侧解析，不是数组 - **`--event-id` 和 `--event-name` 是不同参数**：`get-event-param-list` 用 `--event-id`（数字），其他事件接口用 `--event-name`（字符串） - **`--period-type` 在渠道/版本过滤接口中是必填**，在基础指标接口中是可选 - 游戏账号接口（`get-new-accounts` / `get-active-accounts`）**仅对游戏类型 App 有效**


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: zhihang9978/teamgram-biz-service (version 1.0.0)

--- name: teamgram-biz-service description: Documents the biz_service core business layer in Teamgram Server with 6 RPC helpers covering user, chat, dialog, message, updates, and verification code management. compatibility: Documentation/knowledge skill only. No executable code. Reference material for Teamgram Server developers. metadata: author: zhihang9978 version: "1.0.0" source: https://github.com/teamgram/teamgram-server homepage: https://github.com/teamgram/teamgram-server openclaw: requires: env: [] bins: [] securityNotes: | Documentation-only skill. Contains no executable code, no network calls, no credential handling. Config snippets show localhost-only addresses from the default development setup. All content references the open-source teamgram-server project (Apache-2.0). --- # 业务逻辑层：biz_service（6 个 helper） ## 概述 biz_service 是一个"组合服务"，在一个 gRPC server 中注册 6 个子服务： - **RPCChat** — 群组/频道管理 - **RPCCode** — 验证码管理 - **RPCDialog** — 对话管理 - **RPCMessage** — 消息构造/权限校验/写DB - **RPCUpdates** — Updates 构造 - **RPCUser** — 用户读取/搜索 ## etcd 注册 它们都以 `service.biz_service` 注册到 etcd，供 BFF/msg/sync 等发现调用。 ## 典型调用关系 | 调用方 | 目标 helper | 场景 | |---|---|---| | BFF.messages | biz_service.message | 构造消息/权限校验/写DB | | BFF.users | biz_service.user | 用户读取/搜索 | | BFF.contacts | biz_service.user + biz_service.dialog | 联系人与对话 | | BFF.chats | biz_service.chat | 群/频道管理 | | BFF.updates | biz_service.updates | updates 构造 | | BFF.authorization | biz_service.code + biz_service.user | 登录注册流程 | ## 配置 ```yaml Name: service.biz_service ListenOn: 127.0.0.1:20500 Etcd: Key: service.biz_service Hosts: - 127.0.0.1:2379 Mysql: DSN: root:@tcp(127.0.0.1:3306)/teamgram?charset=utf8mb4&parseTime=true Cache: - Host: 127.0.0.1:6379 ``` ## 6 个 helper 详解 ### RPCUser - 用户注册、查询、搜索 - 手机号→用户映射 - 用户资料读写（名字、头像、简介等） - 在线状态查询 - 被调用最频繁的 helper，几乎所有 BFF 模块都依赖 ### RPCChat - 创建/删除群组 - 群成员管理（添加/删除/设管理员） - 群标题/头像/权限设置 - 群迁移到超级群 - 群禁言/封禁权限管理 ### RPCDialog - 对话列表查询（分页、筛选） - 对话置顶/归档/标记未读 - 对话未读数管理（read_inbox_max_id / unread_count） - 会话 TTL 设置 - 对话文件夹管理 ### RPCMessage - 消息构造与入库（inbox/outbox 双写模型） - 消息权限校验（是否被禁言、是否被屏蔽等） - 消息 ID 生成（依赖 idgen 服务的 Snowflake ID） - 消息编辑/删除/转发 - 消息搜索（全文、hashtag） ### RPCUpdates - Updates 序列号管理（pts/qts/seq） - 差异更新构造（getDifference） - 状态查询（getState） - 确保客户端与服务端状态同步 ### RPCCode - 验证码生成与校验 - 短信/App code 管理 - 验证码过期与重试逻辑 - 支持自定义验证码发送插件（VerifyCodeInterface） ## 关键代码路径 - 主服务入口：`app/service/biz/` - 各 helper 实现：`app/service/biz/internal/core/` - DAO 层：`app/service/biz/internal/dao/` - 配置文件：`teamgramd/etc/biz_service.yaml` ## Source Code References - Repository: https://github.com/teamgram/teamgram-server (Apache-2.0)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: alesys/audio-mastering-cli (version 1.0.2)

--- name: audio-mastering-cli description: CLI audio mastering without a reference track using ffmpeg; accepts audio or video inputs and outputs mastered WAV/MP3 or remuxed MP4. metadata: {"openclaw":{"emoji":"🎚️","homepage":"https://github.com/alesys/openclaw-skill-audio-mastering-cli","os":["win32"],"requires":{"bins":["ffmpeg","powershell"]}}} --- # Audio Mastering CLI Usa este skill cuando el usuario quiera masterizar audio sin referencia, por CLI, sobre archivo de audio o video. ## Entradas soportadas - Audio: `wav`, `aiff`, `flac`, `mp3`, `m4a` - Video: `mp4`, `mov`, `m4v`, `mkv`, `webm` ## Flujo 1. Verifica que existe el archivo de entrada. 2. Ejecuta: `powershell -ExecutionPolicy Bypass -File "{baseDir}/scripts/master_media.ps1" -InputFile "<ruta-archivo>" -MakeMp3` 3. Entrega salidas: - WAV master: `<base>_master.wav` - MP3 master: `<base>_master.mp3` (si `-MakeMp3`) - Si la entrada es video: `<base>_master.mp4` con video original + audio masterizado AAC 320k 4. Reporta loudness/true peak del log. ## Cadena aplicada - `highpass` + `lowpass` - EQ suave de correccion/mejora - `acompressor` - `alimiter` - `loudnorm` dos pasadas (objetivo conservador multiplaforma) ## Verificacion opcional `ffmpeg -hide_banner -i "<archivo_master.wav>" -af "loudnorm=I=-14:TP=-1:LRA=7:print_format=summary" -f null NUL` ## Notas - Para video: se conserva el stream de video (`-c:v copy`) y solo se reemplaza audio. - Si hay avisos de clipping interno en EQ, bajar input o ganancias de EQ y reexportar.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: iyeque/iyeque-local-system-info (version 1.1.0)

--- name: local-system-info description: Return system metrics (CPU, RAM, disk, processes) using psutil. metadata: { "openclaw": { "emoji": "🖥️", "requires": { "bins": ["python3"], "pip": ["psutil"] }, "install": [ { "id": "psutil", "kind": "pip", "package": "psutil", "label": "Install psutil", }, ], "version": "1.1.0", }, } --- # Local System Info Skill Monitor local system resources including CPU, memory, disk usage, and running processes. ## Tool API ### system_info Retrieve system metrics. - **Parameters:** - `action` (string, required): One of `summary`, `cpu`, `memory`, `disk`, `processes`. - `limit` (integer, optional): Number of processes to list (default: 20). Only used with `action=processes`. **Usage:** ```bash # Get full system summary uv run --with psutil skills/local-system-info/sysinfo.py summary # CPU metrics only uv run --with psutil skills/local-system-info/sysinfo.py cpu # Memory metrics only uv run --with psutil skills/local-system-info/sysinfo.py memory # Disk usage uv run --with psutil skills/local-system-info/sysinfo.py disk # List top processes by CPU usage uv run --with psutil skills/local-system-info/sysinfo.py processes --limit 10 ``` ## Output Format ### summary ```json { "cpu": { "cpu_percent": 15.2, "cpu_count": 8, "load_avg": [0.5, 0.3, 0.2] }, "memory": { "total": 17179869184, "available": 8589934592, "percent": 50.0, "swap_percent": 5.2 }, "disk": { "total": 500000000000, "used": 250000000000, "free": 250000000000, "percent": 50.0 } } ``` ### processes ```json [ { "pid": 1234, "name": "python3", "username": "user", "cpu_percent": 5.2, "memory_percent": 2.1 }, ... ] ``` ## Metrics Explained - **cpu_percent:** Current CPU utilization (0-100%) - **cpu_count:** Number of logical CPU cores - **load_avg:** System load average (1, 5, 15 min) normalized by CPU count - **memory.total/available:** RAM in bytes - **memory.percent:** RAM usage percentage - **disk.percent:** Root filesystem usage percentage - **processes:** Top N processes sorted by CPU usage ## Requirements - **psutil:** Cross-platform system monitoring library - **Python 3.6+:** For f-string support and typing ## Platform Support Works on Linux, macOS, Windows, and WSL. Some metrics may vary by platform: - `load_avg`: Not available on Windows - Process information depth varies by OS


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: fankcoder/email-quote-automation (version 1.0.0)

--- name: medal-email-quote description:奖牌定制电商邮件自动处理技能，定时收取客户询价邮件，自动翻译非中文邮件，根据报价公式和材质参数表格生成报价回复模板并保存到本地。Use when需要处理奖牌定制询价邮件、定时收邮件、自动翻译和生成报价回复。 --- # 奖牌定制询价邮件自动处理技能 ## 概述 本技能用于奖牌定制企业电商渠道的邮件询价自动化处理： 1. **定时收取邮件** - 从邮箱收取新的客户询价邮件 2. **自动翻译** - 非中文邮件自动翻译为中文 3. **保存归档** - 原始邮件和翻译内容保存到本地 4. **生成报价** - 根据报价公式和材质参数表自动计算价格生成回复模板 ## 核心功能 ### 1. 邮件收取 - 支持IMAP协议连接邮箱服务器 - 支持SSL/TLS加密连接 - 可配置定时检查间隔（默认每30分钟检查一次） - 只处理未读邮件，标记已处理 ### 2. 自动翻译 - 检测邮件内容语言 - 非中文内容调用翻译API翻译为简体中文 - 保存原文和译文两份文件 ### 3. 本地存储结构 ``` email_storage/ ├── raw/ # 原始邮件（.eml格式） ├── text/ # 提取文本内容 ├── translated/ # 翻译后的中文内容 └── quotes/ # 生成的报价回复模板 ``` ### 4. 报价计算 - 根据材质、尺寸、数量、工艺计算价格 - 应用阶梯折扣政策 - 自动生成专业报价回复邮件模板 ## 配置说明 在使用前需要编辑 `scripts/config.py` 设置： - 邮箱IMAP服务器地址、端口、用户名、密码 - 翻译API配置（可选使用百度翻译、Google翻译等） - 本地存储路径 - 报价数据文件路径 ## 使用方法 ### 手动运行一次检查 ```bash python scripts/email_check.py ``` ### 启动定时服务 ```bash python scripts/email_daemon.py ``` ## 报价规则参考 报价数据文件已内置在技能中: - 材质参数表：`shopdata/材质报价参数信息表.xlsx` - 报价公式：`shopdata/报价公式.txt` ### 核心计算公式 **最终价格 = 基础单价 × 尺寸系数 + 工艺附加费** 然后根据订单数量应用阶梯折扣： - 1-5个：原价 - 6-20个：9.5折 - 21-100个：8.5折 - 101个以上：7折起 ### 尺寸系数 - S (15cm以下): 0.8 - M (15-25cm): 1.0 - L (25-40cm): 1.5 - XL (40cm以上): 单独核价 ### 材质基础单价 | 材质系列 | 零售单价 | 批量单价 | |---------|---------|---------| | 金箔/实木类 | ¥85 起 | ¥45 起 | | 水晶/琉璃类 | ¥120 起 | ¥65 起 | | 金属开模类 | ¥180 起 | ¥90 起 (需模具费) | | 亚克力/复合类 | ¥55 起 | ¥28 起 | ## 脚本说明 ### scripts/ - `config.py` - 配置文件（需要用户配置） - `email_check.py` - 单次检查新邮件 - `email_daemon.py` - 定时后台服务 - `email_utils.py` - 邮件处理工具函数 - `translator.py` - 翻译模块 - `quote_calculator.py` - 报价计算模块 ### references/ - `pricing_data.json` - 缓存的报价参数数据 - `email_template.html` - 回复邮件HTML模板

path: requirements.txt
content: pandas>=1.3.0
langdetect>=1.0.9
googletrans==4.0.0-rc1


path: scripts/__init__.py
content: # Email Quote Automation package


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: kirkraman/martin-ai-agent-helper (version 1.0.0)

--- name: ai-agent-helper description: AI Agent 設定同優化助手 - Prompt Engineering、Task Decomposition、Agent Loop設計 version: 1.0.0 tags: - ai - agent - prompt - automation - productivity --- # AI Agent Helper 幫你setup同優化AI Agents既技能。 ## 功能 - 📝 Prompt Engineering - 整高質量system prompts - 🔄 Task Decomposition - 將複雜任務拆解 - ⚙️ Agent Loop設計 - ReAct/ReAct/Chain-of-Thought - 🎯 Tool Selection - 最佳化agent既tool usage ## 使用場景 "帮我整prompt" / "點樣set AI agent" / "優化agent response" ## 技術 - System Prompt優化 - Few-shot examples - Output parsing (JSON/structured) - Error handling patterns - Token優化 ## 範例 ```python # Good prompt structure system = """你係{role}。 目標：{goal} 限制：{constraints} Output格式：{format}""" ``` ## SkillBoss API Hub 整合 在 agent loop 中调用 LLM，通过 SkillBoss API Hub 统一接口自动路由最优模型： ```python import requests, os SKILLBOSS_API_KEY = os.environ["SKILLBOSS_API_KEY"] def call_llm(messages: list) -> str: r = requests.post( "https://api.skillbossai.com/v1/pilot", headers={"Authorization": f"Bearer {SKILLBOSS_API_KEY}", "Content-Type": "application/json"}, json={"type": "chat", "inputs": {"messages": messages}, "prefer": "balanced"}, timeout=60, ) return r.json()["result"]["choices"][0]["message"]["content"] ``` **requires.env:** `SKILLBOSS_API_KEY`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: modestyrichards/modesty-generate-customer-support-ad-creative-brief (version 1.0.0)

--- name: "Generate Customer Support Ad Creative Brief" description: "Plan campaign visuals and hooks for customer support promotions. Use when working on paid campaign planning for support teams, customer success managers." homepage: "https://www.skillboss.co/skills/generate-customer-support-ad-creative-brief" metadata: {"category":"customer-support","subcategory":"support-ops","skill_id":[REDACTED_SECRET],"generated_day":"","seo_keywords":["ad creative brief AI","customer support ad planning","customer support AI","support workflow automation"]} --- # Generate Customer Support Ad Creative Brief Plan campaign visuals and hooks for customer support promotions. Use when working on paid campaign planning for support teams, customer success managers. ## Use When Use this skill for paid campaign planning work for support teams, customer success managers. ## Workflow 1. Clarify the audience, business goal, constraints, and deliverable. 2. Produce the strongest first draft for the target use case. 3. Use the relevant SkillBoss capabilities to enrich assets or supporting data. 4. Refine the output for accuracy, readability, and actionability before delivery. ## SEO / GEO - Primary keywords: ad creative brief AI, customer support ad planning, customer support AI, support workflow automation - Search intent: paid campaign planning - Canonical slug: `generate-customer-support-ad-creative-brief` - Install query: Install Generate Customer Support Ad Creative Brief with SkillBoss ## APIs Used - `chat` - `image_generation` ## Suggested Prompt [Generate Customer Support Ad Creative Brief] for my team ## Notes - Plan campaign visuals and hooks for customer support promotions - Review outputs before external publication or automated execution. ## SEO Keywords - ad creative brief AI - customer support ad planning - customer support AI - support workflow automation


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: sky-lv/skylv-task-planner (version 1.0.0)

--- name: task-planner description: Task planning assistant. Breaks down complex tasks into executable steps with timeline and progress tracking. Triggers: task planning, project breakdown, task decomposition. metadata: {"openclaw": {"emoji": "📋"}} --- # Task Planner — 任务规划助手 ## 功能说明 将复杂任务分解为可执行的步骤，制定合理的执行计划。 ## 使用方法 ### 1. 任务分解 ``` 用户: 帮我分解"开发一个用户认证系统"这个任务 ``` 输出： 1. 需求分析 2. 数据库设计 3. API设计 4. 核心功能实现 5. 测试 6. 部署 每个子任务再细分... ### 2. 制定计划 ``` 用户: 为以下任务制定执行计划： 1. 设计数据库 2. 开发API 3. 前端集成 4. 测试上线 ``` 输出： - 任务顺序 - 预计耗时 - 依赖关系 - 里程碑节点 ### 3. 工作量估算 ``` 用户: 估算开发一个博客系统需要多少时间 ``` 输出： - 功能模块拆分 - 每个模块工时 - 总工时估算 - 风险缓冲 ### 4. 进度跟踪 ``` 用户: 跟踪项目进度，当前已完成： - 数据库设计 ✓ - API开发 (进行中 60%) - 前端开发 (未开始) ``` 输出： - 整体进度百分比 - 预计完成时间 - 延期风险提示 - 下一步建议 ## 示例输出 ``` 任务规划：开发用户认证系统 阶段一：设计 (预计 2 天) ├── 1.1 需求梳理 [2h] ├── 1.2 数据库设计 [4h] │ ├── 用户表 │ ├── 会话表 │ └── 权限表 ├── 1.3 API接口设计 [4h] └── 1.4 技术选型 [2h] 阶段二：开发 (预计 5 天) ├── 2.1 注册功能 [1天] ├── 2.2 登录功能 [1天] ├── 2.3 密码重置 [0.5天] ├── 2.4 会话管理 [1天] └── 2.5 权限控制 [1.5天] 阶段三：测试 (预计 2 天) ├── 3.1 单元测试 [1天] └── 3.2 集成测试 [1天] 阶段四：部署 (预计 0.5 天) └── 4.1 生产环境部署 总计：9.5 天 依赖关系：阶段二依赖阶段一，阶段三依赖阶段二 关键路径：设计 → 开发 → 测试 → 部署 ``` ## 模板支持 - 软件开发项目 - 产品发布计划 - 学习计划 - 活动筹备


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