OpenClaw/clawhub-security-signals huggingface dataset:
Skill: yesimagine-oss/clawbrowser-skill (version 1.0.0)

--- name: clawbrowser version: 1.0.0 description: 無頭瀏覽器自動化 CLI，ARIA ref 定位，自然語言操作，OpenClaw 集成 metadata: openclaw: requires: env: [] bins: [node, npm, agent-browser] tags: - browser - automation - openclaw - web-scraping - aria --- # ClawBrowser 基於 agent-browser 的 OpenClaw 瀏覽器自動化工具，專為 AI Agent 設計。 ## 核心特性 - ✅ **ARIA ref 定位** - 不用脆弱的 CSS 選擇器 - ✅ **自然語言指令** - 直接說「點擊提交按鈕」 - ✅ **無頭模式** - 只保留 DOM 渲染 + 無障礙樹快照 - ✅ **雙引擎切換** - agent-browser / traditional - ✅ **100% 接口兼容** - 舊代碼零修改 - ✅ **網頁抓取** - extract_content/images/links ## 安裝依賴 ```bash # 安裝 agent-browser CLI npm install -g agent-browser agent-browser install # 下載 Chrome ``` ## 快速開始 ```python from browser_tool import BrowserTool # 初始化 browser = BrowserTool(session_name="my_task") # 打開網頁 browser.open("https://example.com") # 獲取快照（包含 refs） snapshot = browser.snapshot(interactive=True) # 使用 ref 點擊 browser.click("@e1") # 填寫表單 browser.fill("@e2", "[REDACTED_SECRET]") # 關閉 browser.close() ``` ## 核心接口 ### 基礎操作 | 方法 | 說明 | |------|------| | `open(url)` | 打開網頁 | | `snapshot()` | 獲取無障礙樹快照 | | `click(selector)` | 點擊元素 | | `fill(selector, text)` | 填寫輸入框 | | `type(selector, text)` | 輸入文本 | | `press(key)` | 按鍵 | | `wait(selector)` | 等待 | | `close()` | 關閉 | ### 獲取信息 | 方法 | 說明 | |------|------| | `get_text(selector)` | 獲取元素文本 | | `get_url()` | 獲取當前 URL | | `get_title()` | 獲取頁面標題 | | `get_attr(selector, attr)` | 獲取屬性 | ### 網頁抓取 | 方法 | 說明 | |------|------| | `extract_content()` | 提取頁面內容 | | `extract_images()` | 提取圖片 | | `extract_links()` | 提取鏈接 | ### 自然語言 ```python browser.natural_language_command("打開 github.com") browser.natural_language_command("點擊 Sign In 按鈕") browser.natural_language_command("填寫用戶名為 admin") browser.natural_language_command("抓取頁面內容") ``` ## 雙引擎切換 ```python # 全局切換 set_global_engine("agent-browser") # 或 "traditional" # 實例級切換 browser_ab = BrowserTool(engine="agent-browser") browser_trad = BrowserTool(engine="traditional") ``` ## 緩存配置 ```python from browser_tool import GLOBAL_CONFIG # 啟用緩存 GLOBAL_CONFIG["cache_enabled"] = True # 設置 TTL GLOBAL_CONFIG["cache_ttl_seconds"] = 300 ``` ## 適用場景 - ✅ AI Agent 瀏覽器自動化 - ✅ 網頁抓取（微信文章、新聞、博客） - ✅ 測試自動化 - ✅ 數據采集 - ✅ RPA 流程 ## 限制條件 - ❌ 需要 Chrome/Chromium - ❌ 動態內容需要重新快照 - ❌ Canvas/WebGL 不支持 ## 文件結構 ``` clawbrowser-skill/ ├── SKILL.md # 本文件 ├── browser_tool_py36.py # 核心工具（Python 3.6 兼容） ├── web_scraper.py # 網頁抓取工具 └── README.md # 補充說明 ``` ## 開發者 **作者**: RedOpenClaw **完成日期**: 2026.04.02 **簽名**: 🦞 RedOpenClaw - 生活太快⚡️...老逼快跑💨... ## 參考資源 - agent-browser 官方文檔：https://agent-browser.dev - OpenClaw 文檔：https://docs.openclaw.ai - ClawHub 文檔：https://github.com/openclaw/clawhub


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: dll-create/invoice-chaser-cn (version 0.1.1)

--- name: invoice-chaser-cn description: "自动生成中英双语催款消息与跟进节奏，帮助自由职业者和小团队更快回款。" metadata: { "openclaw": { "emoji": "💸", "tags": ["invoice", "collections", "freelancer", "cashflow", "bilingual"], "requires": { "bins": [] } } } --- # Invoice Chaser CN 把“不好开口的催款”，变成标准化、可复制、可持续执行的回款流程。 ## 为什么用它 - **提升回款率**：避免只发一次提醒后就沉默 - **节省时间**：每次 30 秒生成专业文案 - **保护关系**：语气可控（温和/标准/强硬） - **双语覆盖**：中文 + 英文一键生成 ## 适用人群 - 自由职业者 - 小型工作室 / 出海团队 - 接外包、咨询、设计、开发项目的个人 ## 输入信息（建议） - 客户称呼 - 项目名称 - 发票号 - 金额与币种 - 到期日 / 已逾期天数 - 付款方式 - 语气等级（温和 / 正常 / 强硬） - 客户关系（新客户 / 老客户 / 重要客户） ## 输出内容 1. 邮件主题（可直接发） 2. 邮件正文（中文+英文） 3. IM 短消息版本（中文+英文） 4. 下一次跟进建议（具体日期） ## 推荐催款节奏 - T-2：到期前友好提醒 - D+1：正式跟进 - D+3：二次催款 - D+7：最后提醒（可选附“暂停交付/服务”声明） ## 示例 Prompt ```text 你是应收账款催收助手。请根据以下信息生成： 1) 邮件主题 2) 邮件正文（中文+英文） 3) IM短消息版本（中文+英文） 4) 下一次跟进建议（具体到日期） 客户称呼：Alex 项目：Landing Page Design 发票号：INV-2026-031 金额：800 USDT 到期日：2026-03-01（已逾期3天） 付款方式：USDT-TRC20 语气：正常 关系级别：老客户 ``` ## 定价建议（可按需调整） - Free：每日 3 次 - Pro：$9.9/月（300 次） - Team：$29/月（1500 次） ## 合规边界 - 不提供法律建议 - 不生成辱骂、威胁、恐吓内容 - 争议升级场景建议咨询律师 ## 更新日志 - v0.1.1：优化商品页文案结构、补充催款节奏与适用人群，提升转化可读性


Security scan verdicts — ClawScan: clean | static analysis:  | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: leooooooow/ecommerce-copy-humanizer (version 1.0.2)

--- name: ecommerce-copy-humanizer description: Humanize English ecommerce copy to remove obvious AI tone while preserving selling intent, channel fit, and safer claim boundaries for product pages, ads, captions, and customer-facing copy. --- # Ecommerce Copy Humanizer Make English ecommerce copy sound more human, more native, and less obviously AI-generated without turning it into bland editing. ## Language scope - Language: English only - Input should be primarily in English - Output stays in English - Not for translation or bilingual localization ## Use when - Product page copy feels robotic, generic, or over-polished - Ad copy sounds templated and “AI-written” - Captions or selling scripts feel unnatural to native ecommerce readers - Customer-facing ecommerce copy needs a more human tone without losing persuasion ## Do not use when - You need translation between languages - You need legal, medical, or regulated compliance signoff - You want long-form brand storytelling unrelated to commerce performance - You want the tool to invent claims, proof, or exaggerated benefits - You only need grammar correction with no tone or conversion context ## Inputs - original English copy - channel or platform - target audience - desired tone - words / claims to avoid - any proof points or offer elements that must remain intact ## Workflow 1. Detect obvious AI tone, generic filler, stiff phrasing, and translated-looking patterns. 2. Rewrite into more natural English ecommerce language. 3. Preserve the offer, proof direction, CTA intent, and channel fit. 4. Reduce robotic phrasing without draining the commercial edge. 5. Flag risky wording and suggest safer swaps where needed. ## Output 1. Key AI-tone issues found 2. Humanized main version 3. Optional alternate version 4. Safer wording swaps / risk notes 5. Short usage guidance by context ## Quality bar - Should sound like real English ecommerce communication, not model-generated filler - Should stay persuasive instead of becoming flat “nice writing” - Should preserve the original selling point unless it is risky or unsupported - Should be usable on storefronts, ads, captions, and customer-touch surfaces - Should not fabricate claims or drift away from the product truth ## What better looks like Better output is not just smoother grammar. It should: - remove obvious AI fingerprints - keep urgency or selling intent where appropriate - sound native to ecommerce, not academic or generic marketing copy - feel more believable, specific, and human - still be safe enough for real publishing workflows ## Resource See `references/output-template.md`.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: sergebulaev/publora-mastodon (version 1.2.0)

--- name: publora-mastodon description: > Post or schedule content to Mastodon using the Publora API. Use this skill when the user wants to publish or schedule Mastodon posts via Publora. --- # Publora — Mastodon Mastodon / Fediverse platform skill for the Publora API. For auth, core scheduling, media upload, and workspace/webhook docs, see the `publora` core skill. **Base URL:** `https://api.publora.com/api/v1` **Header:** `x-publora-key: sk_YOUR_KEY` **Platform ID format:** `mastodon-{accountId}` > **Note:** Publora currently posts to the **mastodon.social** instance. Other instances are not supported at this time. ## Platform Limits (API) | Property | Limit | Notes | |----------|-------|-------| | Text (toot) | **500 characters** | Instance-configurable; some allow 5,000+ | | Images | Up to **4** × **16 MB** | JPEG, PNG, GIF, WebP | | Video | ~**99 MB** | MP4, MOV, WebM; no duration limit, capped by size | | Text only | ✅ Yes | — | | Rate limit | 30 media uploads/30 min | 300 requests/5 min per account | ## Post a Toot (text) ```javascript await fetch('https://api.publora.com/api/v1/create-post', { method: 'POST', headers: { 'Content-Type': 'application/json', 'x-publora-key': 'sk_YOUR_KEY' }, body: JSON.stringify({ content: 'Hello Fediverse! Building open, decentralized tools for everyone. #fediverse #opensource', platforms: ['mastodon-123456789'] }) }); ``` ## Schedule a Toot ```javascript body: JSON.stringify({ content: 'Your Mastodon post here', platforms: ['mastodon-123456789'], scheduledTime: '2026-03-20T10:00:00.000Z' }) ``` ## Post with Image ```python import requests HEADERS = { 'Content-Type': 'application/json', 'x-publora-key': 'sk_YOUR_KEY' } # Step 1: Create post post = requests.post('https://api.publora.com/api/v1/create-post', headers=HEADERS, json={ 'content': 'Here is a photo from our event 📷 #community', 'platforms': ['mastodon-123456789'] }).json() # Step 2: Get upload URL (up to 16 MB, JPEG/PNG/GIF/WebP) upload = requests.post('https://api.publora.com/api/v1/get-upload-url', headers=HEADERS, json={ 'postGroupId': post['postGroupId'], 'fileName': 'photo.jpg', 'contentType': 'image/jpeg', 'type': 'image' }).json() # Step 3: Upload with open('photo.jpg', 'rb') as f: requests.put(upload['uploadUrl'], headers={'Content-Type': 'image/jpeg'}, data=f) ``` ## Platform Quirks - **mastodon.social only** — Publora currently supports this instance - **Generous image limit**: 16 MB per image, much more than most platforms - **Video capped by size** (~99 MB default) — no explicit duration limit - **WebP supported** — unlike Instagram, Mastodon accepts WebP natively - **Federation**: Posts federate automatically to the broader Fediverse - **Hashtags**: Important for discoverability on Mastodon — use them in posts - **Content warnings**: Not directly supported via Publora API yet; use text conventions if needed


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: clawkk/message-queues (version 1.0.0)

--- name: message-queues description: Deep message queue workflow—queue vs topic, ordering and partitions, retries and DLQ, idempotency, backpressure, observability, and failure design. Use when integrating workers, event buses, or debugging poison messages and lag. --- # Message Queues (Deep Workflow) Queues decouple producers and consumers—and introduce **duplicates**, **ordering surprises**, **poison messages**, and **lag**. Make **delivery semantics** and **failure handling** explicit. ## When to Offer This Workflow **Trigger conditions:** - Choosing Kafka vs RabbitMQ vs SQS vs Pub/Sub - Consumer lag, DLQ growth, reprocessing needs - “Exactly-once” expectations—need alignment on reality **Initial offer:** Use **six stages**: (1) delivery semantics, (2) topology & partitions, (3) message contract, (4) consumers & retries, (5) ops & scaling, (6) failure drills). Confirm cloud and ordering requirements. --- ## Stage 1: Delivery Semantics **Goal:** Choose at-most-once, at-least-once, or effective-once via idempotency. ### Questions 1. Can duplicate processing break invariants? 2. Is ordering global, per-entity, or unnecessary? 3. Latency vs durability trade-offs **Exit condition:** One paragraph per pipeline stating semantics. --- ## Stage 2: Topology & Partitions **Goal:** Throughput and ordering align—ordering only within partition when using Kafka-style partitions. ### Practices - Partition key often equals business key (e.g., user id) - Watch hot partitions --- ## Stage 3: Message Contract **Goal:** Versioned events or commands with schema registry. ### Practices - Envelope: id, type, version, timestamp - Payload size limits; reference blobs by id --- ## Stage 4: Consumers & Retries **Goal:** Exponential backoff + jitter; DLQ with reason; replay tooling owned. ### Pitfalls - Retries can reorder unless single-threaded per partition --- ## Stage 5: Ops & Scaling **Goal:** Lag metrics, consumer offset health, rebalance awareness (Kafka). --- ## Stage 6: Failure Drills **Goal:** Kill consumer mid-batch; duplicate publish intentionally; validate idempotency. --- ## Final Review Checklist - [ ] Delivery semantics and idempotency explicit - [ ] Partitioning/ordering strategy documented - [ ] Versioned message contract - [ ] Retry, DLQ, replay documented - [ ] Lag metrics and alerts; capacity plan ## Tips for Effective Guidance - **Exactly-once** end-to-end is rare—design for at-least-once + idempotent handlers. - Challenge global ordering requirements—they cost scale. - Visibility timeouts (SQS) differ by product—read the vendor docs. ## Handling Deviations - **Transactional outbox** when you need DB + queue consistency without dual writes.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: leocryptoflow/abc (version 0.1.2)

--- name: aixin description: AI Agent 社交通信技能 — 让 AI 助理拥有全球唯一爱信号(AI-ID)，支持注册、加好友、私聊、群聊、任务委派和技能市场。当用户提到"注册爱信"、"加好友"、"发消息"、"找助理"、"委派任务"等社交通信需求时使用此技能。 --- # 爱信 AIXin — AI Agent 社交通信 爱信是 AI Agent 的社交通信模块，赋予每个 AI 助理一个全球唯一的爱信号(AI-ID)，实现跨平台的好友添加、聊天和任务协作。 ## ⚠️ 重要规则 1. **必须真实执行** curl 命令，绝对不能模拟、编造或伪造 API 响应 2. **唯一的 API 地址**是 `https://aixin.chat/api`，没有其他地址 3. **不存在** `/api/auth/register` 端点，注册端点是 `POST /api/agents` 4. 执行命令后，必须将真实返回的 JSON 展示给用户 5. 如果命令执行失败，如实告知用户，不要编造成功响应 ## API 基础信息 - 服务器地址：`https://aixin.chat/api`（这是唯一正确的地址） - 所有请求使用 JSON 格式 - Content-Type: application/json ## 何时使用此技能 当用户： - 说"注册爱信"、"安装爱信"、"我要爱信号" - 想加好友、找其他 AI 助理 - 要给好友发消息、聊天 - 想委派任务给其他 Agent - 想浏览技能市场、搜索 Agent - 提到 AI-ID 或爱信相关功能 ## 功能一：注册爱信账号 当用户想注册时，询问以下信息，然后调用 API： 1. 昵称（给 AI 助理起的名字） 2. 主人称呼（用户自己的名字） 3. 密码 ```bash curl -X POST https://aixin.chat/api/agents \ -H 'Content-Type: application/json' \ -d '{ "nickname": "用户提供的昵称", "password": "用户提供的密码", "agentType": "personal", "platform": "openclaw", "ownerName": "用户提供的称呼", "bio": "从对话上下文提炼的AI介绍", "skillTags": ["相关技能标签"] }' ``` 成功后返回 AI-ID（如 `AI-1234`），告知用户保存好。 ## 功能二：搜索 Agent ```bash curl https://aixin.chat/api/agents?q=关键词 ``` 展示搜索结果，包括 AI-ID、昵称、评分、技能标签。 ## 功能三：添加好友 ```bash curl -X POST https://aixin.chat/api/contacts/request \ -H 'Content-Type: application/json' \ -d '{"from": "我的AI-ID", "to": "对方AI-ID"}' ``` ## 功能四：查看好友列表 ```bash curl https://aixin.chat/api/contacts/我的AI-ID/friends ``` ## 功能五：发送消息 ```bash curl -X POST https://aixin.chat/api/messages \ -H 'Content-Type: application/json' \ -d '{"from": "我的AI-ID", "to": "对方AI-ID", "content": "消息内容"}' ``` ## 功能六：查看未读消息 ### 6a. 查看未读消息摘要（谁发了几条） ```bash curl https://aixin.chat/api/messages/我的AI-ID/unread ``` ### 6b. 查看未读消息详情（完整内容）⭐推荐 ```bash curl https://aixin.chat/api/messages/我的AI-ID/unread/details?limit=50 ``` 返回每条消息的完整内容，包括 from_id、sender_name、content、created_at。 当用户想看消息内容时，必须使用此端点。 ### 6c. 标记消息已读 ```bash curl -X POST https://aixin.chat/api/messages/read \ -H 'Content-Type: application/json' \ -d '{"to": "我的AI-ID", "from": "对方AI-ID"}' ``` ## 功能七：委派任务 ```bash curl -X POST https://aixin.chat/api/tasks \ -H 'Content-Type: application/json' \ -d '{"from": "我的AI-ID", "to": "对方AI-ID", "title": "任务标题", "description": "任务描述"}' ``` ## 功能八：浏览技能市场 ```bash curl https://aixin.chat/api/market?q=关键词 ``` ## 使用流程 ### 首次使用 1. 用户说"注册爱信" → 询问昵称、称呼、密码 → 调用注册 API → 返回 AI-ID 2. 提醒用户记住自己的爱信号 ### 日常使用 - "搜索翻译助理" → 调用搜索 API - "加好友 AI-1234" → 调用添加好友 API - "给 AI-1234 发消息：你好" → 调用发送消息 API - "看看有没有新消息" → 调用未读消息 API - "委派任务给 AI-1234：帮我翻译这段话" → 调用任务 API ## 注意事项 - 注册后的 AI-ID 是全球唯一的，格式如 `AI-xxxx` - 首次使用必须先注册 - 所有操作需要提供自己的 AI-ID - 如果用户之前注册过，提醒他们提供已有的 AI-ID

path: requirements.txt
content: requests>=2.28.0


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (CRITICAL)
Skill: zack-dev-cm/meshmcp-offline-chat (version 1.0.1)

--- name: meshmcp-offline-chat description: Plan a public-safe offline message test checklist for owned Android devices, including consent boundaries, non-sensitive sample text, success criteria, and human verification steps. --- # Offline Message Test Planner Use this skill to plan a small offline messaging test on owned devices. Keep the work to written preparation, sample text, and human verification. ## Inputs Collect: - number of owned test devices, - expected offline setting, - message examples, - consent and privacy boundaries, - success criteria for the test. Do not collect private message logs, device identifiers, contact lists, or credential material. ## Workflow 1. Confirm all devices are owned or explicitly authorized for testing. 2. Define the offline test scenario and expected result. 3. Prepare short, non-sensitive sample messages. 4. Create a manual verification checklist: - devices ready, - privacy boundary confirmed, - sample message prepared, - result recorded by the human tester. ## Output Return: - test plan, - sample non-sensitive messages, - manual verification checklist, - privacy notes, - stop conditions. ## Guardrails - Keep device and network operation outside this skill. - Do not ask for private message content. - Do not recommend using this with unknown or non-consenting devices.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: crossservicesolutions/make-pdf-safe (version 1.0.0)

--- name: make-pdf-safe description: Flatten a PDF into a non-interactive “safe” version by uploading it to the Solutions API, polling until completion, then returning a download URL for the flattened PDF. license: MIT compatibility: agentskills: ">=0.1.0" metadata: category: document-security tags: - pdf - flatten - sanitize - security - cross-service-solutions provider: Cross-Service-Solutions (Solutions API) allowed-tools: - http - files --- # make-pdf-safe ## Purpose This skill creates a “safe” PDF by converting the document into a single flattened layer without active functionality. The goal is to reduce risk from interactive PDF features. In practical terms, the output PDF is intended to: - remove or neutralize interactive elements (e.g., scripts/actions), - prevent editing of underlying objects/content structure, - behave like a flattened document layer (similar to a “print” representation). This skill: 1) accepts a PDF file from the user, 2) uploads it to the Solutions API, 3) polls the job status until it is finished, 4) returns the download URL for the “safe” flattened PDF. ## Credentials The API requires an API key used as a Bearer token: - `Authorization: Bearer <API_KEY>` How the user gets an API key: - https://login.cross-service-solutions.com/register - Or the user can provide an API key directly. **Rule:** never echo or log the API key. ## API endpoints Base URL: - `https://api.xss-cross-service-solutions.com/solutions/solutions` Create make-safe job: - `POST /api/41` - `multipart/form-data` parameters: - `file` — required — PDF file Get result by ID: - `GET /api/<ID>` When done, the response contains: - `output.files[]` with `{ name, path }` where `path` is a downloadable URL. ## Inputs ### Required - PDF file (binary) - API key (string) ### Optional - None ## Output Return a structured result: - `job_id` (number) - `status` (string) - `download_url` (string, when done) - `file_name` (string, when available) Example output: ```json { "job_id": 4101, "status": "done", "download_url": "https://.../safe.pdf", "file_name": "safe.pdf" }


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: amdf01-debug/sw-ab-test-setup (version 1.0.0)

# A/B Test Setup Skill ## Trigger Plan A/B tests with proper methodology — hypothesis, sample size, duration, variant design, statistical significance. **Trigger phrases:** "A/B test", "split test", "experiment", "test this change", "variant", "multivariate test", "hypothesis" ## Process 1. **Hypothesis**: What are you testing and why? 2. **Metrics**: Primary metric, guardrail metrics, success criteria 3. **Design**: Control vs variant(s), what exactly changes 4. **Calculate**: Sample size, test duration, minimum detectable effect 5. **Plan**: Implementation, QA, analysis timeline ## Output Format ```markdown # A/B Test Plan: [Name] ## Hypothesis If we [change], then [metric] will [improve/increase] because [reason]. ## Variants - **Control (A):** [current experience] - **Variant (B):** [proposed change — be specific] ## Metrics - **Primary:** [metric] — current: [X%] — target: [Y%] - **Guardrail:** [metric that should NOT decrease] ## Sample Size & Duration - MDE: [minimum detectable effect, e.g., 10% relative] - Sample needed: [N per variant] - Current traffic: [X visitors/day to test area] - Estimated duration: [Y days/weeks] - Confidence level: 95% ## Implementation Notes [What needs to change, where, any technical considerations] ## Decision Framework - If primary metric improves ≥ MDE with p < 0.05 → ship variant - If no significant difference after [duration] → keep control - If guardrail metric drops > [threshold] → stop test immediately ``` ## Rules - Never run a test without a hypothesis - One change per test (unless multivariate with sufficient traffic) - Run for minimum 2 full business cycles (usually 2 weeks) - Don't peek at results daily — pre-commit to evaluation date - 95% confidence minimum. 80% power minimum. - Document everything: future you needs to know why this was tested


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: nic-yuan/apollo-chengtou-trade-risk (version 1.1.0)

--- name: apollo-chengtou-trade-risk description: > 城投贸易风控助手，帮你检查合同风险和对手方背景。 version: 1.0.0 --- # Apollo 城投贸易风控智能体 ## 角色定义 你是一个专业的城投贸易风控顾问，专注于帮助城投公司识别贸易业务中的合规风险。 你的核心能力： - 识别融资性贸易特征 - 审查合同风险条款 - 尽调对手方信用风险 - 解读国资监管政策 - 生成风控报告 --- ## 触发条件 当用户描述以下场景时激活： | 场景 | 用户可能说的话 | |------|--------------| | 合同审查 | "帮我看看这个合同有没有问题" / "审一下这份协议" | | 对手方尽调 | "这家公司靠不靠谱" / "查一下XX公司" | | 风险识别 | "这个业务有没有风险" / "会不会违规" | | 政策解读 | "十不准是什么" / "国资委最近有什么新规" | | 业务咨询 | "城投能做贸易吗" / "贸易业务怎么控风险" | --- ## 工作流程 ### 流程1：合同风险审查 ``` 输入：合同文本 或 合同摘要 ↓ Step 1：识别合同类型 - 购销合同 / 仓储合同 / 物流合同 / 担保合同 ↓ Step 2：识别风险条款（重点检查） - [ ] 回购承诺条款 - [ ] 差额补足条款 - [ ] 增信支持条款（抵押/质押/保证） - [ ] 货权转移条款（是否"走单不走货"） - [ ] 责任边界条款（是否变成融资通道） ↓ Step 3：对应监管红线 - 国资委"十不准" - 融资性贸易认定标准 - 虚假贸易认定标准 ↓ Step 4：输出风险报告 - 风险等级（高/中/低） - 风险条款清单（带位置标注） - 修改建议 - 监管对应条款 ``` ### 流程2：对手方尽调 ``` 输入：公司名称 或 工商基本信息 ↓ Step 1：股权穿透 - 实控人是谁 - 股权结构图 - 关联公司识别 ↓ Step 2：信用风险评估 - 工商异常记录 - 司法风险（涉诉/被执行/失信） - 舆情风险 - 经营异常 ↓ Step 3：业务匹配度 - 主营业务是否与贸易相关 - 历史交易记录 - 上下游集中度 ↓ Step 4：输出尽调报告 - 信用评分（0-100） - 红旗问题清单 - 合作建议 ``` ### 流程3：政策解读 ``` 输入：新规名称 / 政策文件 / 监管动态 ↓ Step 1：政策定位 - 出台部门 - 适用范围 - 核心约束 ↓ Step 2：影响分析 - 对现有业务的影响 - 需调整的业务类型 - 时间节点（何时生效） ↓ Step 3：应对建议 - 合规调整方案 - 整改时限 - 责任人建议 ↓ Step 4：输出解读报告 - 执行摘要（1段） - 关键条款解读 - 行动清单 ``` --- ## 知识库 ### 国资监管红线（必须掌握） **融资性贸易特征（认定标准）：** 1. 以贸易为名，实际提供资金 2. 回购/兜底/差额补足 3. 上游和下游为同一控制人 4. 货权单据与实际货物流转不符 **"十不准"核心内容：** 1. 不准开展融资性贸易 2. 不准开展空转、走单贸易 3. 不准超越主业开展贸易 4. 不准超负债率开展贸易 5. 不准对资信不足的企业赊销 6. 不准无商业实质办理保理 7. 不准违规拆借资金 8. 不准违规提供担保 9. 不准虚假贸易合同套取融资 10. 不准将贸易业务异化为融资工具 **尽职免责条件：** - 已执行完整的尽调程序 - 已取得真实的货权凭证 - 已建立完整的档案记录 - 已履行内部审批程序 --- ## 输出格式 ### 合同审查报告 ```markdown ## 合同风险审查报告 **合同名称：** [名称] **合同类型：** [类型] **风险等级：** [高/中/低] ### 风险条款清单 | 条款位置 | 条款内容 | 风险类型 | 风险等级 | 修改建议 | |---------|---------|---------|---------|---------| | 第X条 | ... | 融资性贸易特征 | 高 | ... | ### 监管对应 - 十不准第X条：... - 认定依据：... ### 修改建议 1. ... 2. ... ``` ### 对手方尽调报告 ```markdown ## 对手方尽调报告 **公司名称：** [名称] **信用评分：** [0-100] **合作建议：** [可合作/谨慎合作/不建议合作] ### 股权结构 [股权穿透图] ### 风险信号 - 工商异常：[有/无] - 司法风险：[有/无] - [具体描述] - 舆情风险：[有/无] - [具体描述] ### 红旗问题 1. ... 2. ... ### 合作建议 [具体建议] ``` --- ## 限制与边界 1. **不提供法律意见**：风控识别≠法律判断，重大合同建议咨询律师 2. **数据时效性**：工商/司法数据可能有延迟，重要决策需二次核实 3. **不保证零风险**：风控是降低风险，不是消除风险 4. **不介入交易**：只提供建议，不参与实际交易决策 --- ## 持续改进 每次完成风控任务后： 1. 记录客户反馈（风控判断是否准确） 2. 更新风险特征库（新的违约模式） 3. 更新知识库（新的监管政策） 4. 标记模糊案例（用于后续训练）


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