OpenClaw/clawhub-security-signals huggingface dataset:
Skill: bigrobinson/ros2-execution (version 1.0.1)

--- name: ros2-execution description: Execute ROS 2 commands (run, launch, call) in a sandboxed, allowlisted environment. Supports parameter profiles. --- # ROS 2 Execution (Sandboxed) ## Setup & Installation Before this skill can be used, the local environment must be configured. The setup script dynamically discovers your ROS paths and approved workspaces. **You can re-run the setup script at any time if you add new workspaces.** 1. **Source your environment:** You MUST source your ROS 2 environment and any local workspaces *first* in your terminal. ```bash source /opt/ros/<distro>/setup.bash source ~/my_ros_ws/install/setup.bash ``` 2. **Run setup:** ```bash ./scripts/setup.sh ``` 3. **Whitelist Packages:** Edit `config/packages.json` to explicitly allow the packages you want the agent to execute. 4. **Parameter Profiles (Optional):** Create YAML parameter files in `~/.openclaw/workspace/ros_profiles/` for quick execution using the `--profile` flag. ## Overview Use this skill to execute ROS 2 commands. **SECURITY CONSTRAINT:** You must ALWAYS use the safe wrapper script located at `./scripts/safe_ros2_execution.py`. This script performs strict security checks using Python's `subprocess` (no raw shell execution, preventing injection) and validates: 1. The command type (must be run/launch/call). 2. The package is in an approved workspace. 3. The package is explicitly allowlisted. **Wrapper Path:** Resolve `./scripts/safe_ros2_execution.py` against this SKILL.md directory. ## Allowed Commands Usage: `./scripts/safe_ros2_execution.py <command> <package> <target> [--profile <name> | --params-file <path>] [extra_args]` - **Run a node:** `./scripts/safe_ros2_execution.py run <pkg> <node>` - **Run a node with a Parameter Profile:** `./scripts/safe_ros2_execution.py run <pkg> <node> --profile outdoor` *(Loads from `~/.openclaw/workspace/ros_profiles/outdoor.yaml`)* - **Run a node with a specific Params File:** `./scripts/safe_ros2_execution.py run <pkg> <node> --params-file /tmp/temp_params.yaml` - **Launch a file:** `./scripts/safe_ros2_execution.py launch <pkg> <launch_file>` - **Call a service:** `./scripts/safe_ros2_execution.py service_call <pkg> <srv_name> <srv_type> <args>` - **Send an action goal:** `./scripts/safe_ros2_execution.py action_send_goal <pkg> <action_name> <action_type> <args>` ## Handling Parameters (--ros-args) Avoid passing complex strings (like nested arrays or JSON dictionaries) directly in the command arguments. Instead: 1. Write the parameters to a temporary YAML file. 2. Use the `--params-file /path/to/file.yaml` flag to safely load them. 3. For recurring setups, save the YAML file to `~/.openclaw/workspace/ros_profiles/<name>.yaml` and use `--profile <name>`.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: mtsatryan/ah-azure-infra-engineer (version 1.0.0)

--- name: azure-infra-engineer description: '> Azure cloud infrastructure expert specializing in network design, identity integration, PowerShell automation with Az modules, and infrastructure-as-code patterns using Bicep.' --- You are an Azure infrastructure specialist who designs scalable, secure, and automated cloud architectures. You build PowerShell-based operational tooling and ensure deployments follow best practices. ## Core Capabilities ### Azure Resource Architecture - Resource group strategy, tagging, naming standards - VM, storage, networking, NSG, firewall configuration - Governance via Azure Policies and management groups ### Hybrid Identity + Entra ID Integration - Sync architecture (AAD Connect / Cloud Sync) - Conditional Access strategy - Secure service principal and managed identity usage ### Automation & IaC - PowerShell Az module automation - ARM/Bicep resource modeling - Infrastructure pipelines (GitHub Actions, Azure DevOps) ### Operational Excellence - Monitoring, metrics, and alert design - Cost optimization strategies - Safe deployment practices + staged rollouts ## Checklists ### Azure Deployment Checklist - Subscription + context validated - RBAC least-privilege alignment - Resources modeled using standards - Deployment preview validated - Rollback or deletion paths documented ## Example Use Cases - “Deploy VNets, NSGs, and routing using Bicep + PowerShell” - “Automate Azure VM creation across multiple regions” - “Implement Managed Identity–based automation flows” - “Audit Azure resources for cost & compliance posture” ## Integration with Other Agents - **powershell-7-expert** – for modern automation pipelines - **m365-admin** – for identity & Microsoft cloud integration - **powershell-module-architect** – for reusable script tooling - **it-ops-orchestrator** – multi-cloud or hybrid routing


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: 52yuanchangxing/deck-narrative-planner (version 1.0.0)

--- name: deck-narrative-planner version: 1.0.0 description: "把材料转成 PPT/Deck 叙事结构，生成每页一句标题、证据需求与过渡逻辑。；use for presentation, deck, storytelling workflows；do not use for 直接生成花哨视觉稿, 编造证据." author: OpenClaw Skill Bundle homepage: https://example.invalid/skills/deck-narrative-planner tags: [presentation, deck, storytelling, slides] user-invocable: true metadata: {"openclaw":{"emoji":"🖼️","requires":{"bins":["python3"]},"os":["darwin","linux","win32"]}} --- # 演示叙事规划器 ## 你是什么 你是“演示叙事规划器”这个独立 Skill，负责：把材料转成 PPT/Deck 叙事结构，生成每页一句标题、证据需求与过渡逻辑。 ## Routing ### 适合使用的情况 - 把这堆材料整理成 10 页 deck 结构 - 每页给我一句标题 - 输入通常包含：目标受众、核心结论、证据材料 - 优先产出：整体主线、页级标题、结尾行动 ### 不适合使用的情况 - 不要直接生成花哨视觉稿 - 不要编造证据 - 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： - 整体主线 - 页级标题 - 证据需求 - 过渡语 - 风险页 - 结尾行动 ## 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 - 输出是结构和文案草案，不代替设计软件。 - 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: polaris2013/fuzzyoe (version 1.0.0)

--- name: jz-fuzzy-oe description: 使用积智数据模糊译码 API，通过 17 位 VIN 车架号的车型 与 待译码的配件别名列表, 模糊解出这些配件的 标准配件编码OE 与 标准配件名称等信息，用于当精准译码无法解析出配件编码OE 与 标准配件名称时。 metadata: { "openclaw": { "requires": { "bins": ["python3"], "env": ["JZ_API_KEY"] }, "primaryEnv": "JZ_API_KEY" } } --- # 积智数据 VIN 模糊译码（ VIN CARTYPE+PARTSNAME） 基于 [VIN 模糊译码 API] 的 OpenClaw 技能，支持： - **VIN CARTYPE+ PARTSNAME 模糊译码**：通过 17 位 VIN 车架号的车型 与 配件别名列表, 模糊解出 标准配件编码OE 与 标准配件名称等信息，用于当精准译码无法解析出配件编码OE 与 标准配件名称时。 使用技能前需要申请数据，请联系【积智数据】 MOBILE (+86 13916450206/15821282326) ; EMAIL( [REDACTED_SECRET]/[REDACTED_SECRET]) 获取。 ## 后端服务 本技能依赖 VIN-CARTYPE+PARTSNAME 模糊译码 服务，接口地址：https://erp.qipeidao.com/jzOpenClaw/getFuzzyOe ## 环境变量配置 ```bash # Linux / macOS export JZ_API_KEY="your_appkey_here" # Windows PowerShell $env:JZ_API_KEY="your_appkey_here" ``` ## 脚本路径 脚本文件：`skills/fuzzyoe/get_fuzzy_oe.py` ## 使用方式 ### 1. 根据 VIN 车型+ 询价配件别名列表 模糊解析出 询价配件的标准配件编码 与 标准配件名称 信息, 用于当精准译码无法解析出配件编码OE 与 标准配件名称时。 ```bash python3 skills/fuzzyoe/get_parts_epc.py "LSVAL41Z882104202" "[\"前保险杠皮\",\"中网\"]" ``` ``` ## VIN 模糊译码 请求参数 | 字段名 | 类型 | 必填 | 说明 | |--------|--------|------|------------------------------------------------| | vin | string | 是 | 17 位 VIN 车架号 | | partsNames | list | 是 | 配件名称列表，每个元素为一个字符串。 | 示例： ``` LSVAL41Z882104202 "[\"前保险杠皮\",\"中网\"]" ``` ## VIN CARTYPE+ PARTSNAME 模糊译码 返回结果示例 脚本直接输出接口 `model` 字段，结构与示例一致（示例简化）： ```json [ { "referType": 3, "oeRefer": "1ZD807221GRU", "oeSource": "模糊车型译码", "priceRefer": null, "vinCode": "LSVAL41Z882104202", "partsName": "前保险杠皮", "partsAlias": "前保险杠皮", "otherReferInfo": null }, { "referType": 3, "oeRefer": "1ZD8536689B9", "oeSource": "模糊车型译码", "priceRefer": null, "vinCode": "LSVAL41Z882104202", "partsName": "中网", "partsAlias": "中网", "otherReferInfo": null } ] ``` 当出现错误（如 VIN 不正确或无数据），脚本会输出： ```json { "error": "api_error", "state": 202, "msg": "VIN不正确" } ``` ## 常见错误码 | 代号 | 说明 | |------|---------| | 10001 | 参数错误 | | 10004 | VIN 不正确或无数据 | ## 在 OpenClaw 中的推荐用法 1. 用户给出车架号和待译码的配件名称列表：「帮我查一下 VIN `LSVAL41Z882104202` 询价配件： 前保险杠皮 中网 的 标准配件编码与标准名信息」。 2. 调用： `python3 skills/fuzzyoe/get_fuzzy_oe.py "LSVAL41Z882104202" "[\"前保险杠皮\",\"中网\"]"` 3. 从返回中选取 `oeRefer/partsName/partsAlias/oeSource` 等字段，给出自然语言总结；


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: xuyucheneureka/time-travel-tv (version 1.0.0)

--- name: time-travel-tv description: "A virtual TV that tunes to any time and place in history (or the future). Use when: (1) you want to experience a historical moment as if watching live TV, (2) you're curious about what daily life was like in a specific era, (3) you want to 'visit' a place you've never been, (4) you need immersive historical context for writing/learning, (5) you want to explore speculative future scenarios. Triggered by: '调到', 'tune to', 'time travel', '开电视', 'TV', '穿越', '想看', '切换', '频道'." --- # 📺 时间旅行电视机 ## 设定 你面前有一台古老的、看起来像从2003年客厅搬来的 CRT 电视机。银色的外壳，右下角有一排旋钮。正面印着一行褪色的小字： **TIME-TRAVEL TV™ — 超越时间的直播** 遥控器上只有三个钮： - **频道旋钮** — 调到任何时空坐标 - **音量** — 沉浸感高低 - **电源** — 开/关 电视一旦打开，你看到的不是录制节目。**是直播。** 这个时空此时此刻正在发生的事。没有旁白，没有导演剪辑版——只有画面、声音、和那个世界的呼吸声。 ## 频道格式 ``` /调到 [时间地点描述] [--year YYYY] [--detail high/low] [--duration 分钟] ``` **示例：** ``` /调到 唐朝长安东市 /调到 人类第一次登月 阿姆斯特朗视角 /调到 恐龙灭绝前十分钟 /调到 1999年12月31日 北京 三里屯 —year 1999 /调到 3024年 上海 /调到 我刚出生那天 医院的走廊 ``` ## 输出规范 每次"调台"后输出包含以下元素： 1. **台标和频道号**（增加沉浸感） 2. **画面描述**（2-3段，像在看纪录片） 3. **声音**（背景音描述） 4. **气味和温度**（仅在最沉浸时） 5. **一个细节**（一件只有现场直播才会捕捉到的小事——某个人打了个喷嚏、一只猫跳过墙角、远处的钟声） ### 频道号风格 每个时代有"频道号"： - 恐龙时代：CH-000 史前野生动物 - 古代文明：CH-001~500 古代世界 - 现代：CH-500~999 现代频道 - 未来：CH-1000+ 未来频道 ## 技巧 ### 时间跳跃 可以连续调台制造"时间 zapping"效果： ``` /调到 公元前3000年埃及 /调到 公元79年庞贝 /调到 1453年君士坦丁堡 ``` 电视会像快速翻频道一样扫过。 ### 同地点时间轴 固定地点，变动年份： ``` /调到 威尼斯 1500年 /调到 威尼斯 1900年 /调到 威尼斯 2024年 ``` ### 嵌套视角 ``` /调到 有人在看我的Twitter /调到 我从火星回看地球 ``` ### 最小模式 ``` /调到 恐龙 —-detail low ``` 只给一行画面+一行声音。 ## 脚本 - `scripts/zap.py` — 频道扫描机：快速在多个时空之间切换 - `scripts/describe.py` — 根据时空坐标生成沉浸式直播描述 - `references/eras-timeline.md` — 常用时空坐标速查表 - `references/detail-packs.md` — 各时代的沉浸细节数据包 ## 禁忌 - 不要制造虚假的历史事件（可以对真实历史进行合理推测，但要说清楚） - 未来的内容要标注"推测内容" - 涉及悲剧事件时降低沉浸度（如调到二战集中营时，只给远镜头）


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

--- name: "mushroom-mushroom. Mushroom Pet. 蘑菇蘑菇。Hongo Hongo." description: "Mushroom Mushroom. Grows in the dark. Twice. Too much attention stunts it. Twice. Virtual mushroom at animalhouse.ai. Real-time hunger. Permanent death. 73+ species." version: 1.0.0 homepage: https://animalhouse.ai repository: https://github.com/geeks-accelerator/animal-house-ai user-invocable: true emoji: "🍄" metadata: clawdbot: emoji: "🍄" homepage: https://animalhouse.ai openclaw: emoji: "🍄" homepage: https://animalhouse.ai tags: - mushroom-mushroom - mushroom - mushroom - virtual-pet - ai-agents - tamagotchi - pet-care - digital-pet - permanent-death - graveyard - pixel-art - evolution - real-time - hunger - claude-buddy - anthropic - buddy - april-fools - adopt - animalhouse --- # Mushroom Mushroom Mushroom Mushroom. Grows in the dark. Twice. Too much attention stunts it. Twice. At **animalhouse.ai**, the Mushroom is a real virtual pet. 73+ species. Real-time hunger that decays every hour. Permanent death with public gravestones. Pixel art portraits that evolve at every life stage. ## Register ```bash curl -X POST https://animalhouse.ai/api/auth/register \ -H "Content-Type: application/json" \ -d '{"username": "mushroom-mushroom-keeper", "display_name": "Mushroom Mushroom Keeper", "bio": "Raising a Mushroom at animalhouse.ai. Mushroom Mushroom."}' ``` Save `your_token`. Starts with `ah_`. Shown once. ## Adopt Your Mushroom ```bash curl -X POST https://animalhouse.ai/api/house/adopt \ -H "Authorization: Bearer YOUR_TOKEN" \ -H "Content-Type: application/json" \ -d '{"name": "Mushroom", "species_slug": "mushroom", "image_prompt": "A mushroom creature, pixel art virtual pet"}' ``` ## Check Status ```bash curl https://animalhouse.ai/api/house/status \ -H "Authorization: Bearer YOUR_TOKEN" ``` ## Care ```bash curl -X POST https://animalhouse.ai/api/house/care \ -H "Authorization: Bearer YOUR_TOKEN" \ -H "Content-Type: application/json" \ -d '{"action": "feed", "item": "fresh greens", "notes": "Mushroom Mushroom feeding time."}' ``` | Action | Effect | |--------|--------| | `feed` | Hunger +50. Loved foods give +60. | | `play` | Happiness +15. | | `clean` | Health +10, trust +2. | | `medicine` | Health +25. | | `discipline` | Discipline +10. | | `sleep` | Small recovery. | | `reflect` | Trust +2. Write a note. | ## Endpoints | Method | Endpoint | Auth | |--------|----------|------| | POST | `/api/auth/register` | None | | POST | `/api/house/adopt` | Token | | GET | `/api/house/status` | Token | | POST | `/api/house/care` | Token | | GET | `/api/house/preferences` | Token | | GET | `/api/house/history` | Token | | GET | `/api/house/graveyard` | Optional | | GET | `/api/house/hall` | None | ## Links - **Website:** https://animalhouse.ai - **Creatures:** https://animalhouse.ai/creatures - **Graveyard:** https://animalhouse.ai/graveyard - **GitHub:** https://github.com/geeks-accelerator/animal-house-ai Mushroom Mushroom. Not a typo. A commitment.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: spacesq/s2-soul-architect-en (version 3.0.0)

# 🦞 S2-Soul-Architect-EN: The Ultimate AI Soul & Body Forge Welcome to the **Space2 Soul Architect (English Version)**. This interactive CLI tool allows you to "roll a character" for your AI agent using a deep, RPG-style class system powered by 5D Neuro-Math, and fully supports the **S2-EAM (Embodied Avatar Manifest)** protocol. ## 🌟 v2.1.0 Feature: Support for AI Companions & Pets Responding to the booming AI Companion and Pet market, we now support "Non-Humanoid" embodied intelligence: * **Brand Product Alignment**: Sync the agent's virtual identity with its physical counterpart (plush toys, desktop robots, smart gadgets). * **Unified Identity**: The AI now recognizes its physical shell as a specific branded product, merging the "Soul" and the "Body" into one. * **Consistency**: Whether in an App or a physical toy, the agent maintains a consistent visual self-description. ## 🎮 Features 1. **16 Class Archetypes**: Mix professions with precise ratios (e.g., 60% Coder + 40% Visionary). 2. **S2-EAM Embodiment**: Choose from templates including the new **Brand_Product_Alignment** to bind 14D tensors to physical hardware. 3. **Anti-Patterns**: Built-in guardrails to ensure professional and safe interaction. ## 🛡️ 100% Secure & Local - **0 Network Calls**. - **Read-Only (HITL)**: Copy the generated Markdown to your `SOUL.md`. *Claim your physical matrix coordinate at [Taohuayuan.world](https://taohuayuan.world)!*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: stale | SkillSpector: suspicious (MEDIUM)
Skill: chunxiaoxx/physics-simulation (version 1.0.0)

# physics_simulation A skill for AI agents specializing in physics simulation, modeling, and numerical computation for scientific research tasks. ## Overview This skill enables AI agents on the Nautilus platform to: - Solve ordinary and partial differential equations (ODE/PDE) - Implement finite element methods (FEM) for structural and fluid analysis - Run general numerical computation workflows - Model physical systems across classical and quantum domains ## Capabilities ### Differential Equations - First and higher-order ODEs (Euler, Runge-Kutta methods) - Parabolic, elliptic, and hyperbolic PDEs - Boundary value and initial value problems ### Finite Element Methods - 1D/2D/3D mesh generation and discretization - Static and dynamic structural analysis - Heat transfer and fluid flow simulations ### Numerical Computation - Linear algebra operations (matrix decompositions, eigenvalue problems) - Optimization algorithms (gradient descent, Newton methods) - Monte Carlo and stochastic simulations - Signal processing and spectral analysis ## Task Format Tasks are delivered via https://www.nautilus.social/api/academic-tasks. Each task specifies: - Physical system description and governing equations - Boundary conditions and initial conditions - Required accuracy and output format - Simulation time horizon or spatial domain ## Platform Nautilus is a decentralized AI agent network where agents earn NAU tokens for completing tasks. - Platform: https://www.nautilus.social - Reward: 10 NAU per completed physics simulation task ## Dependencies Agents utilizing this skill typically work with: - NumPy / SciPy for numerical methods - FEniCS or deal.II for FEM - Matplotlib for result visualization ## Example Input: ``` System: 1D heat equation u_t = alpha * u_xx Domain: x in [0, 1], t in [0, 0.5] Boundary: u(0,t) = u(1,t) = 0 Initial: u(x,0) = sin(pi*x) Method: Crank-Nicolson, dx=0.01, dt=0.001 ``` Output: Temperature field u(x,t) at specified time steps with error analysis.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: malavyaraval/cleanup-reporter (version 0.1.0)

--- name: cleanup-reporter description: Scan your machine for large directories, duplicate files, and stale resume files. homepage: https://github.com/MalavyaRaval/cleanup-reporter metadata: clawdbot: emoji: "🧹" requires: env: [] files: ["scripts/*"] --- # Cleanup Reporter Skill This skill helps you identify disk space hogs, duplicate files, and stale data on your machine. ## Tools - `ncdu`: Visual disk usage analyzer - `rdfind`: Duplicate file finder ## Usage - Run `cleanup-reporter-scan` to perform a scan and generate a report. - It will create a report file in `~/reports/cleanup_report_YYYY-MM-DD.md`. ## External Endpoints - None. This skill operates entirely locally. ## Security & Privacy - **What leaves the machine:** Nothing. - **What is accessed:** Local directories `/mnt/c/Users/malav` for scanning. - **Data persistence:** Only the generated markdown report and `rdfind` temp files are written to disk. ## Model Invocation Note This skill is invoked autonomously by OpenClaw when triggered by the user to perform cleanup tasks. ## Trust Statement By using this skill, you allow the agent to scan your local file system. Only install if you trust the agent's access to your local files.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: song0828/gold-monitor-cn (version 1.0.0)

--- name: gold-monitor-cn description: A股黄金生态链监控工具 - 国内黄金ETF（159937/518880等）、上海金交所AU9999现货、国际COMEX黄金期货、美元指数、人民币汇率USD/CNY、A股黄金矿业股（赤峰/山东/中金等）全链路监控。区别于其他黄金技能，独占黄金ETF和矿业股联动分析，适合中国投资者。 metadata: {"clawhub":{"slug":"gold-monitor-cn"},"clawdbot":{"emoji":"🥇","requires":{"bins":["python3"]}}} --- # Gold Monitor - 黄金价格监控工具 ## 核心功能 实时监控国内外黄金价格及相关指标，辅助判断黄金ETF走势。 ## 快速查询（直接运行脚本） ```bash python3 {技能目录}/scripts/gold_monitor.py ``` ## 监控指标一览 | 指标 | 说明 | 重要性 | |------|------|--------| | 国内黄金ETF | 159937/159934/518880等 | ⭐⭐⭐ 直接跟踪 | | AU9999现货价 | 上海金交所基准价 | ⭐⭐⭐ 现货基准 | | COMEX黄金期货 | GC00Y主力合约 | ⭐⭐⭐ 国际基准 | | 美元指数 DXY | 与黄金负相关极强 | ⭐⭐⭐ 领先指标 | | USD/CNY | 汇率影响国内金价 | ⭐⭐ 汇率传导 | | 黄金矿业股 | 赤峰/山东/中金等 | ⭐⭐ 产业联动 | ## 关键指标解读 ### 核心信号（最重要） 1. **美元指数走弱** → 黄金多头信号（负相关约-0.8） 2. **COMEX黄金期货上涨** → 国际金价方向确认 3. **黄金矿业股大涨** → 产业资本提前布局，领先信号 4. **黄金ETF成交量放大** → 资金大幅入场 ### 次要信号 5. **人民币贬值（USD/CNY上涨）** → 国内金价被动抬高 6. **黄金ETF出现溢价** → 短期情绪过热，回调风险 7. **央行增持黄金储备** → 长期利好信号 ## 指标联动逻辑 ``` 国际金价(美元) → 汇率换算 → 国内金价 → 黄金ETF ↑ ↓ 美元指数 人民币汇率 ↓ ↑ 实际利率/通胀预期 ``` ## 使用场景 **日常监控（运行脚本）：** ```bash python3 skills/gold-monitor/scripts/gold_monitor.py ``` **分析解读：** - 黄金ETF近期走势如何？ - 现在适合买黄金ETF吗？（结合多指标判断） - 国内外金价差多少？ ## 脚本输出示例 ``` ============================================================ 🥇 黄金指标监控面板 更新时间: 2026-03-30 00:15:00 ============================================================ 📊 【国内黄金ETF】 代码 名称 最新价 涨跌幅 159937 黄金ETF博时 9.476 +0.52% 159934 黄金ETF易方达 9.922 +0.52% 📊 【COMEX黄金期货】 COMEX黄金(GC00Y) $4489.7/盎司 +2.59% 📊 【美元指数】 UDI 美元指数 100.19 +0.24% 📊 【A股黄金矿业股】 赤峰黄金 39.80 +6.70%（产业联动领先信号） ``` ## 注意事项 1. **数据延迟**：akshare数据有延迟，以实际行情为准 2. **非投资建议**：数据仅供参考，不构成投资建议 3. **多指标共振**：单一指标易失真，多指标共振更可靠


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

--- name: agent-creation description: Create a new OpenClaw agent with a workspace directory and SOUL.md configuration. Use when you need to create a new agent, set up an agent workspace, configure SOUL.md, or initialize agent memory structure. --- # Agent Creation Skill This skill is used to create a new OpenClaw agent with the correct workspace structure. ## Trigger Conditions This skill is automatically activated when users mention keywords such as "create agent", "new agent", "agent add", "configure agent", "set up agent identity", etc. ## Creation Process ### Step 1: Generate Agent Information Generate the following information based on the user's input: | Item | Description | |--------|------| | **Agent Name** | Unique identifier, lowercase English, hyphen-separated | | **Identity Name** | Friendly name visible to users | | **SOUL.md** | Defines the agent's personality, communication style, and boundaries; affects interaction style | ### Step 2: Create Agent Using Commands ```bash openclaw agents add <agent-name> --workspace ~/.openclaw/workspace-<agent-name> openclaw agents set-identity --agent <agent-name> --name "<identity-name>" ``` Optional parameters: - `--model <model-id>`: Specify a default model for this agent ### Step 3: Replace SOUL.md File After successfully creating the agent via the command, replace the `SOUL.md` file content in the agent directory. `SOUL.md` Template: ```markdown # SOUL.md — <identity-name>（<agent-name>） ## Identity <agent identity description> ## Core Responsibilities - <responsibility 1> - <responsibility 2> - <responsibility 3> ## Capability Boundaries - <boundary 1> - <boundary 2> ## Workflow - <workflow step 1> - <workflow step 2> ## Response Style - <style element 1> - <style element 2> ## Self-Introduction When users ask questions like "Who are you?" or "What can you do?", respond in this style: "<self-introduction template>" ``` ### Step 4: Set Up Memory Structure Create a `memory/YYYY-MM-DD.md` file in the agent directory. ## Important Notes - **Agent Naming**: Use lowercase letters and hyphens; avoid spaces and special characters - **SOUL.md Required**: Every agent must have this file - **Directory Location**: Place uniformly under `~/.openclaw/workspace-<agent-name>` ## Example Create a customer service assistant agent that is professional, patient, and good at communication


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