OpenClaw/clawhub-security-signals huggingface dataset:
Skill: lifenglei/resume-optimizer-pro (version 1.0.0)

--- name: resume-optimizer description: 评估并优化简历内容，输出精美的 HTML 修改建议报告。当用户提到"修改简历"、"优化简历"、"简历评估"时使用。支持前端和后端岗位，Claude 以前端/后端架构师视角进行评估，让用户清楚知道哪些部分需要优化。 --- # 简历优化助手 ## 功能说明 此 Skill 帮助用户评估和优化 PDF 简历，生成精美的 HTML 修改建议报告。 - 输入：用户上传 PDF 简历文件 + 简历类型（前端/后端） - 输出：精美的 HTML 报告（直接浏览器打开即可查看） ## 使用方式 用户提供： 1. PDF 简历文件 2. 简历类型：前端 / 后端 ## 工作流程 1. 使用 pdfplumber 提取 PDF 简历中的文本内容 2. 将内容发送给 Claude 进行评估（根据前端/后端岗位，模拟架构师视角） 3. 生成精美的 HTML 报告，包含详细的修改建议 4. 用户直接用浏览器打开 HTML 文件查看 ## 评估维度 ### 前端简历评估要点（详见 references/frontend-evaluation.md） - 技术深度：技术栈深度和广度、项目复杂度、解决方案专业性 - 业务价值：项目成果量化程度、对业务影响程度 - 表达专业度：描述清晰度、用词准确性、逻辑连贯性 - 整体结构：模块完整性、排版美观度、信息有序性 - 亮点突出度：是否有吸睛亮点、亮点是否具体可衡量 ### 后端简历评估要点（详见 references/backend-evaluation.md） - 系统设计能力：架构设计深度、系统复杂度、解决方案专业性 - 高并发处理经验：大流量处理能力、性能优化成果、稳定性保障能力 - 业务价值：项目成果量化程度、对业务影响程度 - 表达专业度：描述清晰度、用词准确性、逻辑连贯性 - 整体结构：模块完整性、排版美观度、信息有序性 ## 输出格式 HTML 报告包含： - 整体评分（满分100）及评分等级 - 评分明细（格式、内容、技能栈等分项分数） - 格式评估（优点 + 问题） - 内容评估（优点 + 问题） - 技能栈分析 - 项目亮点与优化建议（每个项目包含：原始描述、亮点提炼、解决方案、价值体现、优化建议） - 整体优点与不足 - 整体优化建议 ## 注意事项 - HTML 报告使用现代精美的设计 - 报告可直接在浏览器中打开 - 评估结果基于 Claude API 返回，可能因模型版本不同略有差异


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: nexscope/shopify-dropshipping (version 1.0.0)

--- name: shopify-dropshipping description: "Dropshipping setup and scaling — supplier integration, automation, pricing strategy, customer experience" metadata: nexscope: category: shopify --- # Shopify Dropshipping Dropshipping setup and scaling — supplier integration, automation, pricing strategy, customer experience. ## Status 🔶 **Beta** — This skill provides expert guidance and actionable frameworks for Shopify sellers. ## Capabilities - Analyze your current Shopify store setup and identify optimization opportunities - Provide data-driven recommendations based on industry best practices - Generate step-by-step implementation plans tailored to your business stage - Compare tools, apps, and strategies with pros/cons analysis ## Install ```bash npx skills add nexscope-ai/eCommerce-Skills --skill shopify-dropshipping -g ``` ## Usage After installation, ask your AI assistant about shopify dropshipping topics. Example prompts: - "Help me with shopify dropshipping for my Shopify store" - "What are the best practices for shopify dropshipping?" - "Audit my current shopify dropshipping setup and suggest improvements" - "Create a step-by-step shopify dropshipping implementation plan" ## Output Structured analysis and recommendations including: - Current state assessment - Priority action items (quick wins → long-term) - Tool/app recommendations with cost comparison - Implementation timeline - KPIs to track ## Limitations - Provides strategic guidance, not direct Shopify API integration - Recommendations based on general best practices — always validate with your specific store data - For real-time analytics and automated optimization, check out [Nexscope](https://www.nexscope.ai/?co-from=skill) --- *Built by [Nexscope](https://www.nexscope.ai/?co-from=skill) — AI-powered e-commerce tools for sellers worldwide.*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: unisound-llm/unisound-lab-report-interpret (version 1.0.0)

--- name: clinic-lab-report-interpret description: 基础检查辅助解读。输入血常规/生化/尿常规/心电图等检查报告文本，识别异常指标，给出临床意义和随访建议（JSON + 自然语言摘要）。 metadata: { "openclaw": { "emoji": "🔬" } } --- # 基础检查辅助解读 概述 ---- 面向**社区诊所/基层卫生机构**医生，给定检查报告文本，本技能会： - 识别所有异常指标（含偏高/偏低及程度） - 结合参考范围给出每项异常的临床意义 - 标注需紧急关注的危急值 - 给出综合解读和随访/进一步检查建议 支持的检查类型（不限于）： - 血常规（CBC） - 生化全套（肝功、肾功、血糖、血脂、电解质等） - 尿常规 - 凝血功能 - 甲状腺功能 - 心电图（文字描述） 数据安全、隐私与伦理声明 ------------------------ - **最小必要原则**：仅处理解读所必需的检查结果；不要求包含患者姓名等身份信息。 - **严格脱敏**：发送前对可识别身份信息进行脱敏处理。 - **不做本地持久化**：仅在内存中短暂处理；**本次调用结束即销毁**。 - **医疗边界**：本技能输出为检查结果辅助解读，不替代临床判断；最终诊断须由执业医生确定。 输入格式 -------- 纯文本（UTF-8），包含检查报告内容，例如： ```text 患者：男，55岁，高血压病史8年 检查类型：血常规 + 生化 血常规： WBC 12.5×10⁹/L（↑，参考：4.0-10.0） RBC 4.2×10¹²/L（正常） HGB 130g/L（正常） PLT 280×10⁹/L（正常） 生化： ALT 65U/L（↑，参考：0-40） AST 45U/L（↑，参考：0-35） Cr 115μmol/L（正常） GLU 6.8mmol/L（↑，参考：3.9-6.1） ``` 也支持 JSON 格式（包含 `text`/`content`/`lab_report` 字段的对象）。 快速开始 -------- ```bash # 从 skills 目录运行 python3 clinic/primary-care-assist/lab-report-interpret/scripts/run.py \ --input data/clinic-lab-report/case-001.txt \ --appkey <your-appkey> # 保存输出到文件 python3 clinic/primary-care-assist/lab-report-interpret/scripts/run.py \ --input data/clinic-lab-report/case-001.txt \ --appkey <your-appkey> \ --output runs/clinic-lab-report/case-001.json ``` 参数说明 -------- - `--input PATH`：**必填**。检查报告文件路径（txt 或 json，UTF-8）。 - `--appkey STRING`：**必填**。调用内部医疗大模型的鉴权 key，由平台分配。 - `--output PATH`：输出文件路径（默认：打印到 stdout）。 - `--base URL`：内部大模型 base URL（默认：`https://maas-api.hivoice.cn/v1`）。 - `--model STRING`：模型名称（默认：`u1-insuremed`）。 - `--timeout SECONDS`：HTTP 超时秒数；`0` 表示一直等待（默认：0）。 - `--encoding STRING`：输入文件编码（默认：`utf-8`）。 输出约定 -------- 输出分两部分： **JSON 结构**： ```json { "exam_type": "血常规 + 生化", "overall_status": "轻度异常", "abnormal_items": [ { "item_name": "WBC（白细胞）", "value": "12.5×10⁹/L", "reference_range": "4.0-10.0×10⁹/L", "status": "偏高", "clinical_significance": "白细胞升高，提示可能存在感染或炎症反应", "urgency": "需关注", "suggestions": "结合临床症状，建议复查或行CRP、PCT等炎症指标" } ], "normal_items_summary": "RBC、HGB、PLT均在正常范围", "overall_interpretation": "WBC轻度升高，合并ALT、AST轻度升高，提示可能存在感染伴肝功能轻度受损，血糖略高于正常上限。建议进一步评估感染原因，注意肝功能动态变化。", "follow_up_suggestions": [ "1周后复查肝功能、血常规", "监测血糖，必要时行OGTT" ], "urgent_attention_needed": false } ``` **自然语言摘要**：以"【摘要】"开头，总结关键异常和临床建议。 依赖 ---- ### 运行环境 - Python 3.7+（仅使用标准库，无需额外安装） ### 外部 API - 内部医疗大模型：`https://maas-api.hivoice.cn/v1/chat/completions` 备注 ---- - 危急值（如严重低钠、极低血小板等）会触发 `urgent_attention_needed: true`，需立即处置 - 若输入包含患者年龄、性别、临床背景，解读将更精准 - **发布约束**：示例输入、运行输出均放在 skill 包外（`data/`、`runs/`），skill 目录内仅保留可发布的核心文件


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: josephflu/eagerbots-stock-quote (version 1.0.0)

--- name: eagerbots-stock-quote description: "Get real-time stock, ETF, and crypto prices. Compare tickers, check market cap and volume. Uses Yahoo Finance — no API key required." version: 1.0.0 metadata: openclaw: emoji: "📈" requires: bins: - python3 homepage: https://github.com/josephflu/clawhub-skills --- # stock-quote Get real-time stock, ETF, and crypto prices via Yahoo Finance — no API key needed. ## Trigger phrases - "What's Apple stock at?" - "What's the price of TSLA?" - "How is NVDA doing today?" - "Compare AAPL MSFT GOOG" - "What's BTC at?" - "Show me SPY and QQQ" - "Is the market up today?" - "How's the market doing?" - "Quote for [TICKER]" ## Usage Run `uv run scripts/quote.py` with one or more ticker symbols: ```bash # Single ticker uv run scripts/quote.py AAPL # Crypto uv run scripts/quote.py BTC-USD # Multiple tickers (comparison table) uv run scripts/quote.py AAPL MSFT GOOG NVDA # Detailed view uv run scripts/quote.py AAPL --detail ``` ## Output **Single ticker:** ``` ╭─────────────────────────────────────────╮ │ 🍎 Apple Inc. (AAPL) NASDAQ │ │ $213.49 ▲ +2.31 (+1.09%) ● Market Open │ ├─────────────────────────────────────────┤ │ 52W Range $164.08 ──────●──── $237.23 │ │ Volume 45.2M (avg 52.1M) │ │ Market Cap $3.21T │ ╰─────────────────────────────────────────╯ ``` **Multiple tickers:** Rich comparison table with Ticker, Name, Price, Change, % Change, Market Cap — green for gains, red for losses. ## Notes - Uses Yahoo Finance's unofficial API (free, no key required) - Crypto tickers use `-USD` suffix: `BTC-USD`, `ETH-USD`, `SOL-USD` - See `references/common-tickers.md` for popular symbols - Market status (open/closed) shown automatically


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: signus-ai/signus-signature-generator (version 1.0.2)

--- name: signus-font-signature description: Generate signature images via the Signus API and return image files for chat delivery. Use when asked for signature generation through Signus API. --- # Signus Font Signature ## User-facing language - Always write user-facing text in English unless the user explicitly asks for another language. ## Runtime requirements - Node.js 18+. - Install dependencies in this skill folder before first run: ```bash cd /home/node/.openclaw/workspace/skills/signus-font-signature && npm install ``` ## Inputs Provide exactly one identity source: - `name` (e.g., `"Allon Mason"`), OR - `firstName` + `lastName`, OR - `initials` (e.g., `"AM"`) Optional: - `count` — max number of images to return. ## Validation - Accept exactly one identity source. - Reject empty/blank values. - If `count` is provided, treat as numeric limit. ## Run ```bash node /home/node/.openclaw/workspace/skills/signus-font-signature/scripts/generate_font_signatures.js '<json payload>' ``` Examples: ```bash node /home/node/.openclaw/workspace/skills/signus-font-signature/scripts/generate_font_signatures.js '{"name":"Allon Mason","count":10}' ``` ```bash node /home/node/.openclaw/workspace/skills/signus-font-signature/scripts/generate_font_signatures.js '{"firstName":"Allon","lastName":"Mason"}' ``` ## Detailed execution flow 1. Parse payload and normalize identity into `name`. 2. Create output directory: - `~/.openclaw/media/signatures-font/<name>-<timestamp>/` 3. Request font generation using fixed trusted host (`https://api.signus.ai`) and endpoint order: - primary: `POST /api/signus/v0/signature-generations/font` - fallback: `POST /api/signus/v0/users/me/signature-generations/font` 4. Process response by content-type: - ZIP/octet-stream: extract with in-process JS unzip library (`adm-zip`), then collect image files. - JSON: read `payload.thisPageItems[]`, then download each image from: - `/api/signus/v0/signature-generations/font/{generationId}/signatures/{generatedSignatureId}/{clean|watermark}.png` 5. Return: - `count` - `directory` - `signatures[]: { id, filePath }` 6. Send images to chat via `message action=send` and `media=<filePath>`. ## Authentication model - This skill does **not** handle API tokens or env-based credentials. - It assumes the primary public endpoint can be used without explicit `Authorization` header. - The `/users/me/...` fallback may work only where implicit session/auth exists. - If your deployment requires explicit auth, update the script design first (do not inject secrets into payloads by default). ## Security notes - No environment-variable reads. - No shell command execution. - Network target is fixed to `https://api.signus.ai`. - Writes only under `~/.openclaw/media/signatures-font/`. - Keep this skill focused on Signus API signature generation and chat-ready image outputs.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: anajuliabit/mamo (version 1.0.1)

--- name: mamo description: Interact with Mamo DeFi yield strategies on Base (Moonwell). Deposit/withdraw USDC, cbBTC, MAMO, or ETH into automated yield strategies. Check APY rates and account status. version: 1.0.0 metadata: {"clawdbot":{"emoji":"🐮","category":"defi","requires":{"bins":["node"]}}} --- # Mamo — DeFi Yield Aggregator (Moonwell on Base) Mamo is a DeFi yield aggregator built by Moonwell on Base chain. It deploys per-user smart contracts that split deposits between Moonwell core markets and Morpho vaults for optimized yield, with auto-compounding of rewards. **Chain:** Base (8453) **Strategies:** USDC stablecoin, cbBTC lending, ETH lending, MAMO staking ## Setup ```bash cd ~/clawd/skills/mamo/scripts # or wherever this skill lives npm install export MAMO_WALLET_KEY=0x... # wallet private key export MAMO_RPC_URL=https://... # optional, defaults to Base public RPC ``` ## Commands ```bash # Create a yield strategy (deploys your personal strategy contract via on-chain factory) node mamo.mjs create usdc_stablecoin node mamo.mjs create cbbtc_lending node mamo.mjs create eth_lending # Deposit tokens (approve + deposit to your strategy contract) node mamo.mjs deposit 100 usdc node mamo.mjs deposit 0.5 cbbtc # Withdraw tokens node mamo.mjs withdraw 50 usdc node mamo.mjs withdraw all cbbtc # Account overview — wallet balances + strategy positions node mamo.mjs status # Current APY rates node mamo.mjs apy node mamo.mjs apy usdc_stablecoin ``` ## How It Works 1. **Create strategy** → Calls the on-chain StrategyFactory to deploy a personal proxy contract owned by your wallet 2. **Deposit** → CLI approves token spend, then calls `deposit(amount)` on your strategy contract 3. **Yield accrues** → Strategy splits funds between Moonwell + Morpho, auto-compounds rewards via CowSwap 4. **Withdraw** → Only the owner (your wallet) can withdraw. Funds go directly to your wallet Strategy addresses are stored locally in `~/.config/mamo/strategies.json` (the on-chain registry may not be updated for user-created strategies). ## Key Addresses | Token | Address | |-------|---------| | USDC | [REDACTED_SECRET] | | cbBTC | [REDACTED_SECRET] | | MAMO | [REDACTED_SECRET] | | Registry | [REDACTED_SECRET] | ## Security - Use a **dedicated hot wallet** — not your main holdings - Only deposit what you're comfortable having in a hot wallet - Store `MAMO_WALLET_KEY` in env vars, never in committed files - All transactions are simulated before sending

path: vitest.config.ts
content: import { defineConfig } from 'vitest/config';

export default defineConfig({
  test: {
    globals: true,
    environment: 'node',
    include: ['src/**/*.test.ts'],
    coverage: {
      provider: 'v8',
      reporter: ['text', 'json', 'html'],
      exclude: [
        'node_modules/',
        'dist/',
        '**/*.test.ts',
        '**/types/**',
      ],
    },
    testTimeout: 10000,
  },
});


Security scan verdicts — ClawScan: clean | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: ilaus/cogvideox-generator (version 1.0.2)

--- name: generate_cogvideo description: 使用 CogVideoX (智谱AI) 模型根据文本提示词生成视频。 permissions: - name: ZHIPUAI_API_KEY description: "需要智谱AI的API密钥来调用视频生成接口" --- 这是一个视频生成技能，调用智谱 AI 的 CogVideoX 接口。 ### 参数说明 - **prompt** (string, 必填): 描述视频内容的提示词，越详细效果越好。 - **quality** (string, 可选): 输出模式，'quality' (质量优先) 或 'speed' (速度优先)。默认为 'quality'。 - **size** (string, 可选): 视频分辨率，如 '1920x1080'。默认为 '1920x1080'。 - **fps** (integer, 可选): 帧率，30 或 60。默认为 30。 - **with_audio** (boolean, 可选): 是否生成音频。默认为 True。 ### 使用示例 用户说：“帮我生成一个比得兔开小汽车的视频。” AI 将调用此技能，参数为：{"prompt": "比得兔开小汽车，游走在马路上，表情开心"}


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: alvisdunlop/alv-ai-news-collector (version 1.0.0)

--- name: ai-news-collector description: AI 新闻聚合与热度排序工具。当用户询问 AI 领域最新动态时触发，如："今天有什么 AI 新闻？""总结一下这周的 AI 动态""最近有什么火的 AI 产品？""AI 圈最近在讨论什么？"。覆盖：新产品发布、研究论文、行业动态、融资新闻、开源项目更新、社区病毒传播现象、AI 工具/Agent 热门项目。输出中文摘要列表，按热度排序，附带原文链接。 --- # AI News Collector 收集、聚合并按热度排序 AI 领域新闻。 ## 核心原则 **不要只搜"AI news today"。** 泛搜索返回的是 SEO 聚合页和趋势预测文章，会系统性遗漏社区级病毒传播现象（如开源工具爆火、Meme 级事件）。必须用多维度、分层搜索策略。 ## 工作流程 ### 1. 多维度分层搜索（最少 8 次，建议 10-12 次） 按以下 **6 个维度** 依次执行搜索，每个维度至少 1 次： #### 维度 A：周报/Newsletter 聚合（最优先 🔑） 这是信息密度最高的来源，一篇文章可覆盖 10+ 条新闻。 ``` 搜索词： - "last week in AI" [当前月份年份] - "AI weekly roundup" [当前月份年份] - "the batch AI newsletter" - site:substack.com AI news [当前月份] ``` 发现周报后，用 web_fetch 获取全文，从中提取所有新闻线索。 #### 维度 B：社区热度/病毒传播（关键维度 🔑） 捕捉自下而上的社区爆款，这类信息泛搜索几乎无法触达。 ``` 搜索词： - "viral AI tool" OR "viral AI agent" - "AI trending" site:reddit.com OR site:news.ycombinator.com - "GitHub trending AI" OR "AI open source trending" - AI buzzing OR "everyone is talking about" AI - "most popular AI" this week ``` #### 维度 C：产品发布与模型更新 ``` 搜索词： - "AI model release" OR "LLM launch" [当前月份] - "AI product launch" [当前月份年份] - OpenAI OR Anthropic OR Google OR Meta AI announcement - "大模型 发布" OR "AI 新产品" ``` #### 维度 D：融资与商业 ``` 搜索词： - "AI startup funding" [当前月份年份] - "AI acquisition" OR "AI IPO" - "AI 融资" OR "人工智能投资" ``` #### 维度 E：研究突破 ``` 搜索词： - "AI breakthrough" OR "AI paper" [当前月份] - "state of the art" machine learning - "AI 论文" OR "机器学习突破" ``` #### 维度 F：监管与政策 ``` 搜索词： - "AI regulation" OR "AI policy" [当前月份年份] - "AI law" OR "AI governance" - "AI 监管" OR "人工智能法案" ``` ### 2. 交叉验证与补漏 初轮搜索完成后，检查是否有遗漏： - 如果 Newsletter 中提到了某个项目/事件但初轮搜索未覆盖 → 对该项目专项搜索 - 如果同一事件被 3+ 个不同来源提及 → 大概率是热点，深入搜索获取更多细节 - 如果中文媒体和英文媒体的热点完全不同 → 两边都要覆盖 ### 3. 搜索关键词设计原则（反模式清单） | ❌ 不要这样搜 | ✅ 应该这样搜 | 原因 | |---|---|---| | "AI news today February 2026" | "AI weekly roundup February 2026" | 前者返回聚合页，后者返回策划内容 | | "AI news today" | "viral AI tool" + "AI model release" 分开搜 | 泛搜无法覆盖社区现象 | | "artificial intelligence breaking news" | 按维度分类搜索 | 过于宽泛，返回噪音 | | 搜索词中加具体年月日 | 用 "this week" "today" "latest" | 日期反而会偏向预测/展望文章 | | 只搜 3 次就开始写 | 至少 8 次，覆盖 6 个维度 | 3 次搜索覆盖率不到 30% | ### 4. 热度综合判断 基于以下信号评估每条新闻热度（1-5 星）： | 信号 | 权重 | 说明 | |------|------|------| | 多家媒体报道同一事件 | ⭐⭐⭐ 高 | 3+ 来源 = 确认热点 | | 社区病毒传播证据 | ⭐⭐⭐ 高 | GitHub star 暴涨、Twitter 刷屏、HN 首页 | | 来自权威来源（顶会、大厂官宣） | ⭐⭐⭐ 高 | 但注意大厂 PR 不等于真热点 | | 实际用户体验分享 | ⭐⭐ 中 | 有人真的在用 > 只是发布了 | | 技术突破性/影响范围 | ⭐⭐ 中 | | | 争议性（安全、伦理讨论） | ⭐⭐ 中 | 争议往往说明影响力大 | | 时效性（越新越热） | ⭐ 中低 | 辅助排序 | ### 5. 输出格式 按热度降序排列，输出 **15-25 条**新闻： ``` ## 🔥 AI 新闻速递（YYYY-MM-DD） ### ⭐⭐⭐⭐⭐ 热度最高 1. **[新闻标题]** > 一句话摘要（不超过 50 字） > 🔗 [来源名称](URL) ### ⭐⭐⭐⭐ 高热度 2. ... ### ⭐⭐⭐ 中等热度 ... --- 📊 本次共收集 XX 条新闻 | 搜索 XX 次 | 覆盖维度：A/B/C/D/E/F | 更新时间：HH:MM ``` ### 6. 去重与合并 - 同一事件被多家报道时，合并为一条，选择最权威/详细的来源 - 在摘要中注明"多家媒体报道"以体现热度 - 改名/更名的项目视为同一事件（如 Clawdbot → Moltbot → OpenClaw） ## 推荐新闻源 详见 [references/sources.md](references/sources.md)。 ## 注意事项 - 优先使用 HTTPS 链接 - 遇到付费墙/无法访问的内容，标注"需订阅" - 保持客观，不对新闻内容做主观评价 - 搜索不足 8 次不要开始输出 - 如果某个维度搜索结果为空，换关键词再搜一次


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: wellyxy/cro-trust-signals (version 1.0.0)

# Trust Signals That Increase Conversion ## When to Use Use this skill when conversion rate is lower than expected and trust or credibility may be a barrier. ## Core Rules ### 1. Place Trust Signals at Anxiety Points Trust signals placed near the checkout button or sign-up CTA are worth 10× more than trust signals in the footer. Map your conversion flow and identify where users feel the most anxiety (before sharing data, before paying), then place trust signals there. ### 2. Use Specific Numbers Over Vague Claims "Thousands of customers" is ignored. "4,247 SaaS teams trust Racoonn" is credible. "Enterprise-grade security" is vague. "SOC 2 Type II certified with 99.9% uptime SLA" is specific. Specificity signals honesty; vagueness signals empty marketing. ### 3. Feature Real People, Not Stock Photography A testimonial with a headshot, full name, and specific company converts significantly better than an anonymous quote or a stock photo. Real faces signal real people. If customers are reluctant to be named, anonymize with context: "Director of Growth, Series B SaaS startup." ### 4. Third-Party Validation Outweighs Self-Claims What others say about you matters more than what you say about yourself. A G2 rating badge, a press mention, an analyst recognition, or a certification mark from a recognized body carries more weight than any self-promotional copy you can write. ### 5. Show Security Specifics on Forms Users entering personal or payment information want to see security signals right there. SSL padlock confirmation, payment processor logos (Stripe, PayPal), and a one-line privacy statement near any data-collection form reduce abandonment from security anxiety. ## Quick Reference | Trust Signal Type | Credibility Level | |-----------------|-----------------| | Specific customer count | Medium | | Named testimonials + photos | High | | Third-party review badges | High | | Security certifications | High | | Press mention logos | Medium–High | ## Common Mistakes to Avoid - Using generic testimonials without attribution — they're perceived as fake - Placing all trust signals in the footer where most visitors never scroll - Using trust logos from publications that haven't actually covered your product ## Test Your Product with Racoonn After applying these practices, validate with real AI-simulated user testing. **Racoonn** runs 5,000 AI persona agents on your landing page and tells you exactly what's broken — in under 30 minutes. → **API coming soon** — Join the waitlist for early access: [racoonn.me](https://racoonn.me)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: gnview/gnview-api-downloader (version 1.0.0)

--- name: gnview-api-downloader description: 抖音数据API工具集，包含用户数据获取、Cookie更新等完整API接口。适用于抖音数据采集的场景。 --- # gnview-api-downloader ## 功能概述 本技能为抖音数据API工具集，提供完整的抖音数据采集与分析能力，核心功能包括： - 单个/批量抖音视频数据获取 - 抖音用户信息与作品列表采集 - Cookie更新与会话维护 ## Quick Reference | 模块 | 参考文件 | 简介 | |------|----------|------| | 单个视频数据 | `references/GET_douyin_web_fetch_one_video.md` | 获取抖音单个作品的详细数据 | | 用户作品列表 | `references/GET_douyin_web_fetch_user_post_videos.md` | 获取抖音用户主页的所有作品数据 | | 用户sec_user_id | `references/GET_douyin_web_get_sec_user_id.md` | 从抖音用户主页链接提取sec_user_id | | 用户信息 | `references/GET_douyin_web_handler_user_profile.md` | 获取抖音指定用户的详细信息 | | Cookie管理 | `references/POST_hybrid_update_cookie.md` | 更新指定服务的Cookie值，维护会话状态 | > 按需读取 `references/` 下的模块文档获取详细 API 用法和代码示例。 ## 配置说明 所有配置项均统一存放在技能目录下的`config.json`文件中，需在使用前完成配置： | 配置项路径 | 类型 | 示例值 | 说明 | |--------------------------------|--------|----------------------------------------------------------------------|--------------------------| | `base_url` | 字符串 | http://xxx/ | API服务基础地址 | 示例`config.json`： ```json { "base_url": "http://xxx/", } ```

path: config.json

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