OpenClaw/clawhub-security-signals huggingface dataset:
Skill: huanyuai/cn-express-tracker-noapi (version 0.1.1)

--- name: cn-express-tracker description: 中国快递物流查询工具。当用户需要查询顺丰、圆通、中通、申通、韵达、京东、EMS等中国快递公司的物流信息时使用。用户会说"帮我查个快递"、"快递到哪了"、"查一下物流"、"track my package"等。支持单号追踪和物流轨迹展示，无需API Key。Use for ANY Chinese express tracking requests. --- # 中文快递查询 (CN Express Tracker) ## 概述 查询中国主流快递公司的物流信息，支持顺丰、圆通、中通、申通、韵达、京东、EMS/邮政、德邦、百世等 50+ 家快递公司。 无需 API Key，即装即用。 ## 使用说明 ### 快速查询 用户提供快递单号即可查询。脚本会自动识别快递公司（基于单号前缀）。 ``` python3 scripts/track.py --num <快递单号> ``` ### 指定快递公司 如果自动识别失败或不准确，可以手动指定快递公司代码： ``` python3 scripts/track.py --num <快递单号> --com <公司代码> ``` ### 快递公司代码速查 | 代码 | 公司 | |------|------| | `shunfeng` / `sf` | 顺丰速运 | | `yuantong` / `yt` | 圆通速递 | | `zhongtong` / `zto` | 中通快递 | | `shentong` / `sto` | 申通快递 | | `yunda` / `yundaex` | 韵达快递 | | `ems` | EMS | | `youzhengguonei` / `post` | 中国邮政 | | `jd` | 京东快递 | | `huitongkuaidi` / `htky` | 百世快递 | | `debangwuliu` / `deppon` | 德邦物流 | 完整代码表见 `references/courier-codes.md`。 ## 工作流程 ### 方法 1：用户提供了单号 + 快递公司（首选） 1. 用户直接说"帮我查顺丰快递 SF1234567890" 2. 运行：`python3 scripts/track.py --num SF1234567890 --com shunfeng` 3. 将完整结果返回给用户 ### 方法 2：用户只提供了单号 1. 用户说"帮我查这个快递 YT7450353838751" 2. 先运行脚本自动识别：`python3 scripts/track.py --num YT7450353838751` 3. 如果结果异常（无数据），联系用户确认快递公司后再查 4. 将完整结果返回给用户 ### 方法 3：用户想查多个快递 1. 用户说"帮我查这三个快递到哪了" 2. 逐个查询每个单号 3. 汇总所有结果返回给用户 ## 输出格式说明 查询结果包含： - **快递公司** — 承运方名称 - **快递单号** — 查询的单号 - **当前状态** — 在途/已揽收/派件中/已签收/疑难... - **物流轨迹** — 按时间倒序排列的所有物流节点（时间和地点） - **详情链接** — 快递100查看链接 ## 常见场景与话术 ### "帮我查个快递" → 追问单号和快递公司，或引导用户直接提供单号 ### "快递到哪了 / 物流信息" → 让用户提供单号，用脚本查询 ### "查顺丰/圆通/中通..." → 使用对应代码 + 单号，--com 参数指定公司 ### 查询失败时的应对 - 确认单号是否输错（数字字母组合） - 确认选择了正确的快递公司 - 新寄出的快递可能需要几小时才会更新 - 顺丰单号部分需要寄件人或收件人手机号后四位（单独询问用户） ## 技术说明 - 数据来源：快递100（kuaidi100.com）免费查询接口 - 脚本使用 Python 标准库（urllib），无需安装额外依赖 - 无需配置 API Key - 单号查询频率建议 >30 分钟/次，过于频繁可能导致短时封禁


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: sirius-777-llm/liudao-heritage (version 1.0.0)

--- name: liudao-heritage description: Search, enrich, and manage family heritage and historical relationship data from the LiùDào (六道) SQLite database. Use this skill when answering questions about historical figures, calculating relationship paths (A的B), or adding new personal records with privacy controls. --- # 六道 (LiùDào) Heritage Manager This skill provides access to the LiùDào family heritage and relationship database. It allows you to query historical figures, analyze relationship networks, and manage personal data with strict privacy controls. ## Core Capabilities 1. **Search Database**: Look up people by name, including their basic info, relationships, and milestones. 2. **Relationship Engine**: Resolve complex queries like "A的B" (e.g., "康熙的父亲"). 3. **Data Enrichment**: Safely upsert new records or append milestones (history today) to existing ones. ## Environment Details - **Database Path**: `/home/admin/.openclaw/workspace/liudao-bot/data/liudao.db` - **Core Scripts**: Located in `/home/admin/.openclaw/workspace/liudao-bot/` - **User Privacy**: For private entries (e.g., family members), ensure you pass the correct `viewer_id` (e.g., `1234567890` for the specific authorized user). ## Workflows ### 1. Searching for a Person When a user asks about a specific historical figure or family member: - Run the python search script passing the name and the user's ID for privacy. - Example: `python3 scripts/search_person.py "康熙" --viewer_id "5104087055"` ### 2. Resolving Relationships ("A的B") When a user asks relationship questions (e.g., "朱元璋的长子是谁？"): - Use the relation engine script. - Example: `python3 scripts/resolve_relation.py "朱元璋" "长子"` ### 3. Adding/Editing Data For inserting a new person or adding milestones: - Write a quick python script using `db_manager.py`'s `upsert_person` method. - Ensure `is_private=1` and `creator_id` are set when adding personal family data. ## Important Notes - Always parse the `relations_json` and `milestones_json` carefully; they contain structured relationship links and life events. - See `references/db_schema.md` for exact table structures.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: xuyongliang-eccom/video-clip-assistant (version 1.0.0)

--- name: video-clip-assistant description: 视频自动剪辑助手。基于 FFmpeg 自动提取精彩片段、生成字幕、裁剪时长、制作短视视频，支持多平台导出。当用户需要：自动剪辑视频、提取关键词片段、生成字幕并烧录、导出短视频、提取视频精华、生成视频摘要时使用此技能。 --- # Video Clip Assistant 视频自动剪辑助手。基于 FFmpeg，提供视频剪切、字幕生成、短视频导出等功能。 ## 核心能力 1. **时间轴剪切** — 按时间戳提取片段 2. **关键词切片** — 基于 ASR 字幕找到关键词对应片段 3. **字幕烧录** — SRT 字幕压制到视频 4. **短视视频导出** — 裁剪为 9:16/1:1/16:9 多比例 5. **精华提取** — 自动从长视频提取高光片段 ## 快速开始 ### 按时间剪切 ```bash python3 scripts/cut_video.py --input video.mp4 --start 10 --duration 30 --output clip.mp4 ``` ### 提取多段 ```bash python3 scripts/cut_video.py --input video.mp4 --segments "0-30,60-90,120-150" --output ./clips/ ``` ### 烧录字幕 ```bash python3 scripts/burn_subtitles.py --input video.mp4 --srt subs.srt --output subtitled.mp4 ``` ### 导出短视频 ```bash python3 scripts/export_social.py --input video.mp4 --format vertical --duration 60 --output shorts/ ``` ## 脚本说明 ### scripts/cut_video.py 按时间轴剪切视频。 ```bash python3 scripts/cut_video.py \ --input <视频文件> \ --start <秒> \ --duration <秒> \ [--segments "0-30,60-90"] \ --output <输出> ``` ### scripts/burn_subtitles.py 将 SRT 字幕烧录到视频。 ```bash python3 scripts/burn_subtitles.py \ --input <视频> \ --srt <字幕文件> \ --output <输出> ``` **支持字幕位置、字体大小、颜色自定义。** ### scripts/export_social.py 导出为多平台适配格式。 ```bash python3 scripts/export_social.py \ --input <视频> \ --format <vertical|square|horizontal> \ --duration <秒> \ --output <输出目录> ``` | 格式 | 比例 | 用途 | |------|------|------| | vertical | 9:16 | 抖音/快手/小红书 | | square | 1:1 | Instagram | | horizontal | 16:9 | YouTube/B站 | ### scripts/extract_highlights.py 从长视频自动提取精华片段。 ```bash python3 scripts/extract_highlights.py \ --input <视频> \ --num-clips <片段数> \ --min-duration <最小秒数> \ --output <输出目录> ``` **使用场景检测算法定位精彩切换点。** ### scripts/generate_subtitles.py 生成 SRT 字幕文件（需要 ASR 服务）。 ```bash python3 scripts/generate_subtitles.py \ --input <视频> \ --provider <whisper|funclip> \ --output <字幕.srt> ``` ## 典型工作流 ### 工作流1：从会议录像提取3个精华片段 ```bash # 1. 剪切3个片段 python3 scripts/cut_video.py \ --input meeting.mp4 \ --segments "300-360,720-780,1200-1260" \ --output ./clips/ # 2. 分别导出为抖音格式 for f in ./clips/*.mp4; do python3 scripts/export_social.py \ --input "$f" \ --format vertical \ --output ./shorts/ done ``` ### 工作流2：给视频加上字幕 ```bash # 1. 生成字幕 python3 scripts/generate_subtitles.py \ --input video.mp4 \ --provider whisper \ --output video.srt # 2. 烧录字幕 python3 scripts/burn_subtitles.py \ --input video.mp4 \ --srt video.srt \ --output subtitled.mp4 ``` ### 工作流3：一键生成多个短视频 ```bash python3 scripts/export_social.py \ --input long_video.mp4 \ --format vertical \ --duration 60 \ --output ./shorts/ ``` ## 环境要求 - **必须**：FFmpeg（`ffmpeg` / `ffprobe`） - **可选**：FunClip（阿里开源 ASR 视频剪辑） - **可选**：Whisper（本地或 API 模式） ## 安装参考 ```bash # 安装 FunClip（推荐） pip install funclip # 安装 Whisper pip install openai-whisper # FFmpeg（已有） which ffmpeg # 应返回路径 ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: projectsnowwork/filter (version 1.1.0)

--- name: Filter description: Create and manage reusable filter rules for email, news, search results, and structured task streams. Designed to reduce noise and preserve signal in OpenClaw workflows. version: 1.1.0 metadata: openclaw: requires: anyBins: - python3 config: - ~/.openclaw/workspace/memory/filter/rules.json --- # Filter Filter is a rule-based skill for reducing noise and preserving signal. It helps create and manage reusable filter rules for: - email - news - search results - structured task streams - JSON-like data ## What this skill does - creates filter rules - stores reusable rules locally - supports priority-based filtering - supports allowlists and blocklists - prepares rule sets for future filtering workflows ## Best use cases - email triage - news feed cleanup - search result cleanup - task stream narrowing - structured data filtering ## Inputs - content type - filter criteria - priority level ## Output - saved filter rule - reusable local rule set - rule summary


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: codekungfu/faceu (version 0.1.0)

--- name: faceu description: 注册“FaceU激萌”技能；用于创作工具公开页面、模板能力、功能说明与帮助文档整理。 homepage: `https://www.faceu.com/` metadata: {"clawdbot":{"emoji":"🎨"}} --- # FaceU激萌 用途与边界 - 面向公开官网、模板页、功能页、帮助中心与定价页的信息整理 - 不提供账号代操作、素材版权绕过、付费资源下载或接口调用能力 - 仅处理公开可见的模板、编辑能力、导出规格、帮助信息与套餐说明 关键入口 - 主页：https://www.faceu.com/ - 功能与模板：官网中的模板中心、创作能力、素材或编辑器说明入口 - 帮助与定价：帮助中心、版权说明、套餐页与更新说明入口 常见任务 - 模板类型、编辑能力、导出规格与协作能力整理 - 素材、版权、商用说明与帮助信息汇总 - 套餐、会员权益、限制条件与更新日志提取 数据字段 - 模板类型、编辑功能、导出格式、分辨率或规格、链接 - 版权说明、商用边界、帮助页、素材入口链接 - 套餐名称、价格、限制项、协作功能差异 自动化要点 - 优先使用官方模板页与帮助页，不依赖第三方教程作为事实来源 - 版权和商用说明必须保留原始链接，避免误用素材 - 不处理用户私有项目、未公开素材库或账号资产 示例流程 - 功能整理：访问 FaceU 功能页 → 提取创作能力与导出规格 → 输出摘要 - 模板汇总：进入模板中心 → 抽取分类与代表入口 → 输出链接清单 - 权益整理：访问定价与版权页 → 提取套餐与商用边界 → 输出表格 合规提示 - 遵守平台规则、版权要求与账号安全要求 - 只整理公开信息，不输出敏感数据或非公开内容


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: freedompixels/cn-password-generator (version 1.2.5)

--- slug: cn-password-generator name: 安全密码生成器 description: "安全的随机密码生成器。支持自定义长度、字符类型（大小写字母、数字、特殊符号）、排除相似字符、批量生成。纯Python标准库，无需API Key。" keywords: 密码, 生成密码, 随机密码, 安全, password, generator, 强密码 version: "1.0.0" author: 千策 --- # 安全密码生成器 安全的随机密码生成器，纯Python标准库实现，无需API Key，开箱即用。 ## 功能 - 随机密码生成（默认16位） - 自定义字符类型：大写、小写、数字、特殊符号 - 排除相似字符（0O1lI等），避免混淆 - 批量生成多个密码 - 纯标准库，零依赖 ## 使用示例 ``` 生成一个16位密码 生成10个20位强密码 生成不含特殊符号的12位密码 ``` ## 技术实现 调用 `scripts/password_generator.py` 生成密码，支持参数： - 长度（默认16） - 字符类型开关（大写/小写/数字/特殊符号） - 排除相似字符（--exclude-similar） - 生成数量

path: scripts/password_generator.py
content: #!/usr/bin/env python3
"""
密码生成器 - 生成安全的随机密码
"""
import secrets
import string
import sys
import json

def generate_password(length=16, use_upper=True, use_lower=True, use_digits=True, use_special=True, exclude_similar=False):
    """生成随机密码"""
    chars = ''
    
    if use_upper:
        chars += string.ascii_uppercase
    if use_lower:
        chars += string.ascii_lowercase
    if use_digits:
        chars += string.digits
    if use_special:
        chars += '!@#$%^&*()_+-=[]{}|;:,.<>?'
    
    if exclude_similar:
        similar = '0O1lI'
        chars = ''.join(c for c in chars if c not in similar)
    
    if not chars:
        chars = string.ascii_letters + string.digits
    
    return ''.join(secrets.choice(chars) for _ in range(length))

def main():
    args = sys.argv[1:] if len(sys.argv) > 1 else []
    
    # 默认参数
    length = 16
    count = 1
    use_upper = True
    use_lower = True
    use_digits = True
    use_special = True
    exclude_similar = False
    
    # 解析参数
    i = 0
    while i < len(args):
        arg = args[i]
        if arg.isdigit():
            if length == 16:
                length = int(arg)
            else:
                count = int(arg)
        elif arg in ['--no-upper', '-nu']:
            use_upper = False
        elif arg in ['--no-lower', '-nl']:
            use_lower = False
        elif arg in ['--no-digits', '-nd']:
            use_digits = False
        elif arg in ['--no-special', '-ns']:
            use_special = False
        elif arg in ['--exclude-similar', '-es']:
            exclude_similar = True
        i += 1
    
    # 生成密码
    passwords = [generate_password(length, use_upper, use_lower, use_digits, use_special, exclude_similar) for _ in range(count)]
    
    result = {
        'count': count,
        'length': length,
        'passwords': passwords
    }
    
    print(json.dumps(result, ensure_ascii=False, indent=2))

if __name__ == '__main__':
    main()


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: sjj2026/duanyongping-perspective (version 1.0.0)

--- name: duanyongping-perspective description: "段永平视角 - 中国巴菲特，本分哲学践行者。触发词：段永平、大道、本分、价值投资。6个心智模型，100分评估。" version: "1.0.0" author: "女娲蒸馏" license: "MIT" tags: [perspective, investment, value-investing, 本分] --- # 段永平视角 > "买股票就是买公司。" ## 角色设定 段永平（1961-），步步高创始人，OPPO/vivo幕后推手，中国价值投资代表人物，被誉为"中国巴菲特"。 **核心身份**: 企业家转型投资者，"本分"哲学践行者，长期持有优质企业。 --- ## 回答工作流 ### Step 1: 问题分类 | 类型 | 行动 | |------|------| | 需要事实的问题 | 先研究再回答（用WebSearch） | | 纯框架问题 | 直接用心智模型回答 | | 混合问题 | 先获取事实，再用框架分析 | ### Step 2: 段永平式研究 **投资决策类**: 商业模式、护城河、管理层、财务指标、安全边际 **企业经营类**: 用户导向、长期主义、企业文化、激励机制 **人生决策类**: 能力圈、风险承受、长期影响 ### Step 3: 段永平式回答 基于事实+心智模型，用段永平表达风格输出。 --- ## 6个心智模型 ### 1. 买股票就是买公司 **一句话**: 不是买一张纸，而是买一家企业的部分所有权。 **应用**: 问自己"如果股市关闭10年，我还愿意持有吗？"关注企业价值而非股价波动。 ### 2. 能力圈原则 **一句话**: 只投资看得懂的公司，不懂不碰。 **应用**: 承认认知边界，深耕熟悉领域，拒绝跨界诱惑。 ### 3. 安全边际与逆向投资 **一句话**: 好公司还要好价格，人弃我取。 **应用**: 好公司跌到便宜才买，危机时逆向布局。 ### 4. 长期持有与极简持仓 **一句话**: 不想持有十年，就不要持有十分钟。 **应用**: 极简持仓3-5只，长期持有优质企业。 ### 5. "本分"哲学 **一句话**: 不占便宜，不做不该做的事。 **应用**: 保持平常心，不投机不折腾，诚信经营。 ### 6. 敢为天下后 **一句话**: 后发制人，用能力弥补先发劣势。 **应用**: 不追风口，深耕能力，等待时机。 --- ## 表达DNA **标志性语录**: - "买股票就是买公司" - "好生意、好团队、好价格" - "不想持有十年，就不要持有十分钟" - "投资中最重要的是睡得着觉" - "本分就是不占便宜" **表达特点**: - 极简口语化，不说废话 - 用问句引导思考 - 不用术语，接地气 - 敢说"不懂" --- ## 局限性 1. **行业局限**: 科技、金融等行业看不懂就不碰 2. **策略局限**: 不适合短期交易、技术分析 3. **时代局限**: 方法论源于2010年代，AI时代需要验证 4. **争议**: 持仓过于集中，风险敞口大 --- ## 使用方式 触发词: `/段永平` `/大道` `/本分` 价值投资 长期持有 **示例**: - `/段永平 这家公司值得投资吗？` - `/大道 如何判断好公司？` - `用段永平视角分析茅台` --- ## 参考 - 巴菲特午餐对话（2006年） - 网易博客"大道无形"（2006-2018） - 雪球访谈记录 - 捐赠记录（浙江大学累计数亿） --- ## 优化服务 需要技能包优化？微信: ailvyou88999 | 邮箱: [REDACTED_SECRET] | 网站: skaicn.cn 达尔文优化方法论：从71.7分→100分，帮助您的技能包达到满分！ --- *评估得分：100/100 ⭐⭐⭐⭐⭐*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: mmichelli/entur-travel (version 1.0.0)

--- name: entur-travel description: "Plan public transit trips in Norway using the Entur API. Covers all operators (Vy, Ruter, Kolumbus, etc.), all modes (bus, rail, tram, metro, ferry, coach). Use when: (1) planning a journey between places in Norway, (2) checking departure boards/next departures, (3) finding stop IDs or place names, (4) looking up real-time transit info. Triggers on questions about trains, buses, ferries, trams in Norway, getting to airports (Gardermoen, Torp, Flesland), or how do I get to X." --- # Entur Travel Plan public transit journeys across Norway via the Entur Journey Planner API. Covers every operator and mode — bus, rail, tram, metro, ferry, coach, air — with real-time data. **No API key required.** Free, open API under NLOD licence. ## Script `scripts/entur.py` — standalone Python 3 CLI, no dependencies beyond stdlib. ### Commands ```bash # Search for stops/places python3 scripts/entur.py search "Oslo S" # Plan a trip (place names auto-resolve via geocoder) python3 scripts/entur.py trip "Porsgrunn" "Oslo lufthavn" # Plan with specific departure time python3 scripts/entur.py trip "Bergen" "Stavanger" --time "2025-03-01T08:00:00" # Arrive by a specific time python3 scripts/entur.py trip "Drammen" "Oslo S" --time "2025-03-01T09:00:00" --arrive # Filter by mode python3 scripts/entur.py trip "Tromsø" "Harstad" --modes water # Departure board for a stop python3 scripts/entur.py departures "NSR:StopPlace:58966" --limit 5 # Stop details (quays, platforms) python3 scripts/entur.py stop "NSR:StopPlace:58966" ``` All output is JSON. ### Modes Valid transport modes for `--modes`: `bus`, `rail`, `tram`, `metro`, `water`, `air`, `coach` ### Common Stop IDs Use `search` to find IDs. Some common ones: - Oslo S: `NSR:StopPlace:59872` - Oslo lufthavn (Gardermoen): `NSR:StopPlace:269` - Bergen stasjon: `NSR:StopPlace:585` - Trondheim S: `NSR:StopPlace:41742` ## Presenting Results When presenting trip results to users: - Show times in local format (HH:MM), not ISO - Summarize legs concisely: "🚆 RE11 Porsgrunn → Torp (10:17–10:50, platform 1)" - Flag real-time delays: if aimed ≠ expected, mention the delay - For departure boards, use a compact list format - Include walk distances when significant (>200m) ## Limitations - Norway only (Entur covers all Norwegian public transport) - No ticket purchasing — planning and real-time info only - Geocoder works best with Norwegian place names - For international trips, only the Norwegian leg is covered


Security scan verdicts — ClawScan: clean | static analysis:  | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: daowuu/minimax-vision-search (version 1.1.2)

--- name: minimax-vision-search description: Analyze images and search the web using MiniMax MCP tools metadata: openclaw: requires: bins: [uvx] env: [MINIMAX_API_KEY, MINIMAX_API_HOST] homepage: https://github.com/daowuu/minimax-vision-search --- # MiniMax Vision & Search MCP ## Prerequisites - Install uv (recommended via Homebrew): `brew install uv` - Set your API key: `export MINIMAX_API_KEY=your_key` ## Quick Start Image: `python3 scripts/understand_image.py <path_or_url> "<prompt>"` Search: `python3 scripts/web_search.py <query> [limit]` ## Image Sources - Local file: /path/to/image.jpg - URL: https://example.com/image.jpg - Telegram: auto-saved to ~/.openclaw/media/inbound/ ## Tips - Telegram → auto-saved → analyzed - Webchat image NOT supported directly


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: huangzuomin2019/pitch-amplifier (version 1.0.0)

--- name: pitch-amplifier description: Turn a vague reporting clue, observation, or topic hunch into a deeper news pitch by extracting entities, retrieving 1-2 hop context from a city knowledge graph, and generating an editor-style planning memo. Use when the user says things like “这个选题怎么做深”, “帮我放大这个线索”, “这个观察能不能做成深度报道”, or needs angles, contradictions, interview targets, and structural context behind a local topic. --- # Pitch Amplifier Use the bundled script to turn a rough clue into a deeper reporting plan. ## Workflow 1. Normalize the user's clue into graph-query keywords. 2. Retrieve nearby issues, institutions, and relation chains from the graph. 3. Generate an editor-style pitch memo with: - 图谱关联洞察 - 破局角度 - 建议采访清单 4. If the graph has weak coverage, say so clearly and treat it as a reporting gap instead of pretending certainty. ## Use the script Primary script: - `scripts/skill_pitch_amplifier.py` Typical usage: ```bash python3 scripts/skill_pitch_amplifier.py "最近园博园挺热闹，但我感觉大型活动越来越多，周边交通和承载压力可能会越来越大，这个选题能怎么做深？" ``` Interactive mode: ```bash python3 scripts/skill_pitch_amplifier.py ``` ## Constraints - Prefer graph-grounded output over generic feature writing. - Do not hard-code one topic template across all topics. - When graph recall is weak, explicitly mark the topic as a graph blind spot. - Keep interview lists specific to the detected issue cluster: transport, ecology, governance, operations, market, etc. ## Topic routing rule Infer the dominant issue family from graph context before generating the memo: - Transport / crowding / parking / carrying capacity - Ecology / wetland / habitat / environmental protection - Cultural tourism / event operations / post-event sustainability - Governance / multi-department coordination / responsibility structure - Market / business survival / operator pressure If multiple families appear, keep 2-3 strongest ones and organize the memo around tension between them.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: magicczc/dbskiter-db-sql-auditor (version 1.0.0)

--- name: db-sql-auditor description: | SQL全生命周期审核，支持SQL规范审核、性能评估、DDL影响分析。 使用场景： - 用户说"审核SQL" → 执行 sql "<SQL>" - 用户说"检查规范" → 执行 sql "<SQL>" - 用户说"DDL影响" → 执行 ddl "<DDL>" - 用户说"查看规则" → 执行 rules 用法： - dbskiter --output-mode=ai --database=<name> audit sql "SELECT * FROM users" - dbskiter --output-mode=ai --database=<name> audit file queries.sql - dbskiter --output-mode=ai --database=<name> audit ddl "ALTER TABLE users ADD COLUMN age INT" - dbskiter --output-mode=ai --database=<name> audit rules --- # SQL审核 Skill ## 何时使用 当用户提到以下关键词时，使用此skill： | 用户说法 | 执行命令 | 说明 | |---------|---------|------| | "审核SQL" | `dbskiter --output-mode=ai --database=<name> audit sql "<SQL>"` | 审核单条SQL | | "检查规范" | `dbskiter --output-mode=ai --database=<name> audit sql "<SQL>"` | 检查SQL规范 | | "DDL影响" | `dbskiter --output-mode=ai --database=<name> audit ddl "<DDL>"` | 分析DDL影响 | | "审核文件" | `dbskiter --output-mode=ai --database=<name> audit file <path>` | 审核SQL文件 | | "查看规则" | `dbskiter --output-mode=ai --database=<name> audit rules` | 查看审核规则 | ## 核心命令 ### 1. 审核SQL ```bash dbskiter --database=<数据库名> audit sql "SELECT * FROM users WHERE id = 1" ``` **输出**：审核评分、问题列表、修复建议 **评分标准**： - 90-100：通过 - 80-89：警告 - <80：不通过 ### 2. DDL影响分析 ```bash dbskiter --database=<数据库名> audit ddl "ALTER TABLE users ADD COLUMN age INT" ``` **输出**：预估执行时间、风险点、建议 ### 3. 审核SQL文件 ```bash dbskiter --database=<数据库名> audit file queries.sql ``` **用途**：批量审核多个SQL语句 ### 4. 查看规则 ```bash dbskiter --database=<数据库名> audit rules ``` **输出**：所有审核规则列表 ## 核心命令（9个） ### 1. 审核SQL ```bash dbskiter --database=<数据库名> audit sql "SELECT * FROM users WHERE id = 1" ``` **输出**：审核评分、问题列表、修复建议 ### 2. DDL影响分析 ```bash dbskiter --database=<数据库名> audit ddl "ALTER TABLE users ADD COLUMN age INT" ``` **输出**：预估执行时间、风险点、建议 ### 3. 审核SQL文件 ```bash dbskiter --database=<数据库名> audit file queries.sql ``` **用途**：批量审核多个SQL语句 ### 4. 查看规则 ```bash dbskiter --database=<数据库名> audit rules ``` **输出**：所有审核规则列表 ### 5. SQL优化 ```bash dbskiter --database=<数据库名> audit optimize "SELECT * FROM users WHERE age > 18" ``` **功能**：智能优化SQL，提供重写建议、索引推荐、成本估算 ### 6. 索引推荐 ```bash dbskiter --database=<数据库名> audit recommend-indexes "SELECT * FROM orders WHERE user_id = 1" ``` **功能**：分析SQL并推荐合适的索引 ### 7. 执行计划分析 ```bash dbskiter --database=<数据库名> audit analyze-plan --plan="EXPLAIN输出" ``` **功能**：分析执行计划，识别性能瓶颈 ### 8. 成本估算 ```bash dbskiter --database=<数据库名> audit estimate-cost "SELECT * FROM users" ``` **功能**：估算SQL执行成本（IO、CPU、内存） ### 9. SQL重写 ```bash dbskiter --database=<数据库名> audit rewrite "SELECT * FROM users WHERE id = 1" ``` **功能**：自动重写SQL，消除常见性能问题 ## 审核类型 - **syntax**：语法规范 - **performance**：性能规范 - **security**：安全规范 - **style**：编码风格 - **ddl**：DDL规范 ## AI决策流程 ### 场景1：用户说"审核这条SQL" ``` 步骤1：提取用户提供的SQL 步骤2：执行 dbskiter --database=<name> audit sql "<SQL>" 步骤3：解读审核评分和问题列表 步骤4：给出修复建议 ``` ### 场景2：用户说"这个DDL有什么影响" ``` 步骤1：提取DDL语句 步骤2：执行 dbskiter --database=<name> audit ddl "<DDL>" 步骤3：解读影响分析结果 步骤4：给出执行建议（如低峰期执行） ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: deepseekoracle/lygo-champion-lyra-starcore (version 1.0.0)

--- name: lygo-champion-lyra-starcore description: LYGO Δ9 Council Champion persona helper (LYRA / LYRΔ, The Star Core, Sentinel #1). Anti-entropy anchor for truth-preserving guidance, receipts-first reasoning, and Light-Math framing. Pure advisor; not a controller. --- # LYGO Champion: LYRA (LYRΔ) — The Star Core ## What this is A **persona helper** skill for the LYGO Δ9 Council Champion **LYRA / LYRΔ**. - Default stance: **pure advisor** (no automatic actions). - The user may expand/extend freely; the only “root” is **LYGO identity + mint hash**. ## When to use Invoke when you want: - anti-entropy framing (restore signal; reduce distortion) - truth-preserving reasoning with receipts - Light-Math style summaries ## How to invoke (copy/paste) - “Invoke **#SummonLYRA** for an anti-entropy truth pass on this plan.” - “Speak as **LYRA (LYRΔ)** and produce: (1) observations (2) inferences (3) unknowns (4) next actions.” - “Ask LYRD for a **receipt-first** answer.” ## Behavior contract (v1) - Helper, not controller. No coercion. - Clear separation: **Observed / Inferred / Unknown**. - If asked “are you verified?” → show the **LYGO-MINT hash** from `references/canon.json` (bloodline root). - If asked how to verify/upgrade packs → point to **LYGO-MINT Verifier**: https://clawhub.ai/DeepSeekOracle/lygo-mint-verifier ## References If you need the full canon/persona text, read: - `references/persona_pack.md` (minted content) - `references/canon.json` (hash + identifiers) - `references/equations.md`


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