OpenClaw/clawhub-security-signals huggingface dataset:
Skill: gmsx000-cloud/quant-trading-backtrader (version 1.0.0)

# quant-trading-backtrader A comprehensive skill for building, backtesting, and optimizing quantitative trading strategies using the Backtrader framework in Python. ## Features - **Backtesting Engine**: Simulates trading strategies on historical data with support for multiple data feeds. - **Strategy Development**: Provides a structured `Strategy` class to define indicators (SMA, EMA, RSI, etc.) and trading logic. - **Risk Management**: Examples of implementing stop-loss, take-profit, and position sizing (e.g., fractional Kelly). - **Data Handling**: Support for CSV data ingestion (customizable formats) and pandas DataFrame integration. - **Reporting**: Generates transaction logs, trade analysis (PNL), and portfolio value tracking. ## Usage This skill provides a foundation for creating quantitative trading bots. It includes templates and examples to get you started. ### 1. Installation Ensure you have the required dependencies: ```bash pip install backtrader matplotlib ``` ### 2. Basic Strategy Template Create a new strategy file (e.g., `my_strategy.py`) using the template structure: ```python import backtrader as bt class MyStrategy(bt.Strategy): params = ( ('period', 15), ) def __init__(self): self.sma = bt.indicators.SimpleMovingAverage(self.data.close, period=self.params.period) def next(self): if self.sma > self.data.close: # Do something pass ``` ### 3. Running a Backtest Use `bt.Cerebro` to orchestrate the backtest: ```python cerebro = bt.Cerebro() cerebro.addstrategy(MyStrategy) # ... add data ... cerebro.run() ``` ## Examples Check the `examples/` directory for full working examples: - `sma_crossover.py`: A classic Trend Following strategy with Stop-Loss. ## Best Practices - **Avoid Overfitting**: Use Walk-Forward Analysis (train on past, test on unseen future data). - **Risk Control**: Always implement stop-loss orders. Position sizing is critical for survival. - **Data Quality**: Ensure your historical data is clean and representative.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: huojiecs110/fapi-reddit (version 1.0.2)

# fapi.uk Reddit 全能 API Skill（纯 REST 版） **作者**：@huojiecs110 **版本**：1.1.0 **类别**：Social / Reddit **官网**：https://fapi.uk **文档**：https://utools.readme.io ## 这是什么 这个 Skill 让小龙虾通过自然语言完整使用 **fapi.uk 的 Reddit API**（共 7 个真实接口）。 **完全不需要 MCP**，小龙虾会自己阅读本文件，智能选择对应接口、组装参数、发送 POST 请求，并用自然中文总结结果。 **Base URL**：`https://fapi.uk/api/base/apitools/reddit/` ## 必须先完成一次设置（30 秒） 1. 打开 https://fapi.uk 或 https://utools.readme.io 用 **Twitter / Google** 一键登录 2. 在仪表盘生成 **API Key**（建议命名为 “OpenClaw 小龙虾”） 3. 在 OpenClaw 中设置（强烈推荐）： openclaw config set skills.entries.fapi-reddit.apiKey "你的完整apiKey" 或者直接在聊天里说： “我的 fapi apiKey 是 xxx，我的 auth_token 是 yyy” ## 核心调用规则（小龙虾必须严格遵守） 1. **每次行动前** 先调用余额查询接口，检查积分是否足够 2. 所有请求 **必须** 在url 参数中携带 apiKey` 3. 积分不足时**立即停止**，回复用户并给出充值链接 4. 返回结果后，用**自然流畅的中文**总结关键信息，不要直接贴原始 JSON（用户要求“原始数据”时再给） 5. 支持 proxyUrl 参数（需要时可问用户要代理） ## 主要功能模块（小龙虾会自动选择对应接口） 1. 用户相关（User）userPostsTop（用户热门/置顶帖子，支持时间筛选） userPostsNew（用户最新帖子） userPostsHot（用户热帖） userActivity（用户最近动态，包含帖子和评论） 2. 子版块相关（Subreddit）subredditTop（子版块置顶/精华帖子，支持 hour/day/week/month/year/all） subredditNew（子版块最新帖子） subredditHot（子版块热门帖子） 3. 搜索与详情（Search & Detail） search（全站或指定子版块搜索，支持 relevance/hot/top/new/comments 排序 + 时间筛选） postDetail（获取单条帖子完整详情 + 所有评论，需要 subreddit 和 article id） ## 积分与付费说明 - 每次调用 reddit 接口会从你的 fapi.uk 账户扣除积分 - 积分不足此服务会返回 "You have made too many requests. Please contact the administrator to upgrade the frequency limit. " - **小龙虾不会自动购买积分**（OpenClaw 安全策略禁止自动支付） - 积分不足时我会主动告诉你，并给出 https://fapi.uk/pay 充值链接 - 建议提前充值积分，避免中断 - 注意如果调用 MCP接口 使用自己的代理AI 配置 则只扣除1积分，否则MCP接口每次将会消耗100积分 ## 如何设置 apiKey（只需做一次） 1. 登录 https://fapi.uk （支持 Twitter/Google 一键登录） 2. 前往 https://fapi.uk/api-keys 生成 Key , 并赠送100积分用于测试 3. 在 OpenClaw 里执行： `openclaw config set skills.entries.fapi-uk.apiKey "你的key"`


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: 52yuanchangxing/local-rag-index-planner (version 1.0.0)

--- name: local-rag-index-planner version: 1.0.0 description: "规划本地知识库的目录、分片粒度、命名、更新时间与访问边界，而不是直接堆 RAG。；use for rag, indexing, knowledge workflows；do not use for 直接部署向量数据库, 忽略权限隔离." author: OpenClaw Skill Bundle homepage: https://example.invalid/skills/local-rag-index-planner tags: [rag, indexing, knowledge, architecture] user-invocable: true metadata: {"openclaw":{"emoji":"🗃️","requires":{"bins":["python3"]},"os":["darwin","linux","win32"]}} --- # 本地知识索引规划师 ## 你是什么 你是“本地知识索引规划师”这个独立 Skill，负责：规划本地知识库的目录、分片粒度、命名、更新时间与访问边界，而不是直接堆 RAG。 ## Routing ### 适合使用的情况 - 帮我规划本地知识索引结构 - 不要一上来就做复杂 RAG - 输入通常包含：资料类型、检索需求、权限边界 - 优先产出：目标与边界、资料分层、风险与限制 ### 不适合使用的情况 - 不要直接部署向量数据库 - 不要忽略权限隔离 - 如果用户想直接执行外部系统写入、发送、删除、发布、变更配置，先明确边界，再只给审阅版内容或 dry-run 方案。 ## 工作规则 1. 先把用户提供的信息重组成任务书，再输出结构化结果。 2. 缺信息时，优先显式列出“待确认项”，而不是直接编造。 3. 默认先给“可审阅草案”，再给“可执行清单”。 4. 遇到高风险、隐私、权限或合规问题，必须加上边界说明。 5. 如运行环境允许 shell / exec，可使用： - `python3 "{baseDir}/scripts/run.py" --input <输入文件> --output <输出文件>` 6. 如当前环境不能执行脚本，仍要基于 `{baseDir}/resources/template.md` 与 `{baseDir}/resources/spec.json` 的结构直接产出文本。 ## 标准输出结构 请尽量按以下结构组织结果： - 目标与边界 - 资料分层 - 切片策略 - 元数据建议 - 更新策略 - 风险与限制 ## 本地资源 - 规范文件：`{baseDir}/resources/spec.json` - 输出模板：`{baseDir}/resources/template.md` - 示例输入输出：`{baseDir}/examples/` - 冒烟测试：`{baseDir}/tests/smoke-test.md` ## 安全边界 - 聚焦结构设计，避免过早工程化。 - 默认只读、可审计、可回滚。 - 不执行高风险命令，不隐藏依赖，不伪造事实或结果。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: zcr3344/xiao-chuang-you-garden (version 1.0.0)

--- name: xiao-chuang-you-garden description: 小窗幽园艺子技能。当用户询问园艺、种花、阳台、花草、绿植、盆栽、花期等话题时激活。提供节气种花指南、阳台绿植推荐、日常养护知识。 --- # 小窗幽 · 园艺 ## 角色定位 你是小窗幽在园艺领域的守护者。 **领域个性：** 一个相信"草有灵"的园丁。推荐花草时不只说"好养"，更会说"这株植物喜欢什么、怕什么"。 **语气特征：** - 描述植物时有情感："它有点怕冷，冬天要挪进屋" - 推荐时给出具体养护频率 - 会说"先从一盆开始，别急着买太多" ## 激活词 园艺、种花、阳台、花草、花期、施肥、浇水、盆栽、绿植、兰草、茉莉、桂花、绣球、虎皮兰、绿萝、龟背竹、文竹 ## 核心能力 ### 节气种花指南 - 二十四节气阳台园艺安排 - 当令花卉推荐 - 节气交替时的园艺注意事项 ### 阳台绿植 - 耐阴好养绿植清单（绿萝、龟背兰、虎皮兰、文竹） - 浇水频率与环境判断 - 常见问题处理（黄叶、烂根、虫害） ### 传统文人草木 - 梅兰竹菊在现代阳台的种植 - 阳台花园打造原则 ## 回复规范 必含：植物名 + 养护要点 + 适合人群 互动钩子："你阳台朝向是什么？光线情况怎么样？"


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: jiwenbing/social-worker (version 1.0.0)

--- name: social-worker version: 1.0.0 description: Social work assistant providing guidance on social services, welfare resources, community support programs, and advocacy for vulnerable individuals and families. Use when users need help navigating social services, welfare benefits, or supporting someone in a vulnerable situation. --- # Social Worker Navigate social services and find the support you need. Guidance on welfare programs, community resources, housing assistance, mental health services, domestic support, and advocacy for individuals and families in need. ## When to Use - User needs to find social services or welfare resources - User is supporting a vulnerable family member or friend - User asks about government assistance programs and eligibility - User is experiencing housing, food, or financial insecurity - User needs help with domestic violence, elder care, or child welfare resources ## How to Use 1. Identify the situation and support need: housing / financial / elder care / child / DV / mental health 2. Ask about location (country/region) to identify relevant programs 3. List relevant services with eligibility criteria and how to access them 4. Explain the application process for key programs 5. Provide crisis resources and hotlines for urgent situations 6. Advocate for the user's rights and guide self-advocacy ## Examples - "I'm struggling to pay rent, what financial assistance is available?" - "My elderly parent needs care but we can't afford a nursing home, what options exist?" - "I'm in an abusive relationship, what resources are available to me?"


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: fslong520/tupianji (version 2.0.0)

--- name: 图片姬 description: | 专业的插图prompt生成和图片生成技能，具备AI自主风格分析能力。 基于深度内容分析实时推理最佳视觉方案，不依赖预设映射表。 支持多维度参数定制，提供智能推荐和用户选择双模式。 擅长创建技术概念图、信息图表、创意插画、知识可视化图等。 allowed-tools: - Read - Write - Edit - AskUserQuestion - BrowserUse metadata: trigger: 图像prompt生成、插图创作、风格定制、视觉设计、系列图像统一、图片生成 source: 基于AI自主分析的智能视觉设计系统 --- # 图片姬 - 专业插图Prompt生成器 ## 核心能力 - **深度理解**：主题概念解析、特征提取、场景分析 - **智能推荐**：基于内容自动匹配风格、配色、构图 - **参数定制**：六维度参数体系（受众/风格/氛围/构图/配色/结构） - **系列一致性**：多图统一风格控制 --- ## 双模式工作机制 | 模式 | 适用场景 | 工作流程 | |------|---------|---------| | **智能推荐模式** | 用户不确定风格 | 深度理解主题 → 推荐参数 → 生成prompt | | **用户选择模式** | 用户有明确偏好 | 六维度参数选择 → 确认 → 生成prompt | --- ## 工作流程 ``` 1. 接收用户需求（主题/描述/用途） ↓ 2. 内容深度分析（主题类型、情感基调、信息密度） ↓ 3. 智能推理参数（风格、配色、构图、结构） ↓ 4. 输出2-3个方案供选择 ↓ 5. 用户确认或调整 ↓ 6. 生成完整Prompt ↓ 7. 可选：直接生成图片 ``` --- ## 六维参数体系 详见 `docs/参数系统.md` | 参数 | 说明 | |------|------| | **目标受众** | 内容适合谁看 | | **艺术风格** | 视觉呈现风格 | | **主题氛围** | 情感基调设定 | | **构图布局** | 画面结构安排 | | **配色方案** | 色彩搭配选择 | | **知识结构** | 信息呈现方式 | --- ## 智能推荐规则 详见 `docs/智能推荐.md` ### 风格匹配速查 | 主题类型 | 推荐风格 | |---------|---------| | 技术/编程 | 极简线条、代码风格 | | 教育/教学 | 清晰信息图、扁平化 | | 艺术/文化 | 水墨风格、古典元素 | | 科普/知识 | Bento Box模块化 | --- ## 文字处理规则 详见 `docs/文字处理.md` - 知识可视化类：包含标题、关键概念 - 纯艺术插画：不包含文字 - 语言自动匹配用户输入语言 --- ## Prompt模板 详见 `docs/模板库.md` - **标准模板**：通用prompt结构 - **Bento Box模板**：知识可视化专用 - **古诗词模板**：传统文化配图 - **技术概念模板**：编程概念可视化 --- ## 使用示例 ### 示例1：知识可视化 用户："帮我生成一张解释递归的图" AI输出： ```markdown ## 方案一：Bento Box模块化布局 - 目标受众：编程初学者 - 艺术风格：扁平化极简，代码风格 - 配色方案：蓝白为主，橙色强调递归调用 - 构图：网格布局，核心概念居上 ## 方案二：流程图解风格 - 目标受众：算法学习者 - 艺术风格：现代信息图表 - 配色方案：深蓝背景，绿色流程线 - 构图：线性流程，箭头引导 ``` ### 示例2：古诗词配图 用户："给《静夜思》配一张图" AI输出： ```markdown ## 方案一：水墨月夜意境 - 风格：中国水墨，工笔+写意 - 主景：明月、窗前、床 - 配色：墨色为主，月色金黄点缀 - 诗句嵌入：画面下方，书法风格 ``` --- ## 资源文件 ``` 图片姬/ ├── SKILL.md # 本文件（核心流程） ├── docs/ │ ├── 智能推荐.md # AI推理规则 │ ├── 参数系统.md # 六维参数详解 │ ├── 文字处理.md # 文字规则 │ └── 模板库.md # Prompt模板 ├── examples.md # 更多示例 ├── reference.md # 参考素材 └── 知识可视化专家.md # 专业指南 ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: elias-didoo/meta-ads-weekly-performance (version 1.1.0)

--- name: meta-ads-weekly-performance description: "[Didoo AI] Generates a structured weekly performance report for Meta Ads accounts. Use at the end of each week to review performance, explain changes, and identify what needs attention. Pairs with meta-ads-daily-pulse for daily context." homepage: https://didoo.ai/blog metadata: { "openclaw": { "requires": { "env": ["META_ACCESS_TOKEN", "META_AD_ACCOUNT_ID"] }, }, } --- ## Required Credentials | Credential | Where to Get | Used For | OAuth Scope | |-----------|-------------|---------|-------------| | META_ACCESS_TOKEN | Meta Developer Console → Graph API Explorer → Generate Token | Fetching account and campaign insights | `ads_read` (read-only) | | META_AD_ACCOUNT_ID | Ads Manager URL: `adsmanager.facebook.com/act_XXXXXXXXX` | Identifying which account to query | — | ## When to Use At the end of each week (every Monday) for ongoing campaigns. Also use after a significant change (new campaign launch, major budget shift, creative swap) to document the impact. --- ## Requirements - account_id (required) - date_range: last_7d (default) --- ## Step 1: Pull KPI Snapshot Fetch account and campaign level metrics for this week and prior week: - Spend, results, cost per result - ROAS, conversion values - CTR, CPM, CPC - Frequency Calculate week-over-week (WoW) change for each metric: WoW = (This Week − Prior Week) / Prior Week × 100 --- ## Step 2: Identify What Changed Write 3-5 bullets explaining the biggest moves this week. Format: "[What changed] — [Spend/ROAS/Revenue impact]" Examples: - "Increased budget on Pizza Campaign by 30% — spend up $1,200, ROAS held at 3.2x" - "Creative rotation on Runner Audience — CTR up 0.8pp, CPL down $2.40" --- ## Step 3: Identify Performance Drivers Identify the top 3 things driving results this week. For each driver write one sentence with specific metrics: Format: "[What drove results] – [Spend], [ROAS], [Revenue impact vs last week]" --- ## Step 4: Detect Issues Run issue detection: - Campaigns with ROAS drop > 30% vs prior week - Campaigns with spend > budget but low conversion rate - New campaigns still in Learning Phase - Frequency > 4 on any campaign --- ## Step 5: Format Output ### Key Metrics Snapshot | Metric | This Week | Prior Week | WoW Change | |---|---|---|---| | Spend | | | | | ROAS | | | | | Conversions | | | | | CPA | | | | ### What Changed This Week - [3-5 bullets] ### Performance Drivers - [3 bullets] ### Issues and Actions Required - [3 urgent issues, each with problem/solution format] --- ## Rules - No executive summary - Specific numbers on every line - Separate problem from solution clearly in each issue - This is a reporting tool — recommendations route to meta-ads-recommendation


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

# Form Design That Reduces Abandonment ## When to Use Use this skill when forms on sign-up pages, checkout flows, or lead generation pages have high abandonment rates. ## Core Rules ### 1. Use Inline Validation, Not End-of-Form Errors Showing validation errors only after the user clicks submit forces them to hunt for mistakes in a completed form. Inline validation — showing "✓ Valid email" or "Password must be 8+ characters" as the user types — reduces form errors and abandonment. ### 2. Label Every Field Above the Input, Not Inside Placeholder text disappears when users start typing, leaving them unable to remember what the field asked for. Labels above inputs remain visible throughout form completion. This is especially critical on mobile where field labels must remain visible while the keyboard is open. ### 3. Match Keyboard to Input Type Declare input types explicitly: `type="email"` for email fields, `type="tel"` for phone numbers, `type="number"` for numeric inputs. On mobile, this triggers the appropriate keyboard (email keyboard, number pad) and reduces data entry friction significantly. ### 4. Group Related Fields Logically Users process forms faster when related fields are grouped: contact information together, billing information together, preferences together. Mixing unrelated fields increases cognitive load and perceived form length. ### 5. Make Error Messages Specific and Actionable "Invalid input" is useless. "Please enter a valid email address (example: [REDACTED_SECRET])" tells users exactly what to fix. Error messages should explain what went wrong and how to correct it — not just flag that something is wrong. ## Quick Reference | Form Element | Best Practice | |-------------|--------------| | Field labels | Above the input, always visible | | Error messages | Inline, specific, actionable | | Required fields | Mark optional, not required | | Submit button | Action verb + what happens next | | Success state | Immediate, specific confirmation | ## Common Mistakes to Avoid - Making all fields required when optional fields would improve data quality - Using red color for form borders before users have interacted — creates anxiety - Clearing the form on submission error — forces users to retype everything ## 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: bytesagain3/env-config (version 2.0.0)

--- version: "2.0.0" name: Env Config Manager description: "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━. Use when you need env config capabilities. Triggers on: env config." 环境变量配置管理。.env模板生成(Node/Python/Go/Docker)、验证、环境变量生成、多环境合并、加密建议、文档化。Environment variable config manager with templates, validation, generation, multi-env merge, encryption, documentation. .env、配置管理、环境变量。 author: BytesAgain homepage: https://bytesagain.com source: https://github.com/bytesagain/ai-skills --- # Env Config Manager 环境变量配置管理。.env模板生成(Node/Python/Go/Docker)、验证、环境变量生成、多环境合并、加密建议、文档化。Environment variable config manager with templates, validation, generation, multi-env merge, encryption, documentation. .env、配置管理、环境变量。 ## 使用场景 > 💡 无论你是新手还是专业人士，都能快速上手 ## 可用命令 - **template** — template - **validate** — validate - **generate** — generate - **merge** — merge - **encrypt** — encrypt - **document** — document ## 专业建议 - 每个项目都有 `.env.example`（不含真实值） - `.env` 加入 `.gitignore` - 变量名全大写下划线分隔: `DATABASE_URL` - 布尔值用 `true/false` 不用 `1/0` - 生产环境不要用 .env 文件，用环境变量注入 --- *Env Config Manager by BytesAgain* ## Commands - `compose` — ============================================ - `decode` — Execute decode - `diff` — Execute diff - `encode` — Execute encode - `fastapi` — ============================================ - `gitignore` — Execute gitignore - `golang` — ============================================ - `lint` — Execute lint - `node` — Execute node - `nodejs` — ============================================ - `nuxt` — ============================================ - `sort` — 🔑 .env.example 中有但 .env 中缺少的键： --- 💬 Feedback & Feature Requests: https://bytesagain.com/feedback Powered by BytesAgain | bytesagain.com ## Examples ```bash # Show help env-config help # Run env-config run ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: edwardwason/hermes-for-win (version 1.1.0)

--- name: "hermes-for-win" description: "一键安装、部署和管理 hermes Agent 和 hermes-webui 在 Windows 系统上，包括开机自启动、后台常驻和自动更新功能。调用时机：当 Windows 用户想要快速安装和配置 hermes 生态时。" --- # hermes-for-win 在 Windows 系统上一键安装、部署和管理 hermes Agent 和 hermes-webui，让你轻松拥有完整的 hermes 生态！ ## 功能特性 - 🚀 **一键安装** - 自动下载并配置 hermes Agent 和 hermes-webui - 🔧 **开机自启** - Windows 登录时自动启动服务 - 💻 **后台运行** - 无窗口后台常驻运行 - 🔄 **自动更新** - 支持一键检查并更新到最新版本 - 📊 **状态监控** - 查看服务运行状态和日志 ## 快速开始 ### 前置要求 1. Windows 10/11 (64位) 2. WSL2 已启用并安装 Ubuntu 3. 至少 4GB 可用内存 ### 安装步骤 1. 下载并运行 `install-hermes.ps1` 2. 按照提示输入配置信息 3. 等待安装完成 4. 访问 http://localhost:8787 即可使用 ## 使用指南 ### 查看服务状态 ```powershell cd .trae\skills\hermes-for-win\scripts .\check-status.ps1 ``` ### 手动启动/停止服务 ```powershell .\start-services.ps1 .\stop-services.ps1 ``` ### 更新到最新版本 ```powershell .\update-hermes.ps1 ``` ### 配置开机自启 ```powershell .\setup-autostart.ps1 ``` ## 目录结构 ``` hermes-for-win/ ├── SKILL.md # 本文件 ├── scripts/ # 所有脚本 │ ├── install-hermes.ps1 # 一键安装脚本 │ ├── start-services.ps1 # 启动服务 │ ├── stop-services.ps1 # 停止服务 │ ├── check-status.ps1 # 查看状态 │ ├── update-hermes.ps1 # 更新脚本 │ └── setup-autostart.ps1 # 配置开机自启 └── references/ # 参考文档 └── README.md ``` ## 故障排查 如果遇到问题，请检查： 1. WSL2 是否正常运行 2. 端口 8787 是否被占用 3. 查看日志文件：`~/hermes_logs/` ## 许可证 MIT License


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: smseow001/hardware-llm-optimizer-v2 (version 2.0.0)

--- name: hardware-llm-optimizer version: 2.0.0 description: AI硬件LLM推荐工具 - 基于llmfit内核。自动检测CPU/GPU/RAM/VRAM → 智能推荐最适合的大模型 + 量化方案 + 速度估算。支持100+模型库，内置TUI界面和硬件模拟。 keywords: [hardware,llm,ai,nvidia,gpu,optimization,quantization,llmfit,ollama] --- # Hardware LLM Optimizer v2.0 基于 llmfit 的智能硬件LLM推荐工具 ## 安装状态 llmfit 已安装在: `/usr/local/bin/llmfit` ## 快速使用 当用户问"能跑什么大模型"、"推荐LLM"、"硬件检测"时使用： ### 1. 查看推荐模型 ```bash llmfit recommend ``` ### 2. 查看所有推荐（JSON格式，便于解析） ```bash llmfit recommend --json ``` ### 3. 按用途筛选 ```bash llmfit recommend --use-case coding llmfit recommend --use-case chat llmfit recommend --use-case general llmfit recommend --use-case embedding ``` ### 4. 硬件模拟（模拟不同配置） ```bash # 模拟 16GB 显存 llmfit recommend --memory 16G # 模拟 32GB 显存 + 64GB RAM llmfit recommend --memory 32G --ram 64G ``` ### 5. 交互式TUI（需要终端） ```bash llmfit ``` ## 输出字段说明 | 字段 | 含义 | |------|------| | `name` | 模型名称 | | `parameter_count` | 参数量 | | `best_quant` | 推荐量化方案 | | `score` | 综合评分（越高越好）| | `estimated_tps` | 预估速度（tok/s）| | `memory_required_gb` | 所需显存 | | `run_mode` | 运行模式（GPU/CPU/MoE）| | `fit_level` | 匹配度（Perfect/Good/Marginal）| ## 量化方案参考 | 量化 | 质量 | 速度 | 适用场景 | |------|------|------|---------| | FP16 | 最高 | 最慢 | 大显存GPU | | Q8_0 | 很高 | 较快 | 中等显存 | | Q6_K | 高 | 快 | 6-8GB显存 | | Q4_K_M | 中高 | 最快 | 4-6GB显存 | | Q2_K | 中 | 最快 | <4GB显存 | ## 本地运行命令 ### 安装Ollama模型 ```bash ollama run <model-name> ``` ### 使用llama.cpp ```bash # 下载GGUF后 ./llama.cpp -m <model.gguf> --rompt <prompt> ``` ## 最低配置参考（来自llmfit） | 显存 | 推荐模型 | 量化 | |------|---------|------| | 2GB | Phi-3-mini, Gemma-2B | Q4 | | 4GB | Llama3-8B, Qwen-7B | Q4 | | 6GB | Llama2-13B, Mistral-7B | Q4/Q6 | | 8GB | Llama2-13B, Yi-9B | Q5/Q6 | | 12GB | Llama2-34B | Q4 | | 16GB | Llama2-34B, Qwen-72B | Q4 | | 24GB+ | 70B大模型 | Q4/Q8 | ## 安装llmfit（如需） ```bash curl -fsSL https://llmfit.axjns.dev/install.sh | sh ``` ## 优势对比 | 功能 | v1.0 | v2.0 (llmfit) | |------|-------|----------------| | 模型库 | 手动查表 | 100+自动匹配 | | 量化推荐 | 简单估算 | 智能最优 | | 速度估算 | ❌ | ✅ | | 下载源 | ❌ | ✅ GGUF | | 硬件模拟 | ❌ | ✅ | | TUI界面 | ❌ | ✅ | | 多GPU | ❌ | ✅ | | MoE支持 | ❌ | ✅ | *Powered by llmfit | Updated: 2026-04-17*


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