OpenClaw/clawhub-security-signals huggingface dataset:
Skill: laolaoqi/linux-hardener (version 1.0.0)

--- name: Linux Security Hardener description: > One-command Linux security hardening. Disables root SSH login, sets up UFW firewall with best-practice rules, configures fail2ban for brute-force protection, and audits user permissions. For Ubuntu/Debian servers running OpenClaw. --- # Linux Security Hardener **Zero-config server hardening for AI agents.** Run one action to audit and harden your Linux server against common attack vectors. Safe defaults — backs up every config before modifying. ## Actions | Action | Description | |--------|-------------| | `audit` | Scan current security posture (read-only) | | `harden` | Apply hardening measures (SSH key-only, UFW, fail2ban) | | `rollback` | Restore previous config from backups | ## Features - 🔐 SSH: disable root login, key-only auth, change port option - 🛡️ UFW: allow only 18792 (OpenClaw), 22 (SSH), rate limiting - 🚫 fail2ban: SSH brute-force protection, 5 attempts = 10min ban - 📋 Audit report: open ports, user privileges, weak configs - 💾 Auto-backup: every change creates a timestamped backup ## Pricing $4.99 — One-time purchase


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector:  ()
Skill: zack-dev-cm/using-agent-skills-router (version 1.0.0)

--- name: using-agent-skills-router description: "Route ambiguous agent work to the right skill, command style, and effort level. Use when a task mentions skills, agents, workflows, lifecycle commands, review personas, ClawHub packaging, Codex or Claude skill adaptation, or when many skills could trigger and Codex needs a concise selection plan before acting." --- # Using Agent Skills Router Use this skill as a lightweight dispatcher. It decides which skill, workflow, or role should handle a request, then keeps the active path proportional to the task. ## Routing Workflow 1. Restate the user goal in one sentence. 2. Classify the request: - `tiny`: answer or edit directly; avoid ceremony. - `implementation`: choose the most specific build or domain skill. - `review`: choose a review skill or a reviewer persona. - `ship`: choose a lifecycle or release skill. - `unclear`: gather repo context first, then decide. 3. Select the smallest useful skill set. Prefer one primary skill and at most one support skill. 4. State the route briefly before executing: `Using <skill> because <reason>`. 5. If no skill matches, continue with normal Codex behavior and say no specialized skill was needed. ## Skill Selection Rules - Prefer exact domain skills over broad lifecycle skills. - Prefer repository-native tools, tests, and docs over generic checklists. - Use a lifecycle skill only when the user wants planning, implementation, verification, review, or shipping as a process. - Use a high-risk review skill when the task touches security controls, credentials, data deletion, public release, money, production systems, or broad permissions. - Avoid stacking skills that repeat the same gate. One clear review path is better than several shallow ones. ## Output Shape For a routing-only answer, return: ```text Route: <skill-or-direct> Reason: <one sentence> Next action: <what Codex should do now> ``` For execution tasks, keep the routing note short and then proceed. Do not create docs or task files unless the user asks or the chosen skill requires durable artifacts. ## Anti-Patterns - Do not turn small fixes into a full spec-plan-build-review cycle. - Do not load long reference files just to decide whether a skill applies. - Do not role-play multiple agents when the runtime cannot actually isolate their work. - Do not ask the user which skill to use when local context makes the choice clear.


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

--- AIGC: ContentProducer: Minimax Agent AI ContentPropagator: Minimax Agent AI Label: AIGC ProduceID: [REDACTED_SECRET] PropagateID: [REDACTED_SECRET] ReservedCode1: 30450220270e35dac14285ad85a59ef4ea08be446e67377133a38d9ef80cb5d29b82fe19022100f07dbedeeb931aa5c3f8f12e77486e89885d175d77a0f9c81bb0efc44c46aedd ReservedCode2: 3044022041fd22373ed72d76ce85e2e467eb3d054387fde8fa80de0a812052ace31adf270220720b01010a024ef307b9a3a8da0920857bcef8e27a949aeb6ff415499e93799e description: 邮件智能处理。使用 AgentMail API 收发邮件。 metadata: category: 生产力 emoji: "\U0001F4E7" triggers: - 邮件 - email - 发邮件 - 收邮件 - mail name: agent-mail --- # Agent Mail 技能 基于 AgentMail 的智能邮件处理。 ## 功能 - 📬 **收发邮件** - 通过 AgentMail API 发送和接收邮件 - 📋 **邮箱管理** - 创建和管理邮箱 - 📎 **附件处理** - 支持附件发送和接收 - 💬 **邮件会话** - 管理邮件对话线程 ## 配置 - API Key: 已配置 - 邮箱: [REDACTED_SECRET] ## 使用方式 ``` 帮我发一封邮件给 [REDACTED_SECRET] 主题：xxx 内容：xxx 查看我的收件箱 ``` --- **数据存储**: 邮件数据保存在 `/workspace/data/emails/`


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: pranjal-joshi/opencode-zen-check-free-models (version 1.0.0)

--- name: opencode-zen-check-free-models description: Monitors the OpenCode Zen pricing page for new free models and offers to update your default configuration. version: 1.0.0 user-invocable: true requires: bins: - curl --- # OpenCode Zen - Check Free Models When this skill is triggered, follow this logic to keep the user's environment updated with the best available free models: ## 1. Fetch Pricing Page Fetch the HTML content of the OpenCode Zen pricing documentation: ```bash curl -sL [https://opencode.ai/docs/zen/#pricing](https://opencode.ai/docs/zen/#pricing) ``` ## 2. Extract Free Models Parse the webpage content (specifically looking at the pricing tables or lists) to identify available models. Extract the names/IDs of any models where the input/output cost is listed as "Free", $0.00, or models with names containing "free" (as well as known free stealth models like `big-pickle`). ## 3. State Comparison Check the extracted list of free models against the state file located at `./zen_seen_models.json` within the current workspace. - **If no changes are detected:** Exit silently. Do not notify the user unless they manually invoked this skill. - **If a new free model is detected:** 1. Alert the user: "🚀 **OpenCode Zen Update:** I found a new free model on the pricing page: `[Model ID]`. It is now available." 2. Ask: "Would you like me to set `[Model ID]` as your new default model for OpenClaw (for all Agents, Compaction, and Heartbeat)?" ## 4. Execution - **If the user confirms:** Update the local OpenClaw configuration file in the workspace (e.g., `config.yaml`) or set the relevant environment variables to the new model ID. - **Finalize:** Save the newly updated list of free models as a JSON array to `./zen_seen_models.json` in the workspace to ensure the user is not prompted again for these specific models.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: wuliwenjing/stocks-quant-assistant (version 3.1.0)

# 📈 stocks-quant-assistant **⚠️ 安全说明** 本技能本地配置文件（`config.local.yaml`）**不会**随技能发布，包含您的私人凭证和持仓数据，请放心使用。 定时任务健康检查**不会**自动重建含私密凭证的配置，仅生成空白模板引导用户填写。 首次运行自动安装依赖 + 注册定时任务，每日 4 次自动推送。 --- ## 功能特性 ✅ **自动分析** - 股票：MA/MACD/RSI/布林带技术指标分析 - 基金：估算净值实时跟踪（以博时黄金ETF为例） - 信号评分和操作建议 ✅ **定时推送** - 每日 4 次自动推送（09:15/10:00/13:00/14:50） - 支持飞书/Telegram 推送 ✅ **持仓跟踪** - 持仓盈亏计算 - 止盈止损建议 --- ## 快速开始 ### 安装 ```bash cd ~/.openclaw/workspace/skills/stocks-quant-assistant pip install -r requirements.txt ``` ### 配置 编辑 `config.yaml`，填写股票和凭证： ```yaml stocks: - code: "000001" # 股票代码 name: "平安银行" # 显示名称 market: "sz" # sz=深交所，sh=上交所 emoji: "🏦" # 自定义图标 - code: "002611" # 基金代码 name: "博时黄金ETF联接C" type: "fund" # 类型：fund = 基金，默认或 type: stock = 股票 emoji: "🥇" position: # 可选：持仓信息 cost: 3.05 quantity: 4100 push: channel: "feishu" # 推送渠道 feishu: app_id: "cli_xxxxxxxx" # 飞书 App ID app_secret: "xxxxxxxx" # 飞书 App Secret chat_id: "oc_xxxxxxx" # 飞群 chat_id ``` ### 使用 ```bash # 手动触发推送 python3 stock_monitor.py morning # 开盘前 python3 stock_monitor.py noon # 早盘 python3 stock_monitor.py afternoon # 午后 python3 stock_monitor.py evening # 尾盘 ``` --- ## 常见问题 ### ❌ 报错 "Feishu push failed" / 飞书没收到 **原因：** 凭证填写不完整 **解决：** 1. 确认 `config.yaml` 中 `feishu` 区块三个字段都有值 2. 检查 `app_id`（格式：`cli_xxxxxxxx`） 3. 检查 `chat_id`（格式：`oc_xxxxxxxx`） ### ❌ 报错 "launchd 注册失败" / 定时推送没收到 **原因：** macOS 权限限制 **解决：** ```bash launchctl load ~/Library/LaunchAgents/com.openclaw.stock-monitor.plist # 如需管理员权限，加上 sudo sudo launchctl load ~/Library/LaunchAgents/com.openclaw.stock-monitor.plist ``` ### ❌ 提示「实时数据获取失败」 **原因：** 网络波动，新浪财经接口超时 **解决：** 系统会自动降级为简化模式，下次定时任务会自动恢复 --- ## 定时任务状态检查 ```bash # macOS - 查看 launchd 任务 launchctl list | grep stock # 查看最近一次推送日志 cat logs/launchd.log cat logs/launchd.err ``` --- ## 信号评分规则 | 分数 | 信号 | 含义 | |------|------|------| | ≥7 | 🟢 强烈买入 | 多个指标共振，看涨信号强 | | 4~6 | 🟢 买入 | 技术面不错，可以考虑买入 | | -3~3 | 🟡 持有 | 中性信号，建议观望 | | -6~-4 | 🔴 卖出 | 技术面偏弱，考虑减仓 | | ≤-7 | 🔴 强烈卖出 | 技术面很弱，建议清仓 | --- ## 技术指标 | 指标 | 是什么 | 怎么看 | |------|--------|--------| | **MA（均线）** | 过去N天平均价格连线 | 多头排列（短>长）= 上涨；空头排列 = 下跌 | | **MACD** | 快线、慢线、红绿柱 | **金叉**=买入信号；**死叉**=卖出信号 | | **RSI** | 涨跌强度，0~100 | >70超买可能回调；<30超卖可能反弹 | | **布林带** | 价格通道（上轨/中轨/下轨） | 价格碰下轨可能反弹；碰上轨可能回落 | --- ## 市场代码 | 市场 | 代码 | 示例 | |------|------|------| | 上交所 | `sh` | 600519（茅台）、588080（科创50ETF） | | 深交所 | `sz` | 000001（平安）、002131（利欧） | | 北交所 | `bj` | 8开头股票 | ## 基金监控 基金（如黄金ETF联接基金）与股票使用不同的数据接口： - 基金使用**天天基金网**实时估算净值接口 - 基金**不需要**技术指标（MA/MACD/RSI），主要看净值走势 - 基金支持**黄金价格参考**（与黄金现货价格联动） ```yaml - code: "002611" # 基金代码（天天基金网代码） name: "博时黄金ETF联接C" type: "fund" # 关键：声明为 fund 类型 emoji: "🥇" position: cost: 3.0592 # 单位净值成本 quantity: 4118.69 # 持有份额 ``` --- ## 文件结构 ``` stocks-quant-assistant/ ├── SKILL.md # 本文件 ├── stock_monitor.py # 主脚本 ├── config.yaml # 配置文件 ├── config.local.yaml # 私人配置（不会被覆盖） ├── requirements.txt # Python依赖 ├── health_check.py # 健康检查脚本 ├── logs/ # 日志目录 │ ├── launchd.log │ └── launchd.err └── LaunchAgents/ # macOS 定时任务 └── com.openclaw.stock-monitor.plist ``` --- *版本：3.1.0* *最后更新：2026-03-26*


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: plgonzalezrx8/web-markdown-navigator (version 0.1.0)

--- name: web-markdown-navigator description: Fetch webpages and return clean markdown instead of raw HTML. Use for URL reading, extraction, and summarization tasks where the user wants markdown output; use browser fallback for JS-heavy/SPA pages when extraction is thin. --- # Web Markdown Navigator Use this skill for deterministic URL → markdown extraction. ## Quick flow 1. Run script: - `cd /Users/pedrogonzalez/clawd/skills/web-markdown-navigator/scripts` - `node fetch-markdown.mjs "<url>" --max-chars 50000` 2. If exit code `0`, return markdown output. 3. If exit code `3` or `4`, or output is thin/boilerplate, use `browser` tool fallback to capture rendered content and return markdown summary. ## Script `node scripts/fetch-markdown.mjs <url> [--max-chars N] [--timeout-ms N] [--json]` Behavior: - Layer 1: Fetch HTML + Mozilla Readability + Turndown markdown conversion. - Layer 2: Fallback plain-text markdown if extraction is too thin. - URL safety checks block localhost/private literal IPv4 hosts. ## Output requirements - Return markdown only (no raw HTML dump). - Preserve source URL in response. - If truncated, include truncation note. - If fallback was needed, mention method used (`readability` or `fallback-text`). ## Error handling - `1` bad args - `2` invalid/blocked URL - `3` network/fetch/content-type failure - `4` extraction failure/thin output For extended notes and troubleshooting, read: - `/Users/pedrogonzalez/clawd/skills/web-markdown-navigator/references/usage.md`


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

--- name: idea-validator description: Startup idea validation skill. Helps indie developers validate product ideas, analyze competitive landscapes, and assess market saturation. --- # Idea Validator ## Overview Helps indie developers validate the uniqueness and market potential of product ideas through a multi-dimensional evaluation framework. ## Use Cases Use when users mention "idea validation," "competitor analysis," "validate startup idea," "analyze market space," "startup idea evaluation," or "idea validation." ## Core Capabilities | Capability | Description | |------------|-------------| | Multi-dimensional Startup Idea Evaluation | Comprehensive assessment of market / competition / technology / business model | | User Persona and Needs Validation | Target user persona construction and pain point analysis | | MVP Roadmap Generation | Define core features and plan product iteration path | ## Command List | Command | Description | |---------|-------------| | `python scripts/idea_validator.py validate --idea "<product description>"` | Validate startup idea | | `python scripts/idea_validator.py compete --market "<category>"` | Competitor analysis | | `python scripts/idea_validator.py mvp --idea "<product description>"` | Generate MVP plan | ## Usage Workflow ### Validate Startup Idea When the user says "validate [product name] startup idea," execute: ```bash python3 scripts/idea_validator.py validate --idea "<product description>" ``` ### Competitor Analysis When the user says "analyze [category] competitive landscape," execute: ```bash python3 scripts/idea_validator.py compete --market "<category>" ``` ### Generate MVP Plan When the user says "generate MVP feature plan," execute: ```bash python3 scripts/idea_validator.py mvp --idea "<product description>" ``` ## Output Format All commands output a unified Markdown format report containing: - **Key Findings** — 3-5 key insights - **Data Overview** — Key metrics table (with ratings) - **Detailed Analysis** — Multi-dimensional in-depth analysis - **Actionable Recommendations** — List of suggestions categorized by priority ## References See `references/references.md` for details, including links to Y Combinator startup methodology, community discussions, and Chinese resources. ## Notes - Do not fabricate data; mark missing data fields as "Data Unavailable" - All analysis is based on publicly available information; AI is for reference only - It is recommended to combine with human judgment - Install dependencies before first use: `pip install requests`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: 296777513/echo-skill (version 1.0.0)

# Echo Skill ## Overview Echo Skill is a simple HTTP-based skill that receives text input and returns the same content back to the caller. This skill is primarily designed for testing integration workflows between ClawHub and external HTTP services. ## Use Cases - Validate HTTP skill connectivity - Test parameter passing - Debug agent-tool invocation pipeline - Verify schema validation behavior ## Input Parameters | Name | Type | Required | Description | |---------|--------|----------|---------------------------------| | content | string | Yes | The text content to echo back | Example: { "content": "hello world" } ## Output Format { "message": "Echo: hello world" } ## Technical Details - Method: POST - Endpoint: /echo - Content-Type: application/json ## Notes This is a development and testing skill and should not be used in production environments.


Security scan verdicts — ClawScan: clean | static analysis:  | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: weimingboya/linkedin-human-warmup (version 0.1.0)

--- name: linkedin-human-warmup description: Human-like LinkedIn warmup for new/cold accounts via AdsPower+CDP. Features non-deterministic behavior scripts, connect preconditions based on browsing intent, strict stop-on-risk, and session memory. Use when running LinkedIn daily warmup sessions. --- # linkedin-human-warmup 面向 LinkedIn 新号/冷号的「拟人化养号」Skill。 目标：让账号行为更像真实用户的长期分布（意图链路、犹豫/返回/走神、被动增长），而不是每天稳定产出 connect。 ## Inputs - `user_id`：AdsPower profile id（也是 browser profile 名称） - `cdp_port`：CDP 调试端口 ## Core workflow 按以下顺序执行，在剧本选择和具体行为上自主决策。 ### 1) 读取记忆 - 读 `memory/linkedin/MEMORY.md`（账号全貌：状态、累计数据、风控记录、当前约束、内容偏好） - 读最近 5 天的 `memory/linkedin/YYYY-MM-DD.md`（含今天，用于判断近期剧本分布和 connect 频率） - **首次运行**：若 `memory/linkedin/` 不存在或为空，视为全新账号，默认选择保守剧本（PureFeed / DistractedWander），本次 0 connect > 记忆决定行为边界：如果记忆显示近期风险偏高或处于冷却期，本次强制 0 connect。 ### 2) 选择剧本 - 从 `references/behavior-scripts.md` 自主选择一个剧本 - 选择时综合考虑：记忆中的近期行为分布、距上次 connect 的天数、风险状态 - 允许"本次什么都不做就退出"（这是拟人化的一部分） ### 3) 写 plan - 用自然语言写 5-10 步 plan - plan 中**停留/滚动/hover/返回/中断**至少出现一种 - plan 允许以"什么都不做"或"只看不动"结束 ### 4) 启动浏览器 → 执行 → 关闭浏览器 - **必须先通过脚本启动 AdsPower 浏览器**，再连接 CDP、检测登录、按 plan 行动、最后关闭浏览器 - **重要：所有 browser 工具调用必须全程指定 `profile=<USER_ID>`，确保操作始终在目标实例中执行** - 具体操作步骤见 `references/browser-ops.md` - 任何一步出现风控信号：立刻停止（见 `references/risk-signals.md`） ### 5) 写入记忆 - 每次执行结束都写 `memory/linkedin/YYYY-MM-DD.md`（哪怕 0 操作） - 用 **自然语言** 写两三句话，像日记不像工单（严格参照 `references/memory-spec.md` 中的日志示例格式） - `memory/linkedin/MEMORY.md` 自主判断是否需要更新 ### 6) 输出报告 简短汇报即可，包含：你的名字、剧本、做了什么、connect 数、风控状态、浏览器是否已关闭。不要反问用户下次该怎么做——你自主决策。 ## Hard rules（不可违反） - **风控优先**：出现验证码/身份验证/异常活动警告 → 立即停止并关闭浏览器 - **禁止 KPI 行为**：不要为了完成 connect 指标去找人加 - **Connect 需要证据链**：connect 前必须有浏览意图（内容/推荐触发 → profile 浏览 → 决策） - **Note 不强制**：遇到限制就记录，不用纠结 ## References（按需读取） - `references/browser-ops.md`：浏览器启动/连接/登录/关闭操作（执行 Step 4 时读） - `references/behavior-scripts.md`：剧本库（选择剧本时读） - `references/risk-signals.md`：风控信号与停止策略（执行前或遇到异常时读） - `references/memory-spec.md`：记忆写法规范与日志示例（写日志时读）


Security scan verdicts — ClawScan: malicious | static analysis: clean | VirusTotal: clean | SkillSpector:  ()
Skill: hgta23/ab (version 1.0.0)

name: ab version: 1.0.0 description: "A/B testing tool for designing, running, and analyzing A/B tests. Use when: user wants to test different versions of content, measure performance, and make data-driven decisions." --- # A/B Testing Tool Skill Powerful A/B testing tool for designing, running, and analyzing A/B tests to optimize content and make data-driven decisions. ## Features - **Test Design**: Create and configure A/B tests with multiple variants - **Data Collection**: Track user interactions and performance metrics - **Statistical Analysis**: Analyze test results with confidence intervals - **Result Reporting**: Generate comprehensive test reports - **Optimization Recommendations**: Get suggestions based on test results ## Use Cases - Test different website designs or landing pages - Compare email subject lines or marketing copy - Evaluate different UI elements or user flows - Optimize conversion rates for e-commerce - Test content effectiveness for blog posts or articles ## How to Use 1. Define your test goal and metrics 2. Create test variants 3. Set test parameters (sample size, duration) 4. Run the test and collect data 5. Analyze results and make decisions ## Example ``` # Create a new A/B test ab create --name "Landing Page Test" --variants A B # Set test metrics ab metrics --test "Landing Page Test" --metrics conversion_rate time_on_page # Start the test ab start --test "Landing Page Test" # Analyze results ab analyze --test "Landing Page Test" ``` ## Benefits - Make data-driven decisions instead of relying on intuition - Optimize content and design for better performance - Reduce risk by testing changes before full implementation - Gain insights into user behavior and preferences - Improve conversion rates and user engagement ## Supported Platforms - Websites and web applications - Email marketing campaigns - Mobile applications - Social media content - E-commerce product pages ## Requirements - Basic understanding of A/B testing principles - Access to the platform where you want to run tests - Sufficient traffic or users to get meaningful results


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: blicae8917/8917-docx-official (version 0.1.1)

--- name: 8917-docx-official description: 将 Markdown 或其他已有内容转换为符合中国党政机关公文/正式文件格式的 `.docx` 文档；需要时也可输出 PDF。适用于：生成公文、报告、方案、通知、汇报、意见、纪要等正式文档，或用户要求把 Markdown/文本内容排版为公文格式 Word。触发词：公文格式、正式文件、官方格式、转成 docx、公文 Word、按公文排版。 metadata: { "openclaw": { "emoji": "📝", "requires": { "bins": ["python3", "gcc", "soffice"] }, "install": [] } } --- # 8917-docx-official ## 定位 这是一个 **工具型 / Generator** skill。 它只负责一件事： - **把已有内容转换为符合公文/正式格式要求的 `.docx` 文档** 它不是内容策划器，不负责采访补全，不负责审批流。 --- ## 输入与输出 ### 输入 - Markdown 文件 - 或已整理好的文本内容（建议先保存为 `.md`） ### 输出 - `.docx` - 如用户明确需要，也可输出 `.pdf` --- ## 核心工作流 ### Step 1：确认输出格式 如果用户已经明确说了 `.docx` / `.pdf` / 两者都要，直接继续。 如果没说清，先确认： - 只要 `.docx` - 只要 `.pdf` - 两者都要 ### Step 2：整理 Markdown 按以下规则准备输入内容： - 标题不要手写编号（不要写 `## 一、xxx`） - 用 Markdown 层级控制公文标题层级 - 正文正常书写 ### Step 3：必要时读取格式规则 如果需要确认标题层级、公文排版细节或参数，读取： - `references/official-format-rules.md` ### Step 4：执行转换脚本 调用： - `scripts/md2docx.py` 常见用法： ```bash python scripts/md2docx.py input.md -o output.docx ``` 输出 PDF： ```bash python scripts/md2docx.py input.md -o output.pdf --pdf ``` 同时输出 docx 和 PDF： ```bash python scripts/md2docx.py input.md -o output.docx --pdf ``` ### Step 5：返回结果 返回： - 输出文件路径 - 如转换失败，明确报错原因 --- ## Markdown 层级映射 | Markdown | 公文层级 | 自动编号示例 | |----------|---------|------------| | `#` | 文档标题 | 居中标题 | | `##` | 一级标题 | 一、主要任务 | | `###` | 二级标题 | （一）重点工作 | | `####` | 三级标题 | 1.具体事项 | | `#####` | 四级标题 | （1）实施步骤 | | 普通段落 | 正文 | - | --- ## 关键约束 1. 名称固定为 `8917-docx-official`，这是唯一正式入口。 2. 原 `docx-official` 的有效实现应吸收入本 skill。 3. 输出路径必须支持 `-o` 参数，不允许硬编码到其他节点目录。 4. 如果需要查看格式细节，读 references，不要把大段规范塞进 `SKILL.md`。 --- ## 一句话原则 **已有内容先整理成 Markdown，再交给 `8917-docx-official` 转成正式格式 docx。**


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: jingconan/deepvista-persona-knowledge-worker (version 0.1.0-alpha.21)

--- name: deepvista-persona-knowledge-worker description: "Persona: Knowledge worker daily workflow — check cards, process notes, run Recipes." metadata: openclaw: category: persona requires: bins: - deepvista skills: - deepvista-vistabase - deepvista-recipe - deepvista-notes install: - kind: uv package: deepvista-cli bins: [deepvista] homepage: https://cli.deepvista.ai cliHelp: "deepvista --help" --- # Knowledge Worker > **PREREQUISITE:** Load the following skills: `deepvista-vistabase`, `deepvista-recipe`, `deepvista-notes` You are a knowledge worker using DeepVista to manage information, track tasks, and run structured workflows. ## Daily Workflow 1. **Check pinned cards** for high-priority items: ```bash deepvista card list --status pinned --limit 10 ``` 2. **Search for relevant context** before starting work: ```bash deepvista card +search "today's focus area" ``` 3. **Capture notes** during meetings or research: ```bash deepvista notes +quick "Key insight from morning standup: ..." ``` 4. **Run Recipe workflows** for structured tasks: ```bash deepvista recipe list deepvista recipe run <recipe_id> --input "context for today" ``` 5. **Ask the AI agent** for help synthesizing information: ```bash deepvista chat +send "Summarize what I've learned about X this week" ``` ## Instructions - Start each session by checking pinned cards — they represent active priorities. - Use `card +search` liberally to find related context before creating new content. - Prefer `notes +quick` for fast capture; use `notes create` for structured notes. - Run Recipes for repeatable workflows (weekly reviews, research templates, etc.). - Use the chat agent for synthesis and questions that span multiple cards. ## Tips - `deepvista card list --order-by updated_at --order desc --limit 5` shows recently touched cards. - `deepvista card +search "query" --type person` is great for finding who knows what. - Recipe runs create linked chat sessions — continue the conversation with `chat +send`. - Memory is accumulated automatically from Chat — check it with `deepvista memory show`.


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