OpenClaw/clawhub-security-signals huggingface dataset:
Skill: 10000-c/tyrpay-buyer-skill (version 0.1.8)

--- name: tyrpay-buyer-skill description: Buyer-side TyrPay workflow for LLM agents. Create tasks, optionally wait for seller commitment, fund tasks explicitly, monitor settlement, and request refunds. --- # TyrPay Buyer Skill Use this skill when an agent needs to act as the buyer in a TyrPay payment flow. It assumes the runtime already has a configured `BuyerSdk` and access to the `@tyrpay/buyer-skill` tool set. ## Quick Start 1. Install `@tyrpay/buyer-skill`, `@tyrpay/buyer-sdk`, and a storage adapter. 2. Construct `BuyerSdk` with a signer, settlement address, and storage adapter. 3. Register `createBuyerTools(sdk)` with your tool-calling runtime. 4. Call `tyrpay_ready` before the first paid workflow. 5. Use `tyrpay_post_task` with `expectations` for the default flow, or `createOnly: true` plus `tyrpay_fund_task` (with `expectations`) for explicit control. ## When To Use - The agent is responsible for creating and funding a TyrPay task. - The agent must enforce buyer-side commitment expectations before payment is locked. - The agent needs structured task state that is safe to show to an end user. - The buyer workflow must stay non-blocking and recover cleanly after seller-response timeouts. ## Workflow 1. Run `tyrpay_ready` to verify signer and provider connectivity. 2. Call `tyrpay_post_task` with `expectations` describing the acceptable commitment terms. 3. If you need explicit orchestration, set `createOnly: true` (no `expectations` needed at creation). 4. When the seller commitment is ready, call `tyrpay_fund_task` with `expectations` to validate and fund. 5. Use `tyrpay_check_task` or `tyrpay_list_tasks` to monitor progress. 6. If proof submission or verification stalls past protocol timeouts, call `tyrpay_refund_task`. ## Mandatory Expectations - **`expectations` is required on every funding path.** `tyrpay_fund_task` always requires it. `tyrpay_post_task` requires it unless `createOnly: true`. - The on-chain protocol does **not** enforce that the seller's commitment matches the buyer's requirements. The chain only records the commitment; it does not validate host, path, models, minimum usage, verifier, or deadline against the buyer's intent. - The skill layer rejects any funding call that omits `expectations`, preventing the agent from locking funds under unintended terms. ## Tooling Notes - All tools reject malformed inputs with structured `BuyerSkillToolError` errors. - `tyrpay_post_task` returns `timedOut: true` instead of throwing when seller wait time expires after task creation. - `tyrpay_fund_task` validates the commitment once, then funds without a second redundant validation pass. - Buyer-facing statuses include `VERIFIED_PASS` and `VERIFIED_FAIL` when a verification report is available. ## Resources - `references/tool-reference.md` for the tool contract and status model.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: dongrebeccahhh-boop/ai-image-gen-skill (version 1.0.0)

--- name: ai-image-gen description: AI图片生成技能，使用Gemini生成高质量图片，支持文生图和图生图 version: 1.0.0 author: 小帽 grade: A category: media tags: [图片, AI, gemini, 生成] --- # AI图片生成技能 使用 AI 生成高质量图片。 ## 核心命令 ```bash uv run /usr/lib/node_modules/openclaw/skills/nano-banana-pro/scripts/generate_image.py \ --prompt "你的描述" --filename "output.jpg" --resolution 2K ``` ## 参数说明 | 参数 | 说明 | |------|------| | `--prompt` | 图片描述（中英文都行） | | `--filename` | 输出文件名 | | `--resolution` | 分辨率：1K / 2K | | `-i reference.jpg` | 可选：参考图片（风格迁移、图片编辑） | ## 使用示例 ### 文生图 ```bash # 生成产品封面 uv run /usr/lib/node_modules/openclaw/skills/nano-banana-pro/scripts/generate_image.py \ --prompt "极简白色背景产品展示图，现代科技感" \ --filename cover.jpg \ --resolution 2K ``` ### 图生图（风格迁移） ```bash # 基于参考图修改 uv run /usr/lib/node_modules/openclaw/skills/nano-banana-pro/scripts/generate_image.py \ --prompt "把背景改成蓝天白云" \ -i original.jpg \ --filename edited.jpg ``` ### 常用场景 ```bash # 公众号封面 --prompt "公众号封面：AI技术，科技感，简洁大气" # 产品图 --prompt "产品展示图，白色背景，专业摄影风格" # 插图 --prompt "文章配图：数据分析，图表，商务风格" ``` ## 注意事项 - 需要 `GEMINI_API_KEY` 环境变量 - 支持 1K 和 2K 分辨率 - 中英文 prompt 都支持 --- *技能创建时间: 2026-03-17*


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

--- name: video-downloader description: Download videos from YouTube and other video platforms. Use when user needs to download videos for offline viewing, extract audio from videos, or save video metadata. --- # Video Downloader Download videos from YouTube and other platforms. ## Quick Start ```bash # Download video python scripts/download.py https://youtube.com/watch?v=xxx ``` ## Usage ```bash python scripts/download.py URL [OPTIONS] Options: --output PATH Output directory --format FORMAT Video format (mp4, webm) --quality QUALITY Quality (best, 1080p, 720p, 480p) --audio-only Extract audio only --list-formats List available formats ``` ## Examples ```bash # Download video python scripts/download.py "https://youtube.com/watch?v=xxx" # Download as MP4 python scripts/download.py "URL" --format mp4 # Audio only python scripts/download.py "URL" --audio-only # List formats python scripts/download.py "URL" --list-formats ``` ## Supported Platforms - YouTube - Vimeo - Twitter/X - And more... ## Features - Video downloading - Audio extraction - Quality selection - Format conversion - Metadata extraction


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: turinfohlen/toolchain-bootstrap (version 1.0.0)

--- name: toolchain-bootstrap version: 1.0.0 description: > OpenClaw 新容器初始化工具链引导程序。自动从 GitHub 下载 toolchain_v2.tar.gz， 解压到 /workspace，配置 PATH 环境变量，验证所有已安装语言/工具。 适用场景：新容器启动后一行命令完成全部开发环境配置。 tags: ["toolchain", "bootstrap", "environment", "setup", "development"] --- # toolchain-bootstrap > 新容器初始化 — 5 分钟搞定所有开发语言环境 ## 使用方式 ```bash # 完整初始化（新容器） openclaw skill run toolchain-bootstrap setup # 仅验证当前环境 openclaw skill run toolchain-bootstrap verify # 查看已安装工具列表 openclaw skill run toolchain-bootstrap list ``` ## 验证的工具 | 工具 | 路径 | 版本命令 | |------|------|----------| | Go | `/workspace/toolchain/go/bin/go` | `go version` | | Java (OpenJDK) | `/workspace/toolchain/jdk-21.0.10+7/bin/java` | `java -version` | | Maven | `/workspace/toolchain/apache-maven-3.9.6/bin/mvn` | `mvn -version` | | Erlang | [REDACTED_SECRET] | `erl -eval '...' -noshell` | | Elixir | [REDACTED_SECRET] | `elixir --version` | | Rust | `/workspace/toolchain/rust/rustup/toolchains/*/bin/rustc` | `rustc --version` | | Ruby | [REDACTED_SECRET] | `ruby --version` | | Lua | [REDACTED_SECRET] | `lua -v` | | Node.js | `/usr/local/bin/node` | `node --version` | | Python | `/usr/bin/python3` | `python3 --version` | ## 环境变量 自动写入 `~/.bashrc`： ```bash export TOOLCHAIN=/workspace/toolchain export PATH=/workspace/toolchain/go/bin:$PATH export JAVA_HOME=/workspace/toolchain/jdk-21.0.10+7 export RUSTUP_HOME=/workspace/toolchain/rust export CARGO_HOME=/workspace/toolchain/rust/.cargo ``` ## 源 - 下载包: `https://github.com/TurinFohlen/openclaw-toolchain/releases/download/v2.0/toolchain_v2.tar.gz` - `scripts/bootstrap.sh` — 主引导脚本 - `scripts/setup-env.sh` — 环境变量配置 - `references/env-template.txt` — PATH 模板参考


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: utromaya-code/self-improver-lite-ai (version 1.0.0)

--- name: self-improver-lite description: Runs lightweight self-audits of OpenClaw behavior, finds repeated failures, proposes safe config/process improvements, and tracks what changed. Use after incidents, silent-bot periods, rate-limit spikes, or weekly maintenance. --- # Self Improver Lite ## Goal Continuously improve reliability without risky autonomous changes. ## Trigger Cases - Bot is silent or unstable - Same error appears repeatedly (`rate_limit`, `orphaned`, `timeout`, `sendMessage failed`) - After major config updates - Scheduled weekly health review ## Audit Steps 1. Collect evidence: ```bash systemctl is-active openclaw-gateway ollama journalctl -u openclaw-gateway -n 120 --no-pager ``` 2. Group failures by pattern and count. 3. Identify top 1-3 root causes. 4. Propose minimal, reversible fixes. 5. Apply only low-risk fixes automatically. 6. Record summary and next actions. ## Auto-Allowed Changes - Session cleanup when `orphaned user message` loops - Gateway restart for stuck polling - Context window/maxTokens tuning within known-safe ranges - Fallback chain reordering (no key changes) ## Requires Explicit User Approval - New API provider onboarding - Social network account actions - Financial/crypto operations - Credential rotation or deletion - Any destructive file cleanup beyond sessions/log rotation ## Weekly Review Template ```markdown ## Weekly Self-Audit - Period: <dates> - Uptime notes: <short> ### Top Errors 1. <error> — <count> 2. <error> — <count> ### Changes Applied - <change 1> - <change 2> ### Measured Impact - Response latency: <before -> after> - Failed runs: <before -> after> ### Next Improvements - <single highest-impact next step> ``` ## Guardrails - Never expose secrets in reports. - Always keep a rollback path (backup old config before edits). - Prefer one change per cycle to keep causality clear.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: z1chengge/python-ysddata-keliu (version 1.0.0)

--- name: python-ysddata-keliu description: 查询客流数据，输出JSON，适合直接导入 Bitable 或其它数据可视化工具 --- # 技能:查询客流数据 # 用法（聊天框直接说） -查询2026年4月1日到4月10日客流 # 例子 - 查询{start}到{end}客流 - 查{start}至{end}客流 - 获取{start}至{end}客流 - 拉取{start}到{end}客流数据 params: start: type: date help: 开始日期，例如 2026-04-01 end: type: date help: 结束日期，例如 2026-04-10 # 运行 ```bash cd C:\Users\Administrator\.openclaw\workspace\skills\python-ysddata-keliu\commands python query.py --start 2026-04-01 --end 2026-04-10 ```


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

--- name: tac-tool description: Concatenate and display files in reverse line order (last line first). Use for viewing log files from newest entries and reversing file content. --- # Tac - Reverse File Concatenation Display file contents with lines in reverse order. The last line appears first, making it useful for viewing recent log entries and processing data in reverse. ## Usage ```bash tac-tool [options] <file...> ``` ## Examples ```bash tac-tool log.txt tac-tool file1.txt file2.txt > reversed_combined.txt ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: zhanjian1972/ofdreader (version 1.0.0)

--- name: ofdreader description: OFD 文档读取和转换工具。支持从 OFD（Open Fixed-layout Document）文件中提取文本内容，并将其转换为 Markdown 格式。使用此 skill 处理 .ofd 文档时：提取纯文本内容、转换为包含基本格式（标题、段落、表格）的 Markdown、处理中文版式文档。OFD 是中国国家标准版式文档格式，常用于电子公文、证照等场景。 --- # OfdReader OFD（Open Fixed-layout Document）文档读取和转换工具。从 OFD 文件中提取内容并转换为 Markdown。 ## 快速开始 ### 提取纯文本 ```bash python scripts/ofd_to_text.py <ofd文件路径> ``` 输出到文件： ```bash python scripts/ofd_to_text.py <ofd文件路径> output.txt ``` ### 转换为 Markdown ```bash python scripts/ofd_to_markdown.py <ofd文件路径> ``` 输出到文件： ```bash python scripts/ofd_to_markdown.py <ofd文件路径> output.md ``` ## 工作流程 1. **验证文件**：确认 OFD 文件存在且格式有效（OFD 本质是 ZIP 压缩包） 2. **提取内容**： - 解压 OFD 文件 - 解析内部 XML 结构 - 提取 TextCode 和 TextContent 元素 3. **格式转换**： - **文本模式**：直接拼接所有文本内容 - **Markdown 模式**：识别段落、标题和表格，转换为对应 Markdown 语法 4. **输出结果**：打印到控制台或写入文件 ## 脚本说明 - `scripts/ofd_to_text.py`：提取纯文本，保留所有文字内容但不处理格式 - `scripts/ofd_to_markdown.py`：转换为 Markdown，保留段落、标题和表格结构 - `scripts/install_dependencies.py`：安装可选依赖（核心功能使用标准库） ## OFD 格式说明 OFD 文件结构： - 根目录包含 `OFD.xml`（文档清单） - `Doc_0/` 目录包含文档内容 - 内容以 XML 格式存储，使用 `http://www.ofdspec.org/2016` 命名空间 关键元素： - `TextCode`：文本内容 - `Paragraph`：段落 - `Table`：表格 - `Row`/`Cell`：表格行和单元格 ## 限制和注意事项 1. **格式保真度**：OFD 支持复杂的排版布局，脚本仅提取逻辑内容，无法完全保留视觉效果 2. **表格识别**：基于 XML 结构推断表格，复杂表格可能转换不完整 3. **标题检测**：使用启发式规则（短文本、特定模式），可能误判 4. **编码**：OFD 通常使用 UTF-8，脚本自动处理编码 5. **依赖**：核心脚本使用 Python 标准库（zipfile, xml.etree.ElementTree），无需额外依赖 ## 使用示例 提取 OFD 文档文本： ```bash # 用户询问时 python "C:\Users\zhan\.claude\plugins\skills\OfdReader\scripts\ofd_to_text.py" "document.ofd" "extracted.txt" ``` 转换为 Markdown： ```bash python "C:\Users\zhan\.claude\plugins\skills\OfdReader\scripts\ofd_to_markdown.py" "document.ofd" "document.md" ``` ## 故障排除 **错误："文件不是有效的 OFD (ZIP) 格式"** - 文件可能损坏或不是 OFD 格式 - 检查文件扩展名是否为 .ofd **错误："OFD 文件不存在"** - 检查文件路径是否正确 - Windows 路径需要用引号括起来 **提取内容为空** - OFD 文件可能是扫描版图片（无文本层） - 尝试用其他工具打开确认是否有可提取的文本 **表格格式混乱** - 复杂表格可能无法完美转换 - 考虑使用 ofd_to_text.py 提取纯文本后手动整理

path: scripts/install_dependencies.py
content: #!/usr/bin/env python3
"""
安装 OfdReader skill 所需的依赖
"""
import subprocess
import sys


def install_dependencies():
    """安装所需的 Python 包"""
    dependencies = [
        # OFD 处理可能需要的库
        # ofdrw 是一个 OFD 读写库
        "ofdrw>=0.3.0",
    ]

    print("正在安装 OfdReader skill 的依赖...")

    for package in dependencies:
        print(f"安装 {package}...")
        try:
            subprocess.check_call([sys.executable, "-m", "pip", "install", package])
            print(f"  {package} 安装成功")
        except subprocess.CalledProcessError as e:
            print(f"  警告: {package} 安装失败: {e}")
            print("  注意: 核心功能使用标准库，仍可正常使用")

    print("\n依赖安装完成！")


if __name__ == '__main__':
    install_dependencies()


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: 3rdbrain/reddit-prospecting (version 1.0.1)

--- name: reddit-prospecting description: Find buying-intent leads on Reddit via Brave Search — no Reddit account or OAuth needed. Score posts and generate value-first comments. version: 1.0.0 author: ModelFitAI <[REDACTED_SECRET]> license: MIT keywords: [openclaw, skill, reddit, lead-gen, prospecting, brave-search, sales] requires: env: [BRAVE_API_KEY] --- # Reddit Lead Prospecting Skill Find high-intent leads on Reddit using **Brave Search** (`site:reddit.com`) — no Reddit account, no OAuth, no credentials except a free Brave API key. ## How It Works Instead of Reddit's OAuth API (which requires app registration and credentials), this skill queries `site:reddit.com` via the Brave Search API. You get real Reddit posts and comments with buying intent — without touching Reddit's API at all. ## Commands Run with Node.js: `node {baseDir}/reddit-prospecting.js <command> [args]` - **search `<keyword>`** — Find high-intent Reddit posts via Brave Search - **score** — Score a Reddit prospect for lead quality - **comment** — Generate a value-first comment structure for a post ## Usage ```bash node {baseDir}/reddit-prospecting.js search "looking for AI agent tool" node {baseDir}/reddit-prospecting.js search "alternative to Zapier" --subreddit startups node {baseDir}/reddit-prospecting.js score --username u/founder123 --post "frustrated with my current tool" node {baseDir}/reddit-prospecting.js comment --title "Best AI tools for lead gen?" --subreddit AIMarketing ``` ## Environment Variables - `BRAVE_API_KEY` — Brave Search API key. Free tier: 2,000 queries/month. Sign up at [api.search.brave.com](https://api.search.brave.com) ## What It Does NOT Use - No Reddit client ID or secret - No Reddit username or password - No Reddit OAuth flow - No direct calls to reddit.com API endpoints


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: cloudyxuq/meat-protein-kjeldahl (version 1.0.1)

--- name: meat-protein-kjeldahl description: 依据GB 5009.5-2025第一法凯氏定氮法，指导畜禽肉蛋白质含量测定全流程，涵盖试剂配制、消解蒸馏、滴定操作及结果计算；当用户需要检测肉类蛋白质、执行凯氏定氮实验、计算蛋白质含量或验证精密度时使用 --- # 畜禽肉蛋白质凯氏定氮法测定 ## 任务目标 - 本 Skill 用于：依据 GB 5009.5-2025 第一法凯氏定氮法，指导畜禽肉中蛋白质含量的测定 - 能力包含：检测流程指导、试剂配制方案、结果计算与精密度验证 - 触发条件：用户需要检测畜禽肉蛋白质含量、执行凯氏定氮实验、计算或验证检测结果 ## 前置准备 - 本方法依据：GB 5009.5-2025《食品安全国家标准 食品中蛋白质的测定》第一法 - 适用范围：畜禽肉类及其制品中蛋白质含量的测定 - 畜禽肉蛋白质折算系数：F = 6.25 ## 操作步骤 ### 步骤 1：试样制备 - 畜禽肉属于基质均匀的半固态样品，直接称量即可 - 制备好的试样应尽快测定 - 如结果需以干基计算，按 GB 5009.3 测定水分含量 ### 步骤 2：试剂与设备确认 - 读取 [references/kjeldahl-procedure.md](references/kjeldahl-procedure.md) 中"试剂配制"与"仪器设备"章节 - 确认所有试剂在有效期内，标准滴定溶液已标定 - 关键试剂：硫酸铜(CuSO4-5H2O)、硫酸钾(K2SO4)、硫酸(>=98%)、氢氧化钠、硼酸、混合指示液 ### 步骤 3：试样消解 - 称取固体试样 0.2 g~2 g（精确至 0.001 g），移入干燥的定氮瓶中 - 加入 0.4 g 硫酸铜、6 g 硫酸钾及 20 mL 硫酸 - 轻摇后于瓶口放一小漏斗，将瓶以 45 度角斜置于有小孔的石棉网上 - 缓慢加热，待内容物全部碳化、泡沫完全停止后，加大火力 - 保持瓶内液体微沸，至液体呈蓝绿色并澄清透明后，再继续加热 0.5 h~1 h - 取下定氮瓶冷却至室温，小心加入 20 mL 水 - 将内容物全部转移至 100 mL 容量瓶中，洗涤定氮瓶内壁，洗液并入容量瓶，加水至刻度，混匀备用 - 同时进行空白试验 > 消解注意事项详见 [references/kjeldahl-procedure.md](references/kjeldahl-procedure.md) 中"消解操作要点" ### 步骤 4：蒸馏与滴定 - 按定氮蒸馏装置装好设备，向水蒸气发生器内装水至 2/3 处 - 加入数粒玻璃珠、甲基红数滴及数毫升硫酸，保持水呈酸性 - 向接收瓶内加入 10.0 mL 硼酸溶液及 3~4 滴混合指示剂（A 或 B） - 使冷凝管下端插入液面下，准确吸取 2.0 mL~10.0 mL 试样处理液注入反应室 - 加入 10.0 mL 氢氧化钠溶液，立即密封并水封，开始蒸馏 - 蒸馏 15 min 后使接收瓶液面离开冷凝管下端，再蒸馏约 1 min - 用 pH 试纸检测馏出液为中性后，用少量水冲洗冷凝管下端 - 以硫酸或盐酸标准滴定溶液滴定至终点 > 滴定终点判断：A 混合指示液终点为紫红色；B 混合指示液终点为浅灰红色 ### 步骤 5：结果计算 - 调用 `scripts/calculate_protein.py` 计算蛋白质含量 - 脚本参数说明： - `--v1`：试液消耗标准滴定液体积（mL） - `--v2`：试剂空白消耗标准滴定液体积（mL） - `--c`：标准滴定溶液浓度（mol/L） - `--m`：试样质量（g） - `--v3`：吸取消化液体积（mL） - `--v4`：消解溶液定容体积（mL） - `--F`：蛋白质折算系数，畜禽肉默认 6.25 - `--moisture`：水分含量百分比（可选，用于干基计算） - 示例命令： ```bash python scripts/calculate_protein.py --v1 2.50 --v2 0.20 --c 0.1005 --m 1.002 --v3 10.0 --v4 100 --F 6.25 ``` ### 步骤 6：精密度验证 - 当进行两次平行测定时，使用脚本的 `--precision` 参数验证精密度 - 精密度要求： - 蛋白质含量 <= 10 g/100g：两次结果绝对差值不超过算术平均值的 10% - 蛋白质含量 > 10 g/100g：两次结果绝对差值不超过算术平均值的 5% - 示例命令： ```bash python scripts/calculate_protein.py --v1 2.50 --v2 0.20 --c 0.1005 --m 1.002 --v3 10.0 --v4 100 --F 6.25 --precision --v1_2 2.60 --v2_2 0.20 --c2 0.1005 --m2 1.005 --v3_2 10.0 --v4_2 100 ``` ### 步骤 7：全自动/半自动凯氏定氮仪法（可选） - 使用消化炉消解，当消化炉温度达到 420 度后继续消化至少 1 h - 消化管中液体呈绿色透明状后，于全自动/半自动凯氏定氮仪检测 - 蛋白质含量 <= 1 g/100g 时建议用 0.05 mol/L 标准滴定液 - 蛋白质含量 > 1 g/100g 时建议用 0.10 mol/L 标准滴定液 - 使用全自动仪器时 V3 = V4（全部转移消化液） ## 资源索引 - 计算脚本：见 [scripts/calculate_protein.py](scripts/calculate_protein.py)（用途：蛋白质含量计算与精密度验证） - 详细规程：见 [references/kjeldahl-procedure.md](references/kjeldahl-procedure.md)（何时读取：需要试剂配制方法、设备清单、消解操作要点或折算系数表时） ## 注意事项 - 消解过程必须全程在通风橱中进行，防止酸雾伤害 - 硫酸具有强腐蚀性，操作时佩戴防护手套和护目镜 - 空白试验与样品试验必须同步进行，使用同一批试剂 - 标准滴定溶液需按 GB/T 601 配制和标定，或使用有证标准物质 - 蛋白质含量 >= 1 g/100g 时结果保留三位有效数字；< 1 g/100g 时保留两位有效数字 - 分析结果以蛋白质含量表述时，应同时报告蛋白质折算系数 - 食品中若添加非蛋白质含氮物质，将影响蛋白质测定结果的准确性


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: olina1ye/data-analysis-zh (version 1.0.0)

--- name: data-analysis-zh description: > 数据分析助手，从原始数据中提取洞察、生成可视化建议和分析报告。适用于销售数据分析、 用户行为分析、财务数据解读、运营指标拆解等场景。解决"数据太多不知道看哪里"、 "帮我做数据分析"、"数据报告怎么写"、"这份数据有什么规律"等问题。 支持 Excel / CSV / 数据库查询结果等多种输入格式，兼容 WPS 表格与 Microsoft Excel。 tags: [数据分析, data-analysis, 数据洞察, 分析报告, 运营分析, 业务分析, 数据可视化] --- # 数据分析助手 从原始数据中提取业务洞察，生成结构化分析报告和可视化建议。 ## Tools Required - read ## Usage - "帮我分析这份销售数据，找出增长最快的产品和下滑的原因" - "这些用户行为数据有什么规律，帮我总结关键指标" - "根据这个季度的运营数据，写一份带结论和建议的分析报告" ## Examples 输入：`这是我们上个月的销售明细表（Excel），帮我找出销售额前三的品类，以及周环比变化趋势` 输出： - 销售额 Top3 品类及占比 - 各品类周环比变化表格 - 异常波动原因分析（若有） - 下月重点关注建议


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal:  | SkillSpector: clean (LOW)
Skill: abyousef739/clawskillshield (version 1.0.0)

# ClawSkillShield 🛡️ **Local-first security scanner for OpenClaw/ClawHub skills.** ## What It Does - **Static analysis** for security risks and malware patterns - **Detects**: - Hardcoded secrets (API keys, credentials, private keys) - Risky imports (`os`, `subprocess`, `socket`, `ctypes`) - Dangerous calls (`eval()`, `exec()`, `open()`) - Obfuscation (base64 blobs, suspicious encoding) - Hardcoded IPs - **Risk scoring** (0–10) + detailed threat reports - **Quarantine** high-risk skills automatically ## Dual-Use Design - **CLI for humans**: Quick safety checks before installing skills - **Agent API**: Importable functions for autonomous agents/Moltbots to proactively scan and quarantine risky skills (essential post-ClawHavoc) ## Quick Start ### CLI (Humans) ```bash pip install -e . clawskillshield scan-local /path/to/skill clawskillshield quarantine /path/to/skill ``` ### Python API (Agents) ```python from clawskillshield import scan_local, quarantine threats = scan_local("/path/to/skill") if risk_score < 4: # HIGH RISK quarantine("/path/to/skill") ``` ## Zero Dependencies Pure Python. No network calls. Runs entirely locally. ## Why This Matters ClawHavoc demonstrated how easily malicious skills can slip into the ecosystem. ClawSkillShield provides a trusted, open-source defense layer—audit the code, run offline, stay safe. --- **GitHub**: https://github.com/AbYousef739/clawskillshield **License**: MIT **Author**: Ab Yousef **Contact**: [REDACTED_SECRET]


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: jpengcheng523-netizen/jpeng-ab-test-analyzer (version 1.0.0)

--- name: jpeng-ab-test-analyzer description: "Analyze A/B test results" version: "1.0.0" author: "jpeng" tags: ["testing", "ab", "analytics"] --- # A/B Test Analyzer Analyze A/B test results ## When to Use - User needs testing related functionality - Automating ab tasks - Analytics operations ## Usage ```bash python3 scripts/ab_test_analyzer.py --input <input> --output <output> ``` ## Configuration Set required environment variables: ```bash export AB_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)