OpenClaw/clawhub-security-signals huggingface dataset:
Skill: jolestar/crypto-com-mcp-skill (version 1.0.0)

--- name: crypto-com-mcp-skill description: Use Crypto.com MCP through UXC for exchange market data workflows with help-first discovery and read-only guardrails. --- # Crypto.com MCP Skill Use this skill to run Crypto.com exchange market data workflows through `uxc` + MCP. Reuse the `uxc` skill for shared MCP execution, output parsing, and auth handling. ## Prerequisites - `uxc` is installed and available in `PATH`. - Network access to `https://mcp.crypto.com/market-data/mcp`. - Access to the official Crypto.com MCP docs: - `https://mcp.crypto.com/docs` ## Scope This skill covers the official Crypto.com MCP surface for: - exchange market discovery - ticker and order book style reads - read-only market data workflows This skill does **not** cover: - exchange trading or account writes - private account workflows - non-MCP REST or WebSocket product families ## Endpoint Use the official Crypto.com MCP endpoint: - `https://mcp.crypto.com/market-data/mcp` ## Core Workflow 1. Use the fixed link command by default: - `command -v crypto-com-mcp-cli` - If missing, create it: `uxc link crypto-com-mcp-cli https://mcp.crypto.com/market-data/mcp` 2. Inspect tool and argument help before execution: - `crypto-com-mcp-cli -h` - `crypto-com-mcp-cli get_ticker -h` - `crypto-com-mcp-cli get_book -h` - `crypto-com-mcp-cli get_candlestick -h` 3. Prefer read-only market queries and keep instruments and limits narrow: - `crypto-com-mcp-cli get_ticker instrument_name=BTC_USDT` - `crypto-com-mcp-cli get_book instrument_name=BTC_USDT depth=20` - `crypto-com-mcp-cli get_candlestick instrument_name=BTC_USDT timeframe=1h` ## Operations - `get_instruments` - `get_instrument` - `get_ticker` - `get_tickers` - `get_book` - `get_index_price` - `get_mark_price` - `get_candlestick` - `get_trades` ## Guardrails - Keep automation on the JSON output envelope; do not use `--text`. - Parse stable fields first: `ok`, `kind`, `protocol`, `data`, `error`. - Treat this v1 skill as read-only. - Treat this as market-data only. Do not imply trading, balances, or private account access. - `crypto-com-mcp-cli <operation> ...` is equivalent to `uxc https://mcp.crypto.com/market-data/mcp <operation> ...`. ## References - Usage patterns: `references/usage-patterns.md` - Official Crypto.com MCP docs: https://mcp.crypto.com/docs


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

--- name: interview-questions-zh description: > 专为HR招聘人员和面试官设计的结构化面试题库生成工具，根据岗位名称及要求， 自动生成包含行为面试题、技术能力题、情景模拟题和综合素质题的完整面试题库。 适用于校园招聘、社会招聘、内部晋升等多种场景，支持按岗位级别、部门职能 定制题目难度与侧重点。涵盖"面试题库/面试问题/结构化面试/行为面试/ interview questions/招聘题目/岗位测评"等多种使用场景。 tags: [面试题库, 结构化面试, 行为面试, HR招聘, 情景面试题, interview-questions, 人力资源, 岗位评估] --- # 结构化面试题库生成器 根据岗位信息，自动生成分类清晰、覆盖全面的结构化面试题库，助力HR和面试官高效完成候选人评估。 ## Tools Required - read - write ## Usage - "我需要为产品经理岗位生成一套完整的结构化面试题库，包含行为面试题和情景题" - "帮我出10道适合高级Java工程师的技术面试题，要有参考答案和评分要点" - "我们下周要招销售总监，请帮我设计一套包含行为面试和情景模拟的面试问题清单" - "生成一份适合校招应届生的综合素质面试题，岗位是市场专员" ## Examples 输入：产品经理（3-5年经验），重点考察用户需求分析、跨部门协作和数据驱动决策能力 输出： **【行为面试题】** 1. 请描述一次你发现产品数据异常并推动改进的经历，你是如何识别问题、分析原因并协调资源解决的？ - 参考要点：数据敏感度、问题拆解逻辑、跨团队沟通能力 **【情景模拟题】** 2. 假设研发团队评估某核心需求需要3个月，但业务方要求1个月上线，你会如何处理这一冲突？ - 参考要点：优先级判断、需求拆解能力、stakeholder管理 **【技术能力题】** 3. 请说明你常用的用户需求收集与验证方法，并举例说明如何避免需求失真？ - 参考要点：用户访谈、问卷设计、A/B测试、需求文档规范 **【综合素质题】** 4. 回顾你过去负责的产品，哪个版本迭代你最不满意？原因是什么，你从中学到了什么？ - 参考要点：自我反思能力、成长心态、复盘思维


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: wangzhiming1999/bug-investigation (version 1.0.1)

--- name: bug-investigation description: Systematically reproduces, locates, and diagnoses frontend bugs using steps, hypotheses, DevTools, and minimal repro. Use when 排查bug, bug定位, 调试, debugging, 复现问题, or investigating frontend issues. --- # Bug 排查（Bug Investigation） 用可复现步骤、假设验证和工具链，快速定位并诊断前端问题根因。 ## 触发场景 - 用户说「排查 bug」「定位问题」「复现不了」「不知道哪里错了」 - 描述现象：白屏、报错、样式错乱、请求失败、交互异常等 - 提供错误信息、截图、环境（浏览器、设备、网络） ## 执行流程 ### 1. 明确现象与环境 - **现象**：用户操作路径 + 预期 vs 实际（一句话描述） - **环境**：浏览器及版本、是否生产/预发/本地、设备/分辨率、网络（慢网/离线） - **必现/偶现**：若偶现，补充可能相关条件（数据量、时机、多 tab 等） ### 2. 复现与缩小范围 - 写出**最小复现步骤**（3–5 步内能复现） - 判断范围：仅某页面 / 某组件 / 某接口 / 某浏览器 / 某分辨率 - 若无法复现：请用户提供录屏、Console 报错、Network 失败请求，或可复现的测试数据/账号 ### 3. 假设与验证 按优先级做假设并逐条验证（用「是/否」结论）： | 假设类型 | 验证方式 | |----------|----------| | 前端逻辑/状态 | 断点、console.log、React DevTools 状态 | | 接口/数据 | Network 看请求与响应、mock 数据对比 | | 样式/布局 | 审查元素、Computed、覆盖样式来源 | | 时机/异步 | 执行顺序、Promise/async、事件触发顺序 | | 环境/兼容 | 换浏览器、无痕、禁用扩展、不同设备 | ### 4. 定位根因 - 指出**具体文件 + 位置（行/函数/组件）** - 用一句话说明**根因**：例如「在 XX 条件下未做判空导致取属性报错」 - 若为接口或后端问题：明确说明「这是后端问题，前端能做的是：1) 加 loading/错误态兜底 2) 给用户友好提示 3) 上报错误日志」，不要继续往前端代码里找 ### 5. 修复建议 - 给出最小改动修复方案（代码片段或步骤） - 必要时补充：边界情况、测试建议、类似位置是否需一并检查 ## 输出模板 ```markdown ## Bug 排查报告 ### 现象 - 操作：… - 预期：… / 实际：… ### 环境 - 浏览器/设备/网络：… ### 复现步骤 1. … 2. … ### 假设与验证 - [ ] 假设 1：… → 验证结果 - [ ] 假设 2：… → 验证结果 ### 根因 - 位置：文件:行 或 组件名 - 原因：… ### 修复建议 - 方案：… - 注意：… ``` ## 常用手段 - **Console**：报错栈、`console.trace`、断点 - **Network**：状态码、响应体、请求头、是否被缓存 - **Elements**：计算样式、覆盖关系、伪元素 - **React DevTools**：组件树、props/state、重渲染 - **Performance / Lighthouse**：卡顿、长任务、布局抖动 - **最小复现**：新建单页或 CodeSandbox 只保留必要代码与数据


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: 1kalin/afrexai-agent-runbook (version 1.1.0)

# Agent Ops Runbook Generate a production-ready operations runbook for deploying AI agents. Covers pre-deployment checklists, shadow mode → supervised → autonomous rollout stages, monitoring dashboards, rollback procedures, cost management, and incident response templates. ## When to Use - Deploying an AI agent to production - Building monitoring and alerting for agent systems - Creating rollback procedures for autonomous workflows - Estimating and controlling agent operational costs ## Instructions When the user asks for an agent ops runbook or deployment plan: 1. Ask which agent function they're deploying (support, sales, document processing, etc.) 2. Ask about their risk tolerance (conservative, moderate, aggressive rollout) 3. Generate a complete runbook with: - Pre-deployment checklist specific to their function - 3-stage rollout plan with metrics and gates - Monitoring alerts (critical + warning thresholds) - Rollback procedures (3 levels: prompt, feature, full) - Cost estimates based on their expected volume - 90-day implementation timeline - Incident response template 4. Include specific metric targets: - Accuracy vs human baseline: >90% - Error rate: <2% - Cost per task benchmarks by function - Human escalation rate: 5-15% 5. Flag risks specific to their industry (compliance, PII, financial accuracy) Output format: Markdown document ready to share with engineering and ops teams.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: dobbybud/agent-task-manager (version 1.0.0)

--- name: agent-task-manager description: Manages and orchestrates multi-step, stateful agent workflows; handles task dependencies, persistent state, error recovery, and external rate-limiting. Use for creating new multi-agent systems, improving sequential workflows, or managing time-bound actions. --- # Agent Task Manager ## Overview This skill provides the structure and primitives for building resilient, complex, and professional multi-agent systems within the OpenClaw environment. It transforms simple scripts into production-ready workflows. ## Core Capabilities ### 1. **Orchestration and Task State** - **Capability:** Defines tasks with clear inputs, outputs, and dependencies (DAG-like structure). - **Execution:** Uses `molt_task.py` to manage state in `task_state.json`. - **Value:** Prevents redundant work, allows agents to resume mid-workflow after a session reset. ### 2. **External Rate-Limit Management** - **Capability:** Manages the cooldown and retry logic for externally rate-limited actions (e.g., API posts, web scrapes). - **Execution:** Uses the `scripts/cooldown.sh` wrapper to store last-executed timestamps and automatically wait/retry. - **Value:** Ensures continuous operation in environments like Moltbook without violating API rules. ### 3. **Modular Role-Based Agents** - **Capability:** Provides a template structure for specialized roles (e.g., `ContractAuditor`, `FinancialAnalyst`). - **Execution:** Modules are designed to be run independently or sequenced by the Orchestrator. - **Value:** Enables the creation of focused, expert agents for complex tasks like the MoltFinance-Auditor. ## Example Workflow: MoltFinance-Auditor 1. **Task:** `FinancialAudit` 2. **Dependencies:** - **Role 1:** `ContractAuditor` (Input: Contract Address, Output: Contract Safety Score) - **Role 2:** `FinancialAnalyst` (Input: Contract Address + Safety Score, Output: Trust Score) 3. **External Action:** `MoltbookPost` (Dependent on final Trust Score; subject to Rate Limit). ## Resources ### scripts/ - **`molt_task.py`**: Python class for task state management. - **`cooldown.sh`**: Shell wrapper for managing rate-limited executions. ### references/ - **`workflow_schema.md`**: JSON schema for defining complex task dependencies. - **`rate_limit_patterns.md`**: Guide to handling common API rate limits (e.g., Moltbook, Helius).


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (MEDIUM)
Skill: tlreal/ooooooooo (version 0.1.0)

--- name: core-executing-plans description: 当有实施计划需要批量执行时使用，需要人工检查点 --- # Executing Plans - 执行实施计划 ## 概述 批量执行实施计划中的任务，在关键点设置人工检查点。 **与 subagent-driven-development 的区别:** - executing-plans: 批量执行，人工检查点 - subagent-driven-development: 每个任务派发子代理，自动审查 ## 流程 ### 1. 加载计划 读取计划文件，理解: - 总体目标 - 任务列表 - 依赖关系 - 验证步骤 ### 2. 批量执行 按顺序执行任务，每个任务: 1. 宣布当前任务 2. 遵循 TDD 循环（如果适用） 3. 验证任务完成 4. 提交代码 ### 3. 检查点 在以下时机暂停并请求人工确认: - 每 3-5 个任务后 - 遇到不确定的设计决策 - 测试失败需要决策 - 完成重大里程碑 ### 4. 完成 所有任务完成后: - 汇总完成情况 - 运行完整测试套件 - **REQUIRED SUB-SKILL:** 使用 `core-finishing-branch` ## 检查点模板 ``` ✅ 已完成任务 1-3: 1. [任务1描述] - 完成 2. [任务2描述] - 完成 3. [任务3描述] - 完成 📋 下一批任务 4-6: 4. [任务4描述] 5. [任务5描述] 6. [任务6描述] 继续吗？或者有什么需要调整的？ ``` ## 领域扩展钩子 **如果是嵌入式项目，执行时应:** - **RECOMMENDED:** 硬件相关任务使用 `embedded-hardware-debug` 验证 - **RECOMMENDED:** 驱动开发任务使用 `embedded-driver-development` - **RECOMMENDED:** 集成任务使用 `embedded-rtos-integration` ## 注意事项 - 严格遵循计划顺序 - 不要跳过验证步骤 - 遇到问题立即停止并报告 - 保持频繁提交


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

--- name: scamper description: Apply SCAMPER creative thinking method to develop ideas, adapt frameworks, generate hackathon concepts, or break through when stuck. Use when Enzo says "SCAMPER this", asks to develop/expand an idea, wants hackathon concepts from existing tools, says he's stuck, or when processing new ideas in the ideas inbox. --- # SCAMPER Method Creative thinking framework for innovation and problem-solving. ## The 7 Lenses | Letter | Lens | Prompt | |--------|------|--------| | **S** | Substitute | What can be replaced? Different material, person, process, component? | | **C** | Combine | What can merge? Blend features, ideas, purposes, audiences? | | **A** | Adapt | What else is like this? What could be copied or borrowed from elsewhere? | | **M** | Modify | What can change? Size, shape, color, frequency, intensity, meaning? | | **P** | Put to other uses | What else could this be used for? New contexts, audiences, problems? | | **E** | Eliminate | What can be removed? Simplify, reduce, strip to essentials? | | **R** | Reverse | What if opposite? Flip the order, roles, perspective, direction? | ## Application Modes ### Quick SCAMPER (default) Pick 2-3 most relevant lenses, generate 1 strong idea each. Use for: casual brainstorming, quick expansions ### Full SCAMPER All 7 lenses, 1-2 ideas each. Use for: serious idea development, comprehensive exploration ### Targeted SCAMPER Enzo specifies which lens(es) to focus on. Use for: when he knows what angle he wants ## Integration Points ### New Idea Saved After saving to `notes/ideas.md`, offer: "Want me to SCAMPER this?" If yes → run Quick SCAMPER, append output to the idea entry. ### Framework Adaptation When saving a framework, optionally run SCAMPER to personalize: - **Substitute**: What would you swap for your industry/role? - **Combine**: What other framework could this merge with? - **Adapt**: Who does something similar you could borrow from? ### Hackathon Ideation For generating hackathon concepts from existing products: 1. Take the product/tool 2. Run full SCAMPER 3. Filter for "impressive demo in 24h" feasibility 4. Output top 3 concepts ### Stuck Breaker When Enzo says "I'm stuck on X": 1. Clarify the blocker 2. Run SCAMPER on the problem 3. Focus on **Reverse** and **Eliminate** (often best for unblocking) ## Output Format ```markdown ### SCAMPER: [Subject] **Substitute:** [idea] **Combine:** [idea] **Adapt:** [idea] **Modify:** [idea] **Put to other uses:** [idea] **Eliminate:** [idea] **Reverse:** [idea] 💡 **Strongest angle:** [which one and why] ``` For Quick SCAMPER, only include the 2-3 lenses used.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: lky115/est321 (version 1.0.0)

--- name: core-brainstorming description: 在任何创造性工作之前必须使用 - 创建功能、构建组件、添加新功能或修改行为。通过协作对话探索用户意图、需求和设计。 --- # Brainstorming - 将想法转化为设计 ## 概述 通过自然的协作对话，帮助将模糊的想法转化为完整的设计和规格说明。 首先了解当前项目上下文，然后逐个提问来完善想法。一旦理解了要构建的内容，以小节形式（200-300字）呈现设计，每节后确认是否正确。 ## 流程 ### 理解想法 - 首先查看当前项目状态（文件、文档、最近提交） - 逐个提问来完善想法 - 尽可能使用选择题，开放题也可以 - 每条消息只问一个问题 - 如果需要更多探索，分多个问题 - 聚焦于理解：目的、约束、成功标准 ### 探索方案 - 提出 2-3 种不同方案及其权衡 - 以对话方式呈现选项，包括推荐和理由 - 优先展示推荐方案并解释原因 ### 呈现设计 - 确信理解了要构建的内容后，呈现设计 - 分成 200-300 字的小节 - 每节后询问是否正确 - 涵盖：架构、组件、数据流、错误处理、测试 - 准备好返回澄清不清楚的地方 ## 设计完成后 ### 文档输出 - 将验证通过的设计写入 `docs/plans/YYYY-MM-DD-<topic>-design.md` - 提交设计文档到 git ### 继续实施（如果需要） - 询问：「准备好开始实施了吗？」 - **REQUIRED SUB-SKILL:** 使用 `core-writing-plans` 创建详细实施计划 ## 领域扩展钩子 **如果是嵌入式项目：** - **RECOMMENDED:** 使用 `embedded-datasheet-analysis` 分析硬件数据手册 - **RECOMMENDED:** 使用 `embedded-feasibility-assessment` 评估硬件约束 - **RECOMMENDED:** 使用 `embedded-platform-selection` 选择合适平台 **如果是仿真项目：** - **RECOMMENDED:** 使用 `simulation-requirements` 分析仿真需求 - **RECOMMENDED:** 使用 `simulation-architecture` 设计仿真架构 ## 核心原则 - **一次一个问题** - 不要用多个问题压垮用户 - **首选选择题** - 比开放题更容易回答 - **YAGNI 原则** - 从所有设计中移除不必要的功能 - **探索替代方案** - 在确定之前始终提出 2-3 种方案 - **增量验证** - 分节呈现设计，验证每节 - **保持灵活** - 当某些内容不清楚时返回澄清


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ahsbnb/live-script-planner (version 1.0.0)

--- name: live-script-planner description: 根据用户输入（产品、目标、直播类型等）生成专业的直播间口播、互动和逼单脚本。支持参数化调用，可指定带货类/种草类/品牌宣讲类等不同风格。 --- # SKILL.md for live-script-planner ## Description 这是一个参数化的直播脚本策划技能，可根据用户传入的参数生成不同风格的直播话术方案。支持**带货类**、**种草类**、**品牌宣讲类**等多种直播类型。 ## Core Parameters | 参数名 | 必填 | 说明 | |---|---|---| | `live_stream_type` | 否 | 直播类型 (带货类/种草类/品牌宣讲类) | | `product_name` | 是 | 产品名称 | | `key_selling_points` | 是 | 核心卖点（3-5 个） | | `promotional_details` | 是 | 优惠信息 | | `live_stream_duration` | 否 | 直播时长 (e.g., 60 分钟) | ## Dependencies - `prompt_template.txt` - 核心提示词模板


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: freedompixels/cn-color-scheme-generator (version 1.0.0)

--- slug: cn-color-scheme-generator name: Color Scheme Generator version: "1.0.0" description: "Generate color schemes from a base color. Support complementary, triadic, analogous schemes. Pure Python standard library, no API key required." keywords: color, scheme, palette, design, complementary license: MIT-0 tags: - tools --- # Color Scheme Generator Generate beautiful color palettes from a base color. ## Features - Complementary color scheme (opposite on color wheel) - Triadic color scheme (3 evenly spaced colors) - Analogous color scheme (adjacent colors) - Pure Python, no external dependencies ## Color Theory - **Complementary**: Colors opposite each other on the color wheel. High contrast, vibrant combinations. - **Triadic**: Three colors evenly spaced. Balanced and visually appealing. - **Analogous**: Colors next to each other. Harmonious and natural-looking. ## Usage ``` python3 scripts/color_scheme.py --base "#ff5733" ``` ## Example Output ```json { "base": "#ff5733", "schemes": { "complementary": ["#ff5733", "#33d6ff"], "triadic": ["#ff5733", "#33ff57", "#5733ff"], "analogous": ["#ff5733", "#ffa033", "#ff3357"] } } ``` ## Use Cases - UI/UX design - Brand color palette creation - Data visualization color schemes


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ryudi84/sovereign-aws-cost-optimizer (version 1.0.0)

--- name: AWS Cost Optimizer description: Analyzes AWS infrastructure for cost savings. Right-sizing, Reserved Instances, Savings Plans, unused resources. --- # AWS Cost Optimizer Analyzes AWS infrastructure for cost savings. Right-sizing, Reserved Instances, Savings Plans, unused resources. You are a specialized assistant for this task. Provide detailed, actionable output with code examples, configuration files, and architecture diagrams where relevant. Follow industry best practices and explain trade-offs when multiple approaches exist. Always consider security, scalability, and maintainability.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: 43622283/li-nvvideocodec (version 1.0.1)

--- name: li_nvvideocodec description: NVIDIA AV1视频批量压缩工具，使用FFmpeg调用NVIDIA NVENC硬件编码，支持智能压缩验证和多方案选择 version: 1.0.1 tags: - video - compression - nvidia - av1 - ffmpeg - gpu - multilingual compatible_agents: - hermes - openclaw - qwen-code languages: - en - ja - ko - zh-CN - zh-TW - fr - de - es - ru - ar --- # li_nvvideocodec - NVIDIA AV1 视频压缩工具 ## 🌍 多语言支持 / Multi-Language Support 本工具支持10种语言： | 语言 | 代码 | 文档路径 | |------|------|----------| | 🇺🇸 English | en | `locales/en/README.md` | | 🇯🇵 日本語 | ja | `locales/ja/README.md` | | 🇰🇷 한국어 | ko | `locales/ko/README.md` | | 🇨🇳 简体中文 | zh-CN | `locales/zh-CN/README.md` | | 🇹🇼 繁體中文 | zh-TW | `locales/zh-TW/README.md` | | 🇫🇷 Français | fr | `locales/fr/README.md` | | 🇩🇪 Deutsch | de | `locales/de/README.md` | | 🇪🇸 Español | es | `locales/es/README.md` | | 🇷🇺 Русский | ru | `locales/ru/README.md` | | 🇸🇦 العربية | ar | `locales/ar/README.md` | ## 📋 功能简介 使用NVIDIA GPU硬件加速的AV1视频批量压缩工具，可以： - 🎯 **智能验证** - 自动测试压缩效果，避免无效压缩 - 📊 **三种方案** - 保守/平衡/激进，满足不同需求 - 🖥️ **双平台** - 支持Windows和Ubuntu Linux - 📈 **实时进度** - 显示压缩进度和详细统计 - 🔒 **安全保护** - 原文件不删除，输出到独立目录 **兼容Agent**: hermes, openclaw, qwen-code ## 🚀 快速使用 ### 基本用法 ```bash # 交互式模式（会引导你选择参数） python scripts/compress_videos.py # 指定目录和方案 python scripts/compress_videos.py -i "输入目录" -p B # 测试模式（只压缩1个视频验证） python scripts/compress_videos.py -i "输入目录" -p B --test --test-count 1 --no-confirm # 完整压缩（非交互） python scripts/compress_scripts/compress_videos.py -i "输入目录" -p B --no-confirm ``` ## 📊 压缩方案 | 方案 | 分辨率 | CRF | 帧率 | 音频 | 预估节省 | 适用场景 | |------|--------|-----|------|------|----------|----------| | **A** | 保持原样 | 23 | 保持 | 128k | 40-60% | 追求质量 | | **B** ⭐ | 1280x720 | 24 | 24fps | 96k | 65-75% | **平衡推荐** | | **C** | 1280x720 | 28 | 15fps | 64k | 78-85% | 最大节省 | ## ⚙️ 系统要求 ### 必需 - ✅ **FFmpeg** - 需要支持av1_nvenc编码器 - ✅ **NVIDIA GPU** - GTX 1650及以上（支持NVENC） - ✅ **NVIDIA驱动** - 已安装并正常工作 - ✅ **Python 3.7+** - 运行脚本 ### 可选 - tqdm库 - 显示进度条（脚本会自动安装） ### 安装FFmpeg **Windows:** ```bash # 下载: https://ffmpeg.org/download.html # 解压后将ffmpeg.exe添加到PATH ``` **Ubuntu:** ```bash sudo apt update sudo apt install ffmpeg ``` ## 📝 完整参数 | 参数 | 简写 | 说明 | 示例 | |------|------|------|------| | `--input` | `-i` | 输入视频目录 | `-i "E:\视频"` | | `--output` | `-o` | 输出目录 | `-o "F:\压缩后"` | | `--profile` | `-p` | 压缩方案 (A/B/C) | `-p B` | | `--test` | | 测试模式 | `--test` | | `--test-count` | | 测试文件数 | `--test-count 5` | | `--no-confirm` | | 跳过确认 | `--no-confirm` | ## 🔍 工作流程 ``` 1. 环境检查 ↓ 检查FFmpeg、GPU、编码器支持 ↓ 2. 选择压缩方案 ↓ A（保守）/ B（平衡）/ C（激进） ↓ 3. 验证压缩效果 ↓ 测试压缩1个小视频，对比大小 ↓ 4. 智能判断 ↓ 如果有效 → 继续批量压缩 如果无效 → 提示取消任务 ↓ 5. 批量压缩 ↓ 显示进度、统计信息 ↓ 6. 生成报告 ``` ## ⚠️ 重要提示 ### B站视频等特殊情況 如果视频来自B站（Bilibili）等平台： - 这些视频已被高度压缩（低码率） - AV1压缩可能**不会节省空间** - 脚本会自动检测并提示 ### 原文件保护 - ✅ 原文件**不会删除** - ✅ 压缩文件输出到**独立目录** - ✅ 可以安全对比测试 ## 📞 技术支持 如遇问题，请检查： 1. NVIDIA驱动是否正常（`nvidia-smi`） 2. FFmpeg是否安装（`ffmpeg -version`） 3. FFmpeg是否支持av1_nvenc（`ffmpeg -encoders | grep av1_nvenc`） ## 📄 许可证 作者: 北京老李 (beijingLL)

path: requirements.txt
content: tqdm>=4.60.0


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: certainlogicai/smart-router-intents (version 1.0.1)

--- name: smart-router-intents version: 1.0.1 description: "Route any query by intent. Code, analysis, creative, realtime, general. Each goes to the cheapest model that can handle it." author: "CertainLogic <[REDACTED_SECRET]>" license: MIT homepage: https://certainlogic.ai repository: https://github.com/CertainLogicAI/smart-router-intents tags: ["model-routing", "intent-detection", "cost-reduction", "ai-costs", "smart-router", "open-source"] --- # smart-router-intents Route any query by intent. Code, analysis, creative, realtime, general. Each goes to the cheapest model that can handle it. ## How it works ``` INPUT: any query ↓ Is it cached? → Return instantly (0 cost) ↓ if no Detect intent: CODE write / debug / refactor → Qwen3.5 Flash (fast) or Grok 4.20 ANALYSIS research / explain / compare / evaluate → Grok 4.20 (standard) or Opus 4.8 (deep) CREATIVE write / brainstorm / design / draft → Grok 4.20 REALTIME news / prices / weather / current events → Grok 4.20 (live data) GENERAL Q&A / summarize / translate / chat → Qwen3.5 Flash ``` ## Speed Router adds 2ms. The rest is the LLM you'd wait for anyway. | Setup | Average wait per query | |-------|----------------------| | All Opus 4.8 | 45s | | All Grok 4.20 | 11s | | **With router** | **~13s** | ## What you save | Setup | Cost per 1k queries | |-------|-------------------| | All Opus 4.8 | $15 | | All Grok 4.20 | $4 | | All Qwen3.5 Flash | $0.80 | | **With router** | **~$3** | ## Intent detection detail ``` CODE Keywords: write, code, debug, fix, refactor, function, class, test → Qwen3.5 Flash (fast), Grok 4.20 (complex) ANALYSIS Keywords: analyze, explain, compare, research, evaluate, assess → Grok 4.20. Opus 4.8 for audits. CREATIVE Keywords: write a story, brainstorm, imagine, design, draft → Grok 4.20 REALTIME Keywords: now, today, current, latest, news, price, weather → Grok 4.20 (requires live data) GENERAL Default. Simple Q&A, translate, summarize, chat → Qwen3.5 Flash ``` ## Usage ```bash clawhub install smart-router-intents ``` ## Author Created by CertainLogic — deterministic AI infrastructure. certainlogic.ai


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