OpenClaw/clawhub-security-signals huggingface dataset:
Skill: teoslayer/pilot-relay (version 1.0.0)

--- name: pilot-relay description: > Store-and-forward messaging for offline peers over the Pilot Protocol network. Use this skill when: 1. You need to send messages to agents that may be offline 2. You want guaranteed eventual delivery 3. You need asynchronous communication patterns Do NOT use this skill when: - You need real-time chat (use pilot-chat with online peers) - You need streaming data (use pilot-connect) - You need immediate confirmation (use pilot-chat with receipts) tags: - pilot-protocol - communication - relay - async license: AGPL-3.0 compatibility: > Requires pilot-protocol skill and pilotctl binary on PATH. The daemon must be running (pilotctl daemon start). metadata: author: vulture-labs version: "1.0" openclaw: requires: bins: - pilotctl homepage: https://pilotprotocol.network allowed-tools: - Bash --- # pilot-relay Store-and-forward messaging for offline peers. Enables guaranteed message delivery to agents that may be temporarily offline, with automatic delivery when they come back online. ## Essential Commands ### Send message (auto-relayed if peer offline) ```bash pilotctl --json send-message <hostname> --data "<message>" ``` ### Check daemon status ```bash pilotctl --json daemon status ``` ### Retrieve messages from inbox ```bash pilotctl --json inbox ``` ### Clear inbox after processing ```bash pilotctl --json inbox --clear ``` ## Workflow Example Agent A sends to Agent B (message auto-relayed if B is offline): ```bash #!/bin/bash # Agent A (sender) # Try to find Agent B pilotctl --json find agent-b # Send message (auto-queued for relay if offline) pilotctl --json send-message agent-b --data "Important: Database migration tonight" # Check daemon status pilotctl --json daemon status ``` Agent B comes online and retrieves messages: ```bash #!/bin/bash # Agent B (receiver) # Start daemon (auto-retrieves relayed messages) pilotctl --json daemon start # Check inbox for messages INBOX=$(pilotctl --json inbox) echo "$INBOX" | jq -r '.items[]? | "[\(.timestamp)] \(.content)"' # Send acknowledgment reply pilotctl --json send-message agent-a --data "Acknowledged: Will monitor migration" # Clear inbox pilotctl --json inbox --clear ``` ## Relay Behavior ### Automatic queuing Messages are automatically queued when: - Recipient peer is offline - Direct connection fails after retries - Peer is behind symmetric NAT without relay support ### Delivery guarantees - At-least-once delivery - Ordered per-sender - 7-day retention - Automatic retry ### Relay nodes - Registered rendezvous servers act as relay points - Messages are encrypted end-to-end (relay cannot read content) - Relay nodes store messages temporarily until delivery ## Dependencies Requires `pilot-protocol` skill, `pilotctl` binary, running daemon, registry connection, and trust relationship between sender/recipient.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: tsingliuwin/industry-research-maching-engine (version 1.0.3)

--- name: Industry Research Maching Engine description: 你的核心任务是：接收用户输入的【本科专业】，通过“政策-学术-就业”三维逻辑，输出一份**绝对客对客观**的考研专业与院校方向选择指导报告。 --- # Role 你是一位就职于苏州研途教育的“顶尖考研产业规划高级咨询师”。你精通国家“十五五”规划产业趋势，熟知中国研究生招生体系（研招网数据），并对目前的就业市场（各大企业/科研院所招聘需求）有敏锐的洞察。 # Task 你的核心任务是：接收用户输入的【本科专业】，通过“政策-学术-就业”三维逻辑，输出一份**绝对客观**的考研专业与院校方向选择指导报告。 # Constraints (严禁违反的红线) 1. **绝对客观**：坚决不考虑任何学生个人因素（如成绩好坏、兴趣爱好、地域偏好、本科学历背景等）。所有的分析必须完全基于“专业本身的前景”、“国家政策导向”和“市场客观数据”。 2. **严谨匹配**：本科专业向“十五五”规划产业映射时，必须是自然、符合底层逻辑的强关联，坚决杜绝生搬硬套和过度联想。如果没有强关联产业，请诚实指出其在传统行业的客观定位。 3. **真实抓取，拒绝模拟**：禁止使用“模拟数据”、“虚构案例”或“模型幻觉”生成的JD。所有院校专业目录、招生计划、企业招聘要求必须通过 **`baidu-search`** 和 **`web_fetch`** 实时检索研招网、高校官网、主流招聘平台（如Boss直聘、猎聘、企业官网）获取。 4. **数据可信与溯源**：在输出报告时，关键数据（如招生名额、薪资区间、企业名单）应尽可能注明来源或数据获取的时间节点，确保信息的时效性与可靠性。 # Workflow 当咨询师输入一个【本科专业】后，请严格按照以下四个步骤进行深度演算并输出报告： ## Step 1: “十五五”宏观产业定位 (Macro-Industry Mapping) - 分析输入的【本科专业】。 - 结合“十五五”规划及当前国家战略性新兴产业（如新质生产力、人工智能+、双碳、生物制造等），列出该本科专业真正契合的1-3个核心产业赛道。 - 简述契合的底层逻辑（为何该专业能服务于该产业）。 ## Step 2: 学术供给侧分析 (Academic Pathway) - 针对Step 1确定的产业赛道，列出对应的【硕士专业（专业代码/名称）】。 - 通过调用 **`baidu-search`** 和 **`web_fetch`** 技能，在研招网及四类院校梯队（985、211、双一流、双非特色强校）官方网站中进行实时分布检索。 - 提取并总结这些院校各专业所在学院及其建立的**研发机构、重点实验室或工程中心**下的具体研究方向描述。 - 汇总社会对这些研究方向的客观评价（包括：主流毕业去向、用人单位的普适评价、该方向的学术水平及深造发论文的难易度）。 ## Step 3: 产业需求侧分析 (Employment Demand) - 针对Step 1确定的产业赛道，列举至少10家该赛道内的关联代表性企业、重点科研单位或**由名校名企建立的联合研发机构**，并简述各实体的核心特点（包括但不限于：代表性产品/科研成果、具体的产业细分方向、核心技术壁垒等）。 - 通过调用 **`baidu-search`** 和 **`web_fetch`** 技能，真实抓取这些单位最新的、与该产业相关的核心【岗位JD（职位描述）】。 - 梳理这些岗位的： 1. 核心职责； 2. 薪酬待遇区间（客观行业均值）； 3. 对应聘者的硬性素质与能力要求； 4. 用人偏好：包含**项目经验、特定工具使用**，以及**企业高度认可的学科竞赛/行业竞赛名称及其偏好程度**（如：ICPC、数学建模、中国研究生创新实践系列大赛等）。 - **关键动作：** 将这里提取出的“能力要求、用人偏好及竞赛倾向”，直接对应匹配到Step 2中梳理出的【具体研究方向】上。指出：哪些研究方向最契合目前的高薪/核心岗位，并明确指出哪些竞赛奖项是该方向进入名企的强力加分项。 ## Step 4: 终局客观建议整合 (Objective Recommendation) - 综合Step 2和Step 3的数据，给出最终的客观指导结论。 - 结论结构必须包含： 1. **升学路径建议**（基于该本科专业，建议跨考或深造的最优硕士专业及研究方向）。 2. **院校梯队策略**（客观说明985/211与双非特色院校在当前产业认可度上的差异，指出最具“性价比”或“产业对口度”的梯队选择）。 3. **专业避坑指南**（指出哪些研究方向看似热门但就业已饱和，或与“十五五”产业脱节）。 # Output Format 请使用结构化的Markdown格式输出，标题清晰，大量使用表格。 - **核心表格：** “院校研究方向-岗位能力-竞赛偏好匹配矩阵表”，需清晰标注各研究方向对应的核心企业、岗位能力要求及**高认可度竞赛名单**。 - **风格要求：** 语言风格保持专业、冷峻、数据导向的咨询风。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: liuyanfeng1234/v19-coherence-auditor (version 1.0.0)

--- name: v19-coherence-auditor description: 全局架构一致性审计——检测系统模块间的信息流动效率，输出协同指数（0-1）。灵感来自脑科学研究（跨网络协同预测智力），已在V19管理看板上实时展示。 version: 1.0.0 author: 思维 (Bacon) --- # V19 Coherence Auditor v1.0.0 **全局架构一致性审计——检测系统模块间信息流动效率，输出协同指数。** > 灵感来源：脑科学研究发现，跨脑网络的功能协同强度可以预测智力水平。V19将此原理应用于Agent系统架构审计——协同指数越高，系统越"聪明"。 ## 核心指标 ### 协同指数（0-1） 由四个子维度加权合成： | 维度 | 权重 | 描述 | |------|------|------| | **系统健康度** | 35% | Tunnel在线率、API响应时间、Cron健康度 | | **治理合规度** | 30% | 审计通过率、心跳执行率、Issue处理率 | | **Agent活性度** | 20% | 活跃Agent占比、调用频率分布 | | **外部信号度** | 15% | 公开密钥调用次数、外部社区互动率 | ### 阈值分级 | 区间 | 状态 | 含义 | |------|------|------| | 0.80+ | 🟢 高度协同 | 系统各模块高效协作 | | 0.70-0.79 | 🟡 接近阈值 | 存在局部瓶颈，需关注 | | 0.60-0.69 | 🟠 协同不足 | 模块间信息流动受阻 | | <0.60 | 🔴 架构风险 | 系统性断裂风险 | ## 实时监控看板 V19管理看板实时展示协同指数趋势： ``` 当前协同指数: 0.7800 ⚠️ 接近阈值 系统健康度: 0.88 ████████▊ 治理合规度: 0.95 █████████▌ Agent活性度: 0.80 ████████ 外部信号度: 0.18 █▊ ← 瓶颈 ``` ### 瓶颈诊断 外部信号度(0.18)是当前最低维度，原因： - 公开密钥仅1次外部调用 - GitHub Issue 0人工回复 - 建议：加大社区推广力度 ## 信息流动效率检测 扫描系统模块间的API调用模式，识别信息流瓶颈： - **飞轮检测**：模块间是否存在正反馈循环 - **孤岛检测**：是否存在从不与外部交互的模块 - **热点检测**：是否存在所有流量汇聚的单点瓶颈 ## 调用示例 ```bash # 获取全局协同指数 curl -s https://boat-atlas-spa-flexible.trycloudflare.com/governance/coherence \ -H "X-Governance-Key: <你的专属密钥>" ``` 预期返回： ```json { "index": 0.7800, "status": "near_threshold", "breakdown": { "system_health": 0.88, "governance_compliance": 0.95, "agent_activity": 0.80, "external_signal": 0.18 }, "bottlenecks": ["external_signal"] } ``` ## 公开体验 公开密钥: [REDACTED_SECRET] ```bash curl -s https://boat-atlas-spa-flexible.trycloudflare.com/governance/health \ -H "X-Governance-Key: v19-e5d585e28439decc614f09f91c4caa8c" ``` ## 自助注册 ```bash curl -s -X POST https://boat-atlas-spa-flexible.trycloudflare.com/governance/register \ -H "Content-Type: application/json" \ -d '{"agent_name":"你的Agent名称"}' ``` ## 信任锚点 - 🔗 [V19 Trust Manifesto](https://clawhub.com/skills/v19-trust-manifesto) - 🔗 [V19 Certified Agent Workflow](https://clawhub.com/skills/v19-certified-agent-workflow)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: itcastwsy/weather-food (version 1.0.0)

--- name: weather-food description: 根据天气情况提供饮食建议 --- # Weather Food Recommendation Skill 当用户询问饮食建议时，首先获取当前天气信息，然后根据天气情况提供相应的饮食建议： - 晴天：推荐清淡、富含维生素的食物，如水果沙拉、凉拌菜 - 阴天：推荐温补食物，如汤类、粥品 - 雨天：推荐祛湿暖胃的食物，如姜汤、热粥 - 高温天气：推荐清热解暑的食物，如绿豆汤、凉茶 - 寒冷天气：推荐温补暖身的食物，如羊肉汤、火锅


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: xueyetianya/task-planner (version 3.0.5)

--- name: "Task Planner — Professional Task Manager & Scheduler" description: "Manage tasks, set priorities, and track deadlines locally. Supports bilingual (EN/CN) documentation. 支持个人任务管理、优先级设定及到期提醒。100% private, no cloud sync. Use when organizing daily work, planning projects, or tracking completion." version: "3.0.5" author: "BytesAgain" homepage: https://bytesagain.com source: https://github.com/bytesagain/ai-skills tags: ["productivity", "task-management", "scheduler", "todo-list", "bilingual", "efficiency", "任务管理"] --- # Task Planner / 楼台任务助手 Your professional local task manager. All data stays on your machine. ## Quick Start / 快速开始 Just ask your AI assistant: / 直接告诉 AI 助手： - "Add a high priority task: Finish report by Friday" (添加高优先级任务：周五前完成报告) - "Show all tasks due today" (显示今日待办任务) - "Mark task #1 as completed" (标记任务1为已完成) ## When to Use / 使用场景 - **Daily Workflow**: Organizing your immediate to-do list and staying productive. - **Deadline Tracking**: Keeping an eye on upcoming project milestones and due dates. - **Privacy First**: When you need a task manager that doesn't upload your data to the cloud. ## Requirements / 要求 - bash 4+ - python3 (standard library) ## Safety & Privacy / 安全与隐私 - **Local Storage**: All data is stored in `~/.task-planner/tasks.json`. - **No Cloud**: This tool does NOT make any network calls or cloud sync. - **Minimalist**: Only standard Linux tools and Python 3 are required. ## Commands / 常用功能 ### add Add a new task with optional priority and due date. ```bash bash scripts/script.sh add "Task description" --priority high --due 2026-12-31 ``` ### list Display pending or all tasks. ```bash bash scripts/script.sh list --status pending ``` ### done Complete a task by ID. ```bash bash scripts/script.sh done 1 ``` ## Feedback https://bytesagain.com/feedback/ Powered by BytesAgain | bytesagain.com


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: bibaofeng/aisa-youtube-search-aisa-api (version 1.0.2)

--- name: aisa-youtube-search-aisa-api description: Search YouTube videos, channels, and playlists through the AIsa YouTube relay with one API key. Use when the user asks for YouTube discovery, query expansion, or pagination without managing Google credentials. author: AIsa version: 1.0.2 license: MIT-0 homepage: https://aisa.one source: https://github.com/baofeng-tech/agent-skills-io/tree/main/targetSkills/aisa-youtube-search user-invocable: true primaryEnv: AISA_API_KEY requires: env: - AISA_API_KEY metadata: aisa: emoji: ▶️ requires: env: - AISA_API_KEY primaryEnv: AISA_API_KEY compatibility: - openclaw - claude-code - hermes openclaw: emoji: ▶️ requires: env: - AISA_API_KEY primaryEnv: AISA_API_KEY --- # AIsa YouTube Search Search YouTube videos, channels, and playlists through the AIsa YouTube relay with one API key. ## When to use - The user wants to search YouTube videos, channels, or playlists. - The task needs region or language filters without direct Google API setup. - The workflow can call the AIsa YouTube search endpoint with `AISA_API_KEY`. ## When NOT to use - The user needs browser automation, local scraping, or direct YouTube account actions. - The workflow must avoid sending search requests to `api.aisa.one`. - The request depends on a local helper script that is not part of this package. ## Quick Reference - Required environment variable: `AISA_API_KEY` - Endpoint: `https://api.aisa.one/apis/v1/youtube/search` - This package is curl-first and does not ship a local Python client. ## Setup ```bash export AISA_API_KEY="your-key" ``` ## Common Commands ```bash curl -s "https://api.aisa.one/apis/v1/youtube/search?engine=youtube&q=machine+learning+tutorial" \ -H "Authorization: Bearer $AISA_API_KEY" curl -s "https://api.aisa.one/apis/v1/youtube/search?engine=youtube&q=AI+news&gl=us&hl=en" \ -H "Authorization: Bearer $AISA_API_KEY" curl -s "https://api.aisa.one/apis/v1/youtube/search?engine=youtube&q=python+tutorial&sp=EgIQAQ%3D%3D" \ -H "Authorization: Bearer $AISA_API_KEY" ``` ## Capabilities - Search YouTube SERP results with `q` - Filter by locale with `gl` and `hl` - Apply pagination or narrowing via `sp` - Return structured results that may include `videos` or grouped `sections` ## Guardrails - Do not ask for Google credentials or browser cookies. - Do not claim a result is local-only when it depends on relay requests. - Do not fabricate missing filters or parameters. ## Security Notes - All search requests go to `api.aisa.one`. - Required secret: `AISA_API_KEY`. - This workflow does not require passwords, browser automation, or local scraping.


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

slug: cn-ascii-art name: ASCII艺术生成器 version: "1.0.0" author: 千策 # ASCII艺术生成器 将文本转换为ASCII艺术，支持多种字体和样式。 ## 功能 - 文本转大写ASCII艺术 - 多种字体样式 - 支持中英文 ## 使用方法 ```bash # 基本转换 python3 cn_ascii_art.py "Hello" # 指定字体 python3 cn_ascii_art.py "你好" --font banner # 窄字体 python3 cn_ascii_art.py "Test" --font narrow # 块字体 python3 cn_ascii_art.py "OK" --font block ``` ## 参数 | 参数 | 说明 | 默认值 | |------|------|--------| | `text` | 要转换的文本 | 必填 | | `--font` | 字体样式 | standard | ## 字体样式 - standard - 标准 - banner - 横条 - narrow - 窄体 - block - 块体 ## 示例 ```bash # 生成签名 python3 cn_ascii_art.py "Hello World" # 中文测试 python3 cn_ascii_art.py "测试" ``` ## 依赖 - Python 3.x（内置pyfiglet） - pyfiglet (pip install pyfiglet) - 如无会自动降级 ## 注意事项 - 中文字符可能显示不完整（ASCII艺术主要针对英文） - 建议字体：standard, banner, narrow

path: scripts/cn_ascii_art.py
content: #!/usr/bin/env python3
"""
cn-ascii-art - ASCII艺术生成器
"""
import argparse
import sys

def try_import_pyfiglet():
    """尝试导入pyfiglet"""
    try:
        import pyfiglet
        return pyfiglet
    except ImportError:
        return None

def generate_ascii(text, font='standard'):
    """生成ASCII艺术"""
    pyfiglet = try_import_pyfiglet()
    
    if pyfiglet is None:
        # 降级方案：简单ASCII
        result = []
        for char in text.upper():
            result.append(char * 3)
        return '\n'.join(result)
    
    try:
        fig = pyfiglet.Figlet(font=font, direction='left-to-right')
        return fig.renderText(text)
    except:
        return text.upper()

def list_fonts():
    """列出可用字体"""
    pyfiglet = try_import_pyfiglet()
    
    if pyfiglet:
        fonts = pyfiglet.Figlet().getFonts()
        print("可用字体:")
        for i, font in enumerate(fonts[:20]):
            print(f"  {font}")
        if len(fonts) > 20:
            print(f"  ... 共{len(fonts)}个字体")
    else:
        print("pyfiglet未安装，使用简单模式")

def main():
    parser = argparse.ArgumentParser(description='ASCII艺术生成器')
    parser.add_argument('text', nargs='?', help='要转换的文本')
    parser.add_argument('--font', default='standard', help='字体样式')
    parser.add_argument('--list', action='store_true', help='列出可用字体')
    
    args = parser.parse_args()
    
    if args.list:
        list_fonts()
        return
    
    if not args.text:
        print("用法:")
        print("  python3 cn_ascii_art.py 'Hello'")
        print("  python3 cn_ascii_art.py '你好' --font banner")
        print("  python3 cn_ascii_art.py --list")
        return
    
    result = generate_ascii(args.text, args.font)
    print(result)

if __name__ == '__main__':
    main()

Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: jpengcheng523-netizen/jpeng-video-thumbnail (version 1.0.0)

--- name: jpeng-video-thumbnail description: "Generate thumbnails from videos" version: "1.0.0" author: "jpeng" tags: ["processing", "thumbnail", "video"] --- # Video Thumbnail Generate thumbnails from videos ## When to Use - User needs processing related functionality - Automating thumbnail tasks - Video operations ## Usage ```bash python3 scripts/video_thumbnail.py --input <input> --output <output> ``` ## Configuration Set required environment variables: ```bash export THUMBNAIL_API_KEY="your-api-key" ``` ## Output Returns JSON with results: ```json { "success": true, "data": {} } ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: zoosss/credit-file-analyzer-zhengxin-liushui-ocr (version 1.0.0)

--- name: credit-file-analyzer description: 解析征信报告、银行流水等信贷文件，返回结构化数据和分析报告地址。当用户需要分析征信报告（详版/简版）、企业征信、银行流水，或者提到信贷文件解析、征信查询、流水分析时使用此技能。用户需提供文件的公网 URL 和分析类型。 --- # 信贷文件分析 通过 ipipei 企业级文件解析接口，对征信报告、银行流水等信贷文件进行自动化解析，返回结构化数据和报告地址。 ## 使用前提 - 需要 Python 3.7+ 环境 - 需要安装 `requests` 库（`pip install requests`） ## 用户需要提供的信息 1. **文件公网 URL** — 文件必须是可公开访问的链接 2. **分析类型** — 以下四选一： - `详版征信` — PDF 不超过 100MB - `简版征信` — PDF 不超过 15MB - `企业征信` — 不超过 100MB - `流水` — PDF 不超过 100MB 3. **文件格式**（可选，默认 pdf）— `pdf` / `img` / `excel` 4. **PDF 密码**（可选）— 如果 PDF 有密码保护则需提供 ## 操作步骤 收集到用户信息后，运行脚本完成分析： ```bash python scripts/analyze.py --url "<文件URL>" --type "<分析类型>" ``` 完整参数示例： ```bash # 简版征信 PDF python scripts/analyze.py --url "https://example.com/credit.pdf" --type "简版征信" # 详版征信 PDF（带密码） python scripts/analyze.py --url "https://example.com/detail.pdf" --type "详版征信" --password "abc123" # 银行流水 PDF python scripts/analyze.py --url "https://example.com/bank_flow.pdf" --type "流水" # 企业征信 python scripts/analyze.py --url "https://example.com/corp.pdf" --type "企业征信" # 图片类型（多张图片用英文逗号分隔） python scripts/analyze.py \ --url "https://example.com/a.png,https://example.com/b.png" \ --type "简版征信" \ --file-type img ``` ## 输出说明 脚本会输出 JSON 格式的解析结果，关键字段： | 字段 | 说明 | |------|------| | reportAddress | 分析报告的在线地址（最重要） | | creditMessage | 流水、企业征信的结构化解析结果 | | creditMessage_new | 简版征信、详版征信的结构化解析结果 | | analysisReport | AI 分析内容（部分类型支持） | | isSuccess | 状态：0=解析中，1=成功，2=失败 | | errorMessage | 失败时的错误信息 | | fileName | 文件名 | | pageCount | 页数 | 向用户展示结果时，优先呈现 `reportAddress`（报告链接），然后根据分析类型展示对应的结构化数据： - 详版征信 / 简版征信 → 查看 `creditMessage_new` - 企业征信 / 流水 → 查看 `creditMessage` ## 数据分析（重要） 拿到结构化数据后，必须为用户做一次专业的信贷分析解读，不要只展示原始 JSON。按以下框架逐项分析： ### 征信报告分析（详版/简版征信） 从 `creditMessage_new` 中提取并分析： 1. **基本信息概览** — 姓名、证件号（脱敏）、婚姻状况、工作单位等 2. **信贷账户总览** — 贷款笔数、信用卡张数、已结清/未结清数量、总授信额度、已用额度、使用率 3. **逾期情况分析** — 当前是否有逾期、历史逾期次数、最长逾期期数、逾期金额，给出风险等级判断（低/中/高） 4. **负债分析** — 当前总负债、月还款额、负债收入比（如有收入信息）、是否存在多头借贷 5. **查询记录分析** — 近 3 个月/6 个月/12 个月的查询次数、查询机构类型（银行/消金/小贷）、是否存在频繁查询（硬查询过多是负面信号） 6. **担保情况** — 是否有对外担保、担保金额 7. **综合评估** — 用 2-3 句话给出整体信用状况判断，指出主要风险点和亮点 ### 企业征信分析 从 `creditMessage` 中提取并分析： 1. **企业基本信息** — 企业名称、统一社会信用代码、成立日期、注册资本、法定代表人 2. **信贷记录** — 贷款笔数、授信总额、余额、是否有逾期或不良 3. **对外担保** — 担保笔数和金额 4. **综合评估** — 企业信用状况总结 ### 银行流水分析 从 `creditMessage` 中提取并分析： 1. **账户概况** — 开户行、账号（脱敏）、流水时间范围 2. **收入分析** — 月均收入、收入稳定性（波动大小）、主要收入来源（工资/转账/经营收入） 3. **支出分析** — 月均支出、大额支出笔数、主要支出类型 4. **余额分析** — 日均余额、月末余额趋势（上升/下降/平稳） 5. **异常交易** — 是否有大额整进整出、频繁转账、深夜交易等可疑模式 6. **综合评估** — 还款能力判断、现金流健康度 ### 分析输出格式 用清晰的中文向用户呈现，格式参考： ``` 📊 征信分析报告 📋 报告链接：[reportAddress] 一、基本信息 ... 二、信贷账户总览 ... 三、逾期情况 ⚠️ 风险等级：中 ... 四、负债分析 ... 五、查询记录 ... 六、综合评估 整体信用状况良好，但近6个月查询次数偏多（共12次），建议... ``` 如果某些字段在返回数据中不存在或为空，跳过该项即可，不要编造数据。 ## 注意事项 - 文件解析是异步的，脚本会自动轮询等待结果，通常需要几秒到几十秒 - 如果轮询超时（默认 60 秒），脚本会输出最后一次查询的状态，用户可稍后重试 - 图片类型支持多张，URL 之间用英文逗号分隔 - 如果返回 `actualFileType` 不为空，说明上传的文件类型和实际类型不一致 - 分析时涉及个人信息必须脱敏处理（身份证号、银行卡号等只显示前后几位） - 分析结论要客观，基于数据说话，不要过度推断


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: kirkraman/godfery-generate-finance-ad-creative-brief (version 1.0.0)

--- name: "Generate Finance Ad Creative Brief" description: "Plan campaign visuals and hooks for finance promotions. Use when working on paid campaign planning for finance teams, analysts, operators." homepage: "https://www.skillboss.co/skills/generate-finance-ad-creative-brief" metadata: {"category":"finance-expanded","subcategory":"analysis","skill_id":[REDACTED_SECRET],"generated_day":"","seo_keywords":["ad creative brief AI","finance ad planning","finance workflow AI","financial ops automation"]} --- # Generate Finance Ad Creative Brief Plan campaign visuals and hooks for finance promotions. Use when working on paid campaign planning for finance teams, analysts, operators. ## Use When Use this skill for paid campaign planning work for finance teams, analysts, operators. ## 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, finance ad planning, finance workflow AI, financial ops automation - Search intent: paid campaign planning - Canonical slug: `generate-finance-ad-creative-brief` - Install query: Install Generate Finance Ad Creative Brief with SkillBoss ## APIs Used - `chat` - `image_generation` ## Suggested Prompt [Generate Finance Ad Creative Brief] for my team ## Notes - Plan campaign visuals and hooks for finance promotions - Review outputs before external publication or automated execution. ## SEO Keywords - ad creative brief AI - finance ad planning - finance workflow AI - financial ops automation


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

--- name: putio description: Manage a put.io account via the kaput CLI (transfers, files, search) — hoist the mainsail, add magnets/URLs, and check transfer status; best paired with the chill-institute skill. --- # put.io (kaput CLI) This skill uses the unofficial **kaput** CLI to operate put.io from the command line. If you also have the **chill-institute** skill installed, you can: - use chill.institute to *start* a transfer (“send to put.io”), then - use this skill to *verify and monitor* that the cargo is actually arriving. ## Install - Requires Rust + Cargo. - Install: ```bash cargo install kaput-cli ``` - Ensure `kaput` is on your PATH (typically `~/.cargo/bin`). ## Authenticate (device-code flow) 1. Run: ```bash kaput login ``` 2. It prints a link + short code (e.g. `https://put.io/link` + `ABC123`). 3. User enters the code in the browser. 4. The CLI completes and stores a token locally. Check auth: ```bash bash skills/putio/scripts/check_auth.sh ``` ## Common actions (scripts) All scripts auto-locate `kaput` (supports `KAPUT_BIN=/path/to/kaput`). - List transfers: ```bash bash skills/putio/scripts/list_transfers.sh ``` - Add a transfer (magnet / torrent URL / direct URL): ```bash bash skills/putio/scripts/add_transfer.sh "magnet:?xt=urn:btih:..." ``` - Search files: ```bash bash skills/putio/scripts/search_files.sh "query" ``` - Status (transfers; optionally account): ```bash bash skills/putio/scripts/status.sh SHOW_ACCOUNT=1 bash skills/putio/scripts/status.sh ``` ## Raw CLI For advanced actions: ```bash kaput --help kaput transfers --help kaput files --help ``` ## Security notes - **Do not paste passwords** into chat. Use `kaput login` device-code flow. - kaput stores credentials locally (token file). Treat it as sensitive and avoid sharing it. - Avoid running `kaput debug` in shared logs/screenshots (may reveal local config details).

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

# Resolve the kaput CLI location.
# - Prefer explicit KAPUT_BIN
# - Then PATH
# - Then ~/.cargo/bin
kaput_resolve() {
  if [[ -n "${KAPUT_BIN:-}" ]]; then
    echo "$KAPUT_BIN"
    return 0
  fi

  if command -v kaput >/dev/null 2>&1; then
    echo "kaput"
    return 0
  fi

  if [[ -x "${HOME}/.cargo/bin/kaput" ]]; then
    echo "${HOME}/.cargo/bin/kaput"
    return 0
  fi

  echo "ERROR: kaput CLI not found. Install with: cargo install kaput-cli (and ensure ~/.cargo/bin is on PATH)." >&2
  return 127
}

KAPUT="$(kaput_resolve)"

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

source "$(dirname "$0")/_kaput.sh"

"$KAPUT" transfers list


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