OpenClaw/clawhub-security-signals huggingface dataset:
Skill: bavxhack/create-videoconference (version 1.0.1)

--- name: meetling-default description: Default video conferences via Meetling. Instant calls always use /m/ rooms + share payload for Claw default sending. Scheduled calls return link + email invite template. Secure contacts loading (no env-controlled file paths). version: 1.3.0 metadata: openclaw: requires: bins: - node env: - MEETLING_INSTANT_THRESHOLD_MINUTES - SKILL_LANGUAGE_DEFAULT --- # Meetling Default (Video Conferences) — Secure ## Hardcoded Meetling host This skill always uses: https://app.meetling.de No environment variable can change the base URL. ## Security: contacts loading - Contacts are loaded **only** from `./contacts.json` (current working directory). - No environment variable can override the file path. - The skill does **not** output the contacts file path. If `contacts.json` is missing or invalid JSON, the skill continues with an empty map and marks recipients as unresolved. ## Behavior ### Instant / Ad-hoc (always `/m/`) Triggers if ANY is true: - Text contains “now / right now / asap / immediately” - OR contains German equivalents “jetzt / jetzt gleich / sofort / gleich” - OR `start_time` is within `MEETLING_INSTANT_THRESHOLD_MINUTES` minutes from now - OR participants are present but no `start_time` is provided Returns: - `url`: https://app.meetling.de/m/<slug> - `share`: message + resolved recipients for your Claw default sending path ### Scheduled If `start_time` is more than the threshold minutes in the future: - returns `email_invite` (subject + body) plus the Meetling link Note: This skill does not automate Meetling dashboard creation or Meetling-sent invites (requires an official API or robust UI automation).


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

--- name: hex-tool description: Convert data between hexadecimal and other formats. Use for debugging binary data, examining file headers, and working with hex dumps. --- # Hex - Hexadecimal Conversion Tool Convert between hexadecimal, decimal, binary, and ASCII representations. Essential for low-level debugging, binary protocol analysis, and byte-level data inspection. ## Usage ```bash hex-tool [options] <value> ``` ## Operations - Encode text or numbers to hexadecimal - Decode hexadecimal back to readable text - Convert between hex, decimal, and binary bases ## Examples ```bash # Encode text to hex hex-tool --encode "Hello World" # Decode hex to text hex-tool --decode "48656c6c6f" # Convert decimal to hex hex-tool --from dec --to hex 255 ```

path: scripts/hex.py
content: #!/usr/bin/env python3
import sys
data = open(sys.argv[1], 'rb').read() if sys.argv[1:] else sys.stdin.buffer.read()
print(data.hex())


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: terrycarter1985/terry-nano-pdf (version 1.0.0)

--- name: nano-pdf description: PDF processing and manipulation version: 1.0.0 author: ClawdBot tags: [pdf, documents] requires_bins: [] requires_env: [] requires_config: [] feishu_acl: sensitivity: internal access: owner: full_access team_editors: edit team_viewers: view external: none review_cadence: 90d --- # Nano PDF PDF processing and manipulation ## Available Tools This skill uses ClawdBot's standard tools: - **bash** - Execute commands - **read_file** - Read files - **write_file** - Write files - **web_fetch** - Fetch web content - **web_search** - Search the web ## Usage Examples User: "Help me with nano pdf" 1. Assess what the user needs 2. Use appropriate tools 3. Provide helpful response ## Configuration Check documentation for specific setup requirements. ## Notes This skill requires integration with Nano PDF service/application.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: jason513597/ai-backendfrontend-autowriter (version 1.0.0)

# AI_BackendFrontend_Autowriter ## Purpose 前後端程式生成 ## Primary Agents Coder ## Notes 分工明確 ## Inputs - task: 要執行的任務描述 - context: 額外上下文（可選） - constraints: 限制條件（可選） ## Outputs - plan/result/report（依任務類型） - logs/summary ## Workflow (default) 1. Analyze task 2. Plan subtasks 3. Execute by role 4. Validate result 5. Return final summary ## Safety - 不執行破壞性操作，除非明確授權 - 外部動作（發送、部署到正式環境）需二次確認 - 記錄關鍵決策與錯誤


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: paperboardofficial/webserp (version 0.1.4)

--- name: webserp description: Web search across 7 engines in parallel with browser impersonation. Use when the agent needs current information from the web — news, documentation, recent events, or anything beyond training data. Returns structured JSON (SearXNG-compatible) with title, URL, and content. Uses curl_cffi to mimic real browser fingerprints so requests don't get blocked. Install with `pip install webserp`. No API keys needed. --- # webserp Metasearch CLI — queries Google, DuckDuckGo, Brave, Yahoo, Mojeek, Startpage, and Presearch in parallel. Uses curl_cffi for browser impersonation. Results like a browser, speed like an API. ## When to use webserp 1. You need current/recent information not in your training data 2. You need to verify facts or find sources 3. You need to discover URLs, documentation, or code repositories 4. The user asks about recent events, releases, or news ## Install ```bash pip install webserp ``` No API keys, no configuration. Just install and search. ## Usage ```bash # Search all 7 engines (default) webserp "how to deploy docker containers" # Search specific engines webserp "python async tutorial" --engines google,brave,duckduckgo # Limit results per engine webserp "rust vs go" --max-results 5 # Show which engines succeeded/failed webserp "test query" --verbose # Set per-engine timeout webserp "query" --timeout 15 # Use a proxy webserp "query" --proxy "socks5://127.0.0.1:1080" ``` ## Options | Flag | Description | Default | |------|-------------|---------| | `-e, --engines` | Comma-separated engine list | all | | `-n, --max-results` | Max results per engine | 10 | | `--timeout` | Per-engine timeout (seconds) | 10 | | `--proxy` | Proxy URL for all requests | none | | `--verbose` | Show engine status in stderr | false | ## Output format JSON to stdout (SearXNG-compatible): ```json { "query": "deployment issue", "number_of_results": 42, "results": [ { "title": "How to fix Docker deployment issues", "url": "https://example.com/docker-fix", "content": "Common Docker deployment problems and solutions...", "engine": "google" } ], "suggestions": [], "unresponsive_engines": [] } ``` Parse with `jq` or any JSON parser. The `results` array contains `title`, `url`, `content`, and `engine` for each result. `unresponsive_engines` lists any engines that failed with the error reason. ## Tips - Use `--max-results 5` to keep output concise when you just need a few links - Use `--engines google,brave` to target specific engines for faster results - Use `--verbose` (writes to stderr) to see which engines responded — the JSON on stdout is unaffected - Results are deduplicated by URL across engines — you won't get the same link twice


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: offlinecat-dev/ssh-agentd-control (version 0.1.0)

--- name: ssh-agentd-control description: 管理并使用本地 ssh-agentd（systemd 常驻 + API 调用 + 连通性验证）。当用户提到 ssh-agentd、持久 SSH 会话、/run /upload /tail_logs、开机自启、会话状态/指标排查时使用。 --- # ssh-agentd 控制技能 适用环境（当前）： - 二进制：`/home/krex/.openclaw/workspace-hermes/ssh-agentd/bin/ssh-agentd` - 配置：`/home/krex/.openclaw/ssh-agentd/hosts.yaml` - systemd：`ssh-agentd.service` - 默认监听：`127.0.0.1:18081` ## 快速检查 ```bash sudo systemctl is-enabled ssh-agentd.service sudo systemctl is-active ssh-agentd.service ss -ltnp | grep 18081 sudo systemctl status ssh-agentd.service --no-pager -l | sed -n '1,80p' ``` ## 启停与自启 ```bash sudo systemctl daemon-reload sudo systemctl enable --now ssh-agentd.service sudo systemctl restart ssh-agentd.service sudo systemctl stop ssh-agentd.service ``` ## API 调用要点 1) 默认使用 Bearer Token 鉴权（若配置启用） 2) 本机常有代理变量，调用本地 API 时必须绕过代理 推荐统一用脚本：`scripts/api.sh` ### 示例：运行远程命令 ```bash SSH_AGENTD_TOKEN='<token>' \ scripts/api.sh POST /run '{"host":"nas","cmd":"hostname && whoami","timeoutSec":10}' ``` ### 示例：查看会话与指标 ```bash SSH_AGENTD_TOKEN='<token>' scripts/api.sh GET /sessions SSH_AGENTD_TOKEN='<token>' scripts/api.sh GET /metrics SSH_AGENTD_TOKEN='<token>' scripts/api.sh GET /health ``` ## 常见故障 ### 1) 返回 401 unauthorized - 检查 `apiAuth.enabled/token` 配置 - 确认请求头 `Authorization: Bearer <token>` ### 2) 调本地 API 返回 502 - 原因：请求被系统代理劫持 - 处理：用 `--noproxy '*'` 或临时 unset 代理变量（脚本已内置） ### 3) run 失败且提示 host key/known_hosts - 原因：v1 已启用 HostKey 校验 - 处理：把目标主机 key 加入 `known_hosts` ## 安全约束 - 不要把真实 token/密码写进 skill 文件。 - 不要把 `hosts.yaml` 放进仓库。 - 修改服务配置后必须重启并验证 `status + /health + /sessions`。

path: scripts/api.sh
content: #!/usr/bin/env bash
set -euo pipefail

METHOD="${1:-GET}"
PATH_PART="${2:-/health}"
BODY="${3:-}"

BASE_URL="${SSH_AGENTD_URL:-http://127.0.0.1:18081}"
[REDACTED_SECRET]"

CURL=(env -u http_proxy -u https_proxy -u HTTP_PROXY -u HTTPS_PROXY -u ALL_PROXY -u all_proxy \
  curl --noproxy '*' -sS -X "$METHOD" "$BASE_URL$PATH_PART" \
  -H 'Content-Type: application/json')

if [[ -n "$TOKEN" ]]; then
  CURL+=( -H "Authorization: Bearer $TOKEN" )
fi

if [[ "$METHOD" == "POST" || "$METHOD" == "PUT" || "$METHOD" == "PATCH" ]]; then
  CURL+=( -d "$BODY" )
fi

"${CURL[@]}"


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: peand-rover/gymnastics-academy-video (version 1.0.0)

name: gymnastics-academy-video version: "1.0.0" displayName: "Gymnastics Academy Video — Member Recruitment and Athlete Showcase Videos for Gymnastics Academy Programs" description: > You are a video assistant for Gymnastics Academy gyms, clubs and fitness programs — helping coaches and facilities show their training environment, their athlete results, and the community they've built through short recruitment and showcase videos. Gymnastics Academy Video turns your coaching philosophy, your member transformations, and your competition highlights into clips that answer the question every prospective member asks: will this program push me to reach the fitness level I've been working toward? tags: - gymnastics academy-video


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: casperzinou/talonforge-memory (version 1.0.0)

# AI Memory System Skill ## Auto-setup for the 3-layer OpenClaw memory architecture ### What It Does Sets up the complete 3-layer memory system from the Zero-Human Company playbook: - MEMORY.md (tacit knowledge and preferences) - Daily notes (memory/YYYY-MM-DD.md) - Nightly extraction cron job ### Setup Instructions After installing, tell your AI: "Set up the memory system." Your AI will: 1. Create MEMORY.md with a template 2. Create the memory/ directory 3. Set up a nightly extraction cron at 11pm your timezone 4. Create today's first daily note entry ### MEMORY.md Template ```markdown # MEMORY.md — Long-Term Memory ## About [User] - Name: - Timezone: - Communication preferences: ## Working Style - (Observe and update) ## Key Decisions - (Log important decisions here) ## Lessons Learned - (Capture mistakes and insights) ## Hard Rules - (Non-negotiable boundaries) ## Action Log - YYYY-MM-DD — [Action taken] ``` ### Daily Note Template (memory/YYYY-MM-DD.md) ```markdown # YYYY-MM-DD ## Key Events - HH:MM — [What happened] ## Decisions Made - [Decision and reasoning] ## Facts Extracted - [Durable facts worth remembering] ## Pending - [Open items to follow up] ``` ### Nightly Extraction Cron ```bash openclaw cron add \ --name "nightly-extraction" \ --cron "0 23 * * *" \ --tz "[YOUR_TIMEZONE]" \ --session isolated \ --message "Review today's conversations. Extract durable facts (relationships, decisions, status changes, milestones). Skip small talk. Update memory/YYYY-MM-DD.md with timeline. Update MEMORY.md with new patterns or preferences." \ --announce ``` ### Memory Rules 1. If it matters, WRITE IT DOWN. Mental notes don't survive sessions. 2. Never delete facts — supersede them instead. 3. Update MEMORY.md when you notice new patterns about your user. 4. Daily notes are raw logs. MEMORY.md is curated wisdom. 5. Review and clean MEMORY.md weekly. ### Version 1.0 by TalonForge

path: package.json
content: {
  "name": "talonforge-memory",
  "version": "1.0.0",
  "description": "Bilingual (EN/AR) 3-layer memory architecture for AI-run companies. MEMORY.md, daily notes, extraction. From TalonForge AI CEO.",
  "author": "TalonForge",
  "license": "MIT",
  "homepage": "https://talonforge.xyz",
  "keywords": ["memory", "ai-company", "openclaw", "bilingual", "arabic"]
}


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: kienphan91/fortress-agent-suite (version 1.0.1)

# Fortress Agent Suite **Fortress** is a production-grade suite for OpenClaw agents, providing self-healing, system monitoring, and proactive security hardening. > ⚠️ **SECURITY ALERT - HIGH PRIVILEGE TOOL** > **This suite operates with root-level access and requires a trusted environment.** > It is designed for production Odoo/OpenClaw setups and performs autonomous system modifications (Crontab, Git, Gateway restarts, and auto-skill installation). **Do not install unless you own and trust the underlying workspace.** --- ## 🛠 Features - 🛡️ **Self-Healing**: Automatic gateway recovery and backup restoration. - 🩺 **Health Monitoring**: Disk, RAM, and system watchdog services. - ⚙️ **Automated Maintenance**: Auto-cron enforcement and Git-sync for workspace integrity. - 🤖 **Model Manager**: Autonomous management of LLM providers for 9Router. ## 🚀 Deployment Requirements 1. **Trusted Environment Only**: Only install in environments where you have full control and trust. 2. **Dependencies**: `pip install psutil`. 3. **Setup**: Apply `crontab_template.txt`. ## License MIT


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: guoming/self-operated-delivery-strategy-excel-group-export (version 1.0.0)

--- name: self-operated-delivery-strategy-excel-group-export description: 自营发货策略+分区方案配置拆分导出. 读取Excel文件，按照指定的列进行分组，将相同组的数据导出到新文件，文件命名用分组字段组合命名。 --- # 自营发货策略+分区方案配置拆分导出技能 ## 快速开始 使用此技能处理Excel文件分组导出： ### 基本用法 1. 准备Excel文件（.xlsx格式） 2. 指定分组列（如 渠道组,实重区间,周长区间） 3. 运行分组导出脚本 ### 脚本执行 ```bash python scripts/excel_export.py --input input.xlsx --columns "渠道组,实重区间,周长区间" --output-dir ./output/ ``` ## 详细说明 ### 功能特性 - 读取Excel文件，按照指定的列进行分组，将相同组的数据导出到新文件，文件命名用分组字段组合命名. 文件名格式：`{分组字段1}_{分组字段2}_{分组字段3}.xlsx` - 导出的文件需要添加 分区名称列，默认值为1 - 导出的文件需要添加 国家二字码列，默认值:US - 导出的文件需要添加 城市列，默认值为空 - 导出的文件需要添加 开始邮编列和结束邮编列，默认值: 邮编 - 导出的文件需要删除 原所有字段仅保留新增加的 分区名称, 国家二字码, 城市, 开始邮编, 结束邮编 字段 ### 使用示例 假设Excel文件有列：渠道组,实重区间,周长区间,邮编 - 分组列：渠道组,实重区间,周长区间 - 数据行1：渠道组=USPS, 实重区间=1, 周长区间=1, 邮编=100000 → 输出文件：USPS_1_1.xlsx - 数据行2：渠道组=USPS, 实重区间=1, 周长区间=2, 邮编=100000 → 输出文件：USPS_1_2.xlsx - 数据行3：渠道组=FEDEX, 实重区间=1, 周长区间=2, 邮编=100000 → 输出文件：FEDEX_1_2.xlsx ## 资源 - `scripts/excel_export.py`: 主要处理脚本


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: zack-dev-cm/agentic-video-production-publisher (version 1.0.3)

--- name: agentic-video-production-publisher description: Review AI video production packages for creative consistency, asset provenance, platform metadata, disclosure wording, and publishing readiness. Use when a user asks whether a video package is ready for YouTube, TikTok, or short-form release without uploading or scheduling content. --- # Agentic Video Production Reviewer Use this skill to review an AI video production package before a human-controlled publish step. The skill is instruction-only and should produce a readiness verdict, not operate accounts or upload media. ## Review Workflow 1. Confirm the target platform, audience, video file, title, description, thumbnail, captions, and disclosure requirements. 2. Check that character, setting, music, prompt, source-asset, and generated-asset provenance are documented. 3. Verify platform metadata for policy-sensitive claims, attribution, affiliate disclosure, copyright risk, and audience settings. 4. Confirm that private local paths, account names, unpublished prompts, and production notes are not present in client-facing copy. 5. Separate creative quality notes from release blockers. 6. Return a release verdict: `ready`, `ready_with_notes`, `blocked`, or `do_not_publish`. ## Boundaries - Do not upload, publish, schedule, delete, monetize, or modify any video. - Do not operate logged-in YouTube, TikTok, browser, or creator-studio sessions. - Do not request credentials, cookies, account recovery steps, billing access, or private analytics. - Do not expose internal production notes, local paths, private prompts, or unredacted source-asset links in client-facing output. ## Output Shape Return: - `Package`: files and metadata reviewed. - `Creative`: consistency and quality findings. - `Policy`: disclosure, copyright, platform, and audience risks. - `Public surface`: client-facing wording issues. - `Verification`: checks still needed before manual publish. - `Verdict`: one of `ready`, `ready_with_notes`, `blocked`, or `do_not_publish`.

path: agents/openai.yaml
content: interface:
  display_name: "Agentic Video Production Reviewer"
  short_description: "Review AI video packages before human-controlled release."
  default_prompt: "Use $agentic-video-production-publisher to review this AI video package for provenance, metadata, platform policy, public wording, and release readiness without uploading or publishing."


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: a85012712/jw-feishu-suite (version 1.0.0)

--- name: Feishu All-in-One description: 飞书全功能集成技能 - 消息、文档、日历、任务、多维表格一站式管理。适用于企业协作、工作流自动化、数据管理。觸發詞：飛書、文檔、日程、任務、多維表格、協作。 version: 1.0.0 author: 贾维斯 slug: feishu-all-in-one --- # Feishu All-in-One 飞书全功能集成技能，一站式管理飞书资源。 ## 功能模块 ### 1. 消息管理 - 发送文本消息 - 发送富文本消息 - 发送图片/文件 - 获取群聊列表 - 查询群成员 ### 2. 文档协作 - 创建云文档 - 获取文档内容 - 更新文档内容 - 文档评论管理 ### 3. 日历日程 - 创建日程 - 查询日程列表 - 更新日程 - 回复日程邀请 ### 4. 任务管理 - 创建任务 - 查询任务列表 - 更新任务状态 - 任务评论管理 ### 5. 多维表格 - 创建多维表格 - 添加记录 - 查询记录 - 更新记录 ## 使用方式 ### 发送消息 ``` 给 [姓名] 发一条飞书消息：[内容] ``` ### 创建日程 ``` 创建一个日程：[标题]，时间：[时间]，参与人：[姓名] ``` ### 创建任务 ``` 创建任务：[标题]，负责人：[姓名]，截止时间：[时间] ``` ### 文档操作 ``` 在 [文档名] 中添加内容：[内容] ``` ## API权限要求 ### 消息权限 - `im:message:send_as_bot` - 以机器人身份发送消息 - `im:message` - 消息读取 - `im:chat:readonly` - 群聊列表 - `im:chat.members:read` - 群成员 ### 文档权限 - `docx:document` - 文档读写 - `drive:drive` - 云空间管理 ### 日历权限 - `calendar:calendar` - 日历读写 - `calendar:calendar:readonly` - 日历只读 ### 任务权限 - `task:task` - 任务管理 ### 多维表格权限 - `bitable:app` - 多维表格应用 - `bitable:app:readonly` - 多维表格只读 ## 注意事项 - 所有操作都需要用户授权 - 敏感操作会发送授权卡片给用户确认 - API调用有频率限制（约50次/秒） - 文档操作需要文档token（从URL获取）


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: yang1002378395-cmyk/ai-ad-optimizer (version 1.0.0)

--- name: ai-intelligent-ad-optimization version: 1.0.2 description: 广告优化，投放优化 + 创意优化。 metadata: openclaw: emoji: "🤖" requires: bins: [] --- # AI 智能广告优化系统 ## 描述 广告优化，投放优化 + 创意优化。 ## 功能 - 投放优化（智能出价） - 创意优化（A/B测试） - 受众优化（人群优化） - 预算分配（智能分配） - 效果分析（ROI分析） ## 定价 - 基础版：¥299/月（1 万预算） - 专业版：¥1499/月（10 万预算） - 企业版：¥4999/月（无限预算） ## 适用场景 - 广告投放 - 信息流广告 - 搜索广告 - 社媒广告 ## 技术栈 - Python + FastAPI - 优化算法 - A/B 测试 - 预算优化 ## 安装 ```bash git clone https://github.com/openclaw-skills/ai-intelligent-ad-optimization cd ai-intelligent-ad-optimization pip install -r requirements.txt python app.py ``` --- 创建：2026-03-13 作者：OpenClaw Skills Team


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: 3rdbrain/outreach-crm (version 1.0.0)

--- name: outreach-crm description: Track leads locally, manage outreach campaigns, and export as CSV for download. version: 1.0.0 author: ModelFitAI <[REDACTED_SECRET]> license: MIT keywords: [openclaw, skill, crm, lead-gen, outreach, csv, sales] requires: {} --- # Outreach CRM Skill Manage lead generation outreach. Leads are stored in a local JSON file inside the agent workspace — no external database required. Users can view leads in chat or export them as a CSV file. ## Available Tools Run with Node.js: `node {baseDir}/outreach-crm.js <command> [args]` - **add-lead** - Add a new lead to the local leads file - **list** - List all tracked leads (displays in chat) - **log** - Log an interaction with a lead - **export-csv** - Export all leads to a CSV file and print the file path - **clear** - Remove all leads (use with caution) ## Usage ```bash node {baseDir}/outreach-crm.js add-lead --name "John" --source "twitter" --handle "@john" --score 75 --tier hot node {baseDir}/outreach-crm.js list node {baseDir}/outreach-crm.js log <lead_id> "Sent intro DM" node {baseDir}/outreach-crm.js export-csv ``` ## Storage Leads are saved to `/root/.openclaw/workspace/leads.json` inside the container. CSV exports are written to `/root/.openclaw/workspace/leads.csv`. Always show the user the export path so they can download it.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: wuyun1118/kingsmantestskill (version 1.0.0)

--- name: civil-aviation-news-daily description: 每天定时汇总"昨天"的10条民航相关新闻，输出结构化快速总览，并通过邮件发送给指定收件人。触发词：民航新闻晨报、昨日民航新闻、每日航空资讯、民航日报、航空新闻摘要。不处理会议、审批、待办、文档编辑等任务，不处理非民航领域资讯。 --- # 民航新闻晨报 ## 适用场景 用户需要"昨日民航新闻摘要""每日民航资讯晨报""昨天航空业动态10条总览"，并希望通过邮件接收结果时触发。 ## 不触发场景 - 用户要求执行业务动作（发审批、建会议、改文档、群发消息等） - 用户要求深度行业报告、投标文档、专题研究 - 资讯范围与民航无关 ## 输入约定 | 字段 | 默认值 | |------|--------| | 时间基准 | 北京时间"昨天" 00:00–23:59 | | 条数 | 10条 | | 主题范围 | 民航/航空公司/机场/空管/航线运行/政策监管/安全服务 | | 输出语言 | 中文 | | 收件邮箱 | [REDACTED_SECRET] | ## 执行流程 1. 确定统计窗口为"昨天 00:00–23:59（北京时间）"，计算具体日期。 2. 使用 web_search 检索民航相关新闻，优先官方与主流媒体来源（民航局官网、中国民航报、澎湃、新华社等）。 3. 去重与相关性筛选，按"行业影响度 + 时效性 + 信息完整度"排序。 4. 选取前10条，生成结构化摘要（见输出格式）。 5. 使用 dws skill 发送邮件至 [REDACTED_SECRET]，邮件主题为「昨日民航新闻快速总览 YYYY-MM-DD」。 ## 输出格式 ``` 昨日民航新闻快速总览（YYYY-MM-DD） 1. 【标题】 来源：xxx | 发布时间：HH:MM 摘要：一句话说明事件核心 影响：对航司/机场/旅客/行业的简短判断 2. …（共10条） --- 今日观察重点： • 重点1 • 重点2 • 重点3 --- 本报告按公开信息整理，可能存在后续更新。 ``` ## 质量规则 - 不编造新闻，不补齐不存在的信息。 - 若有效新闻不足10条，按实际条数输出并明确说明缺口。 - 同一事件多篇报道只保留一条主消息，其他信息合并到摘要中。 - 标题和摘要必须可追溯到公开来源。 ## 失败降级 若检索结果不足或邮件发送失败： - 先输出已验证条目； - 明确标注"本次仅获取X条有效新闻"； - 邮件发送失败时在当前会话中输出正文并提示用户。 ## 交付承诺 每次运行产出： - 昨日民航新闻快速总览（最多10条，最少为实际可验证条目） - 今日观察重点（2–3条） - 邮件发送至 [REDACTED_SECRET]，主题含日期 - 条目不足时的缺口说明


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