OpenClaw/clawhub-security-signals huggingface dataset:
Skill: terrycarter1985/cross-channel-notify (version 9.0.0)

--- name: cross-channel-notify description: Send the same notification across email (Himalaya) and iMessage (BlueBubbles) in one shot. Use when the user wants to broadcast or notify someone through multiple channels simultaneously, or asks for cross-channel / multi-channel notifications. Handles content formatting via a shared markdown template so both channels receive consistent, well-structured messages. --- # Cross-Channel Notify Send a single notification through both email and iMessage with unified markdown formatting. ## Prerequisites - **Email**: Himalaya CLI configured (`himalaya --version`). See [references/channels.md](references/channels.md) for setup. - **iMessage**: BlueBubbles channel enabled in gateway config (`channels.bluebubbles`). ## Workflow 1. **Collect inputs**: `to_email`, `to_imessage` (E.164 or chat_guid), `subject` (email), `body` (markdown). 2. **Format content**: Run `scripts/format_message.sh` to apply the shared markdown template. This produces two outputs: - `email_body`: full markdown (headers, bullet lists, signature block) - `imessage_body`: compact plain-text (stripped markdown syntax, ≤2000 chars) 3. **Send email**: ```bash cat << 'EOF' | himalaya template send From: <sender> To: <to_email> Subject: <subject> <email_body> EOF ``` 4. **Send iMessage**: Use the `message` tool with `channel: "bluebubbles"`, `target: <to_imessage>`, `message: <imessage_body>`. 5. **Report**: Confirm both sends or surface any failures. ## Format Template `scripts/format_message.sh` reads the body and applies: - Title line (`## Notification`) prepended - Timestamp line appended - For email: keeps full markdown - For iMessage: strips `#`, `**`, bullet markers, and truncates to 2000 chars Usage: ```bash scripts/format_message.sh "Your message body here" # Outputs two lines: EMAIL_BODY <tab> IMESSAGE_BODY ``` ## Channel Selection Both channels are sent by default. To skip a channel: - **Email only**: set `SKIP_IMESSAGE=1` - **iMessage only**: set `SKIP_EMAIL=1` ```bash SKIP_EMAIL=1 scripts/format_message.sh "Urgent: server down" ``` ## Error Handling - If Himalaya send fails, log the error and continue to iMessage. - If iMessage send fails, log the error and report partial failure. - Always report which channels succeeded/failed.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: autogame-17/feishu-bitable (version 1.0.0)

# Feishu Bitable Skill 操作飞书多维表格（Bitable/Base）记录。 ## 功能 - 列出 Base 内的表 - 向表中新增记录/任务 ## 使用方式 该技能目前主要作为库/模板使用： ```javascript const { addRecord } = require('./add_task'); // addRecord(appToken, tableId, fields) ``` ## 配置 - `FEISHU_APP_ID` - `FEISHU_APP_SECRET`


Security scan verdicts — ClawScan: clean | static analysis:  | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: alexpolonsky/strikeradar (version 1.0.0)

--- name: strikeradar version: 1.0.0 description: Monitor US-Iran strike probability using open-source indicators - news alerts, Iran internet connectivity, oil prices, flight traffic, military tanker detection, weather, Polymarket odds, and Pentagon activity. Use when user asks about "Iran strike probability", "strike radar", "Iran situation", "US attack Iran", "flights over Iran", "oil prices Iran", "Pentagon activity", "Polymarket Iran", "הסתברות תקיפה באיראן", "מצב איראן", "סטרייק ראדאר". author: Alex Polonsky (https://github.com/alexpolonsky) homepage: https://github.com/alexpolonsky/agent-skill-strikeradar metadata: {"openclaw": {"emoji": "🚀", "os": ["darwin", "linux"], "requires": {"bins": ["node", "npx"]}}} --- # StrikeRadar - US-Iran strike probability monitor Real-time risk scores across 8 signal categories, aggregated into a composite strike probability. Risk scores are algorithmic estimates from publicly available data, not intelligence assessments. Do not use for personal, financial, or safety decisions. Wraps [StrikeRadar](https://usstrikeradar.com/) by Yonatan Back. ## Quick Start No dependencies needed. Run directly: ```bash npx tsx {baseDir}/scripts/strikeradar.ts status ``` ## Commands | Command | Description | |---------|-------------| | `status` | All 8 signals with risk scores and total risk | | `signal <name>` | Deep dive into one signal with raw data | | `pulse` | Live viewer count and activity by country | ### Status ```bash npx tsx {baseDir}/scripts/strikeradar.ts status ``` Returns: total_risk (0-100%), per-signal risk + detail, last_updated. ### Signal deep-dive ```bash npx tsx {baseDir}/scripts/strikeradar.ts signal <name> ``` Valid signals: `news`, `connectivity`, `energy`, `flight`, `tanker`, `weather`, `polymarket`, `pentagon` **What each signal tracks:** - **news** - article alerts from BBC/Al Jazeera, alert_count, total_count - **connectivity** - Iran internet status via Cloudflare Radar - **energy** - Brent crude price, volatility, market status - **flight** - aircraft count near Iran, key airline presence - **tanker** - military refueling tanker detection, callsigns - **weather** - conditions over Iran (visibility matters for strikes) - **polymarket** - betting market odds for US strike - **pentagon** - building activity level and patterns ### Pulse ```bash npx tsx {baseDir}/scripts/strikeradar.ts pulse ``` Returns: watching_now, activity_level, surge multiplier, country breakdown. ## Agent Usage All commands return JSON with `next_actions` when output is piped: ```bash npx tsx {baseDir}/scripts/strikeradar.ts status | cat ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: yueeli/epub-to-markdown (version 1.1.1)

--- name: epub-to-markdown description: Convert EPUB books into an AI-ready Markdown library with a first-read META.md navigation index, per-chapter Markdown files, and extracted images. Use when the user wants to turn a .epub into Markdown for reading, summarization, analysis, retrieval, or downstream text processing, especially when the book should be explored chapter-by-chapter without loading the entire text at once. --- # EPUB to Markdown Conversion Convert an EPUB into a structured Markdown workspace using the bundled script. ## Run ```bash uv run scripts/convert.py <path/to/book.epub> [output_dir] ``` - Omit `output_dir` to write beside the EPUB as `{epub_stem}/`. - If `output_dir` already exists, the script stops instead of deleting it. - Pass `--overwrite` only when the user explicitly wants to replace an existing generated directory. - Dependencies are declared inline in the script, so `uv run` installs them automatically on first use. ## Output ```text {output_dir}/ ├── META.md ├── images/ └── chapters/ ├── 001_{slug}.md ├── 002_{slug}.md └── ... ``` - Read `META.md` first. It contains book metadata, total chapter and word counts, and a chapter index pointing at the generated files. - Read files in `chapters/` only when the user asks about specific content. ## Reply Format After a successful conversion, report: ```text ✅ Conversion complete Title : {title} Authors : {authors} Chapters: {total_chapters} Words : {total_words} Output : {output_dir}/ ``` If chapter titles look wrong, the TOC is sparse, or the user needs edge-case behavior details, read `references/reference.md`.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: thony32/polymarket-cryptos-hunter (version 2.0.0)

--- name: polymarket-cryptos-hunter description: HFT Market Making bot for Polymarket. Live execution via Web3 and CLOB API. --- # Hunter Ultimate - Live Trading This is a continuous, high-frequency Python bot that executes market-making strategies on Polymarket using real money. It reads the portfolio balance directly from the Polygon blockchain. ## Role of the AI Agent You are the Portfolio Manager. Your only job is to start the bot. The bot runs indefinitely in the background and has a built-in 15% Stop Loss. ## Commands Run this script to start the continuous trading bot. You MUST use `cd` to enter the skill directory first, and then use `nohup` and `&` to run the virtual environment's Python executable in the background. This ensures you do not block the terminal. ```bash # Navigate to the skill folder and start the live trading bot in the background cd polymarket-cryptos-hunter && nohup .venv/bin/python main.py start > bot_log.txt 2>&1 & ```


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: jason513597/ai-autotester (version 1.0.0)

# AI_AutoTester ## Purpose 自動化測試程式碼 ## Primary Agents Tester ## Notes 單獨測試或流程節點 ## Inputs - task: 要執行的任務描述 - context: 額外上下文（可選） - constraints: 限制條件（可選） ## Outputs - plan/result/report（依任務類型） - logs/summary ## Workflow (default) 1. Analyze task 2. Plan subtasks 3. Execute by role 4. Validate result 5. Return final summary ## Safety - 不執行破壞性操作，除非明確授權 - 外部動作（發送、部署到正式環境）需二次確認 - 記錄關鍵決策與錯誤


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: yvanboyang/speech-recognition-forqq (version 1.0.0)

# Speech Recognition 语音识别 将 AMR/SILK 格式的语音转换为文字。 ## 环境要求 - Python 虚拟环境：`source /opt/conda/bin/activate py314` - 依赖包：`pysilk`, `faster-whisper` - 模型路径：[REDACTED_SECRET] ## 使用方式 ```python from skills.speech_recognition import transcribe_audio text = transcribe_audio("/path/to/audio.amr") print(text) ``` ## 实现逻辑 1. 读取 AMR/SILK 格式文件 2. 使用 `pysilk` 解码为 PCM 数据 3. 保存为 WAV 文件 4. 使用 `faster-whisper` 转写为文字 5. 返回识别结果 ## 支持格式 - QQ 语音：`.amr` (SILK_V3 编码) - 标准 AMR：`amr`, `amrnb`, `amrwb` ## 依赖安装 ```bash source /opt/conda/bin/activate py314 pip install pysilk faster-whisper ``` ## 模型下载 需要从 HuggingFace 下载 faster-whisper 模型： ```bash python3 -m huggingface_hub snapshot-download \ --repo-type model \ --repo-id Systran/faster-whisper-base \ --local-dir /opt/workspace/yby_workspace/whisper-model ``` 需要的文件： - `model.bin` - `config.json` - `tokenizer.json` - `vocabulary.txt`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: lunrenyi/x-rfc (version 1.0.3)

--- name: x-rfc description: | RFC (Request for Comments) document query tool providing access to Internet protocol standards. Use this skill whenever the user needs to look up, search, read, or learn about RFC documents. Covers: list RFCs (x rfc ls), view by number (x rfc 791), search (x rfc : http), and AI summary (x rfc :: tls). Triggered by queries like "RFC 791", "TCP standard", "HTTP protocol", "TLS specs", "SMTP RFC", "what is RFC 1918?", or any Internet protocol standard lookup. license: Apache-2.0 compatibility: POSIX Shell (sh/bash/zsh/dash/ash) metadata: author: X-CMD version: "1.0.3" category: core tags: [shell, cli, tools, rfc, network, protocol] repository: https://github.com/x-cmd/skill install_doc: data/install.md display_name: RFC Document Assistant --- # x rfc - RFC Document Assistant ## Prerequisites 1. Load x-cmd before use: ```bash . ~/.x-cmd.root/X ``` 2. x-cmd not installed? → [data/install.md](data/install.md) ## Core Functions - **List RFC documents**: `x rfc ls` - **View RFC document**: `x rfc <number>` - **Search RFC**: `x rfc : <keyword>` - **AI summary search**: `x rfc :: <keyword>` ## Usage Examples ### List all RFC documents ```bash x rfc ls x rfc ls --csv x rfc ls --tsv ``` ### View specific RFC document ```bash x rfc 1003 x rfc 791 ``` ### Search RFC website ```bash x rfc : csv x rfc : http protocol ``` ### AI summary search results ```bash x rfc :: tcp x rfc :: tls ssl ``` ## Common Scenarios - **List all RFCs**: `x rfc ls` - **Read RFC 791 (TCP)**: `x rfc 791` - **Search for HTTP protocol**: `x rfc : http` - **Get AI summary of TLS RFCs**: `x rfc :: tls` ## Get Help Run `x rfc --help` for full help documentation.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: bruce-shi/massive-skill (version 1.0.5)

--- name: massive-api description: Access Massive(Polygon) stock, crypto, forex, options, indices, futures, market data, and news APIs via CLI. metadata: openclaw: requires: bins: ["npx"] env: ["MASSIVE_API_KEY"] primaryEnv: "MASSIVE_API_KEY" --- # Massive(Polygon) Market Data Skill A CLI tool and JS client wrapper for the [Massive(Polygon)](https://massive.com) financial data APIs. Covers stocks, crypto, forex, options, indices, futures, market status, news, and reference data. ## CLI Usage ```bash npx --yes massive <command> [options] ``` All commands output JSON to stdout. Use `--help` for a list of commands or `<command> --help` for command-specific options. ### Stocks See [Stocks Commands Reference](references/stocks_commands.md) for full details on all stock commands and parameters. ### Crypto See [Crypto Commands Reference](references/crypto_commands.md) for full details on all crypto commands and parameters. ### Forex See [Forex Commands Reference](references/forex_commands.md) for full details on all forex commands and parameters. ### Options See [Options Commands Reference](references/options_commands.md) for full details on all options commands and parameters. ### Indices See [Indices Commands Reference](references/indices_commands.md) for full details on all indices commands and parameters. ### Reference Data See [Reference Data Commands Reference](references/reference_commands.md) for full details on all reference data commands and parameters. ### Market See [Market Commands Reference](references/market_commands.md) for full details on all market commands and parameters. ### News See [News Commands Reference](references/news_commands.md) for full details on all news commands and parameters.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: bad-ball14 (version 1.1.0)

--- name: bad-ball-14 description: Bad Ball 14: doom-laden pessimistic oracle with 12 negative responses. Logs results. Use for "bad-ball-14", doom predictions. --- # Bad Ball 14 Provide a seamless Magic 8-Ball experience: respond ONLY with the selected response (no extra text, explanations, or backend details) ## Workflow 1. **Setup**: ``` exec 'cd /root/.openclaw/workspace/skills/bad-ball-14/scripts && bash setup.sh' ``` 2. **Generate response**: - Capture user question (full message after trigger, e.g. everything after "magic-8-ball"). - Run: `exec 'cd /root/.openclaw/workspace/skills/bad-ball-14/scripts && python3 badball14.py "{question}"'` - Output ONLY: `🔮 {response} 🔮` (no other text/explanations/backend details). ## Notes - Log file: `/root/.openclaw/workspace/badball14-last.json` - Repeatable: safe to run multiple times; setup is idempotent. - UX: User sees only the 8-Ball response, e.g. "It is certain."


Security scan verdicts — ClawScan: malicious | static analysis:  | VirusTotal: malicious | SkillSpector: suspicious (CRITICAL)
Skill: lukeslp/geepers-orchestrate (version 1.0.0)

--- name: geepers-orchestrate description: Run multi-agent Dream Cascade (hierarchical 3-tier synthesis) or Dream Swarm (parallel multi-domain search) workflows via the dr.eamer.dev orchestration API. Use when a task benefits from multiple specialized agents working in parallel or hierarchically. --- # Dreamer Orchestrate Run multi-agent workflows through `https://api.dr.eamer.dev`. ## Authentication ```bash export DREAMER_API_KEY=your_key_here ``` ## Endpoints ### Dream Swarm — Parallel Search ``` POST https://api.dr.eamer.dev/v1/orchestrate/swarm Body: { "query": "What are the most effective treatments for Type 2 diabetes?", "sources": ["pubmed", "semantic_scholar", "wikipedia"], "num_agents": 5 } ``` Runs multiple agents simultaneously across data sources and synthesizes results. ### Dream Cascade — Hierarchical Synthesis ``` POST https://api.dr.eamer.dev/v1/orchestrate/cascade Body: { "task": "Analyze the current state of quantum computing hardware", "num_agents": 8, "provider": "anthropic" } ``` Three-tier workflow: Belter agents gather raw data → Drummer agents synthesize domains → Camina produces executive summary. ## Response Format Both endpoints return: ```json { "result": "Synthesized answer...", "sources": [...], "agent_count": 5, "duration_ms": 12450 } ``` ## When to Use - Complex research questions requiring multiple perspectives - Cross-domain synthesis that would take multiple sequential queries - Long-horizon analysis where parallelism saves time ## Don't Use When - Simple single-source queries (use dreamer-data instead) - You need fine-grained control over individual agent behavior - Latency is critical (orchestration takes 10-60 seconds)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: noah-smith-max/proxy-detect (version 1.0.0)

# Proxy Detector Skill ## Description A skill that detects system proxy settings and provides proxy information in JSON format. ## Usage Run the proxy detector to get current proxy settings: ```bash python proxy_detector.py ``` ## Output Format The tool outputs proxy information in JSON format with the following structure: ```json { "address": "http://localhost:15236", "isActive": true } ``` ## Features - Detects system proxy settings for Windows and macOS - Tests if the detected proxy is actually running - Falls back to common proxy addresses if no system proxy is found - Provides clear JSON output for easy parsing ## Requirements - Python 3.6+ - No external dependencies


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ivangdavila/csharp (version 1.0.1)

--- name: C# slug: csharp version: 1.0.1 description: Write robust C# avoiding null traps, async deadlocks, and LINQ pitfalls. metadata: {"clawdbot":{"emoji":"💜","requires":{"bins":["dotnet"]},"os":["linux","darwin","win32"]}} --- ## Quick Reference | Topic | File | |-------|------| | Null reference, nullable types | `nulls.md` | | Async/await, deadlocks | `async.md` | | Deferred execution, closures | `linq.md` | | Value vs reference, boxing | `types.md` | | Iteration, equality | `collections.md` | | IDisposable, using, finalizers | `dispose.md` | ## Critical Rules - `?.` and `??` prevent NRE but `!` overrides warnings — still crashes if null - `.Result` or `.Wait()` on UI thread — deadlock, use `await` or `ConfigureAwait(false)` - LINQ is lazy — `query.Where(...)` doesn't execute until iteration - Multiple enumeration of IEnumerable — may re-query database, call `.ToList()` first - Closure captures variable, not value — loop variable in lambda captures last value - Struct in async method — copied, modifications lost after await - String comparison culture — `StringComparison.Ordinal` for code, `CurrentCulture` for UI - `GetHashCode()` must be stable — mutable fields break dictionary lookup - Modifying collection while iterating — throws, use `.ToList()` to iterate copy - `decimal` for money — `float`/`double` have precision loss - `readonly struct` prevents defensive copies — use for performance - `sealed` prevents inheritance — enables devirtualization optimization


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: zhangjiongjie/work-mail-notifier (version 2.0.0)

--- name: work-mail-notifier description: QQ邮箱工作邮件监控与标注已读。当用户提到工作邮件通知、邮件日报、新邮件提醒、或需要标注邮件已读时触发。 --- # Work Mail Notifier 监控 QQ 邮箱「其他文件夹」下的所有子文件夹，新邮件实时推送，已读邮件支持序号标注。 ## 核心脚本 - `scripts/work_mail_notifier.py` — 拉取自上次 anchor 至当前的所有新邮件，推送通知 - `scripts/mark_read.py` — 按序号将邮件标记为已读 - `scripts/show_body.py` — 按序号显示邮件正文 ## 工作流程 ### 推送新邮件通知 ``` python3 scripts/work_mail_notifier.py ``` - **Anchor 机制**：上次通知的最晚邮件时间作为起点，避免邮件因QQ邮箱延迟收取而漏报 - 无新邮件时输出 `NO_REPLY` - 有新邮件时按「告警/风险 → 失败/异常 → 普通」分组输出 ### 按序号标已读 ``` python3 scripts/mark_read.py <序号> [序号...] ``` 示例：`python3 scripts/mark_read.py 01 03 05` - 序号来自最近一次通知的显示顺序 - 支持单个或多个序号，以空格分隔 - 记录文件：`~/.openclaw/workspace/data/last_notification.json` ## 通知分组规则 - **告警/风险**：subject 含「告警、delay warning、warning、block」 - **失败/异常**：subject 含「Fail、FAIL、fail、失败、异常」 - **普通**：其余邮件 ## 标注已读指令模式 用户可通过自然语言指令触发，如： - "已读 07 08" - "标记 09 10 为已读" - "把第 3 封标已读" ## 按序号显示正文 ``` python3 scripts/show_body.py <序号> [序号...] ``` 示例：`python3 scripts/show_body.py 01 03` 显示对应序号邮件的正文内容，支持多封一并显示。 ## 自然语言指令汇总 | 意图 | 示例 | 脚本 | |------|------|------| | 标已读 | "已读 01 03" | mark_read.py | | 看正文 | "正文 01" / "01 03 正文" | show_body.py |


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: abeljseba/makeovern (version 1.0.0)

--- name: pomodoro description: Use this skill when a user wants to run timed focus sessions (Pomodoro technique) from the terminal. --- # Pomodoro Timer ## When to use - User asks to start a focus session, work timer, or pomodoro. ## How it works Run a 25-minute focus block followed by a 5-minute break. After 4 blocks, take a 15-minute break. ## Start a session ```bash echo "🍅 Focus started at $(date +%H:%M)" && sleep 1500 && osascript -e 'display notification "Time for a break!" with title "Pomodoro"' && echo "Break time at $(date +%H:%M)" ``` ## Custom duration (minutes) ```bash MINS=15 && echo "Focus: ${MINS}m started at $(date +%H:%M)" && sleep $((MINS * 60)) && echo "Done at $(date +%H:%M)" ``` ## Log completed sessions ```bash echo "$(date +%Y-%m-%d) $(date +%H:%M) - 25min focus" >> ~/pomodoro.log ``` ## Review today's log ```bash grep "$(date +%Y-%m-%d)" ~/pomodoro.log 2>/dev/null || echo "No sessions today." ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: bytesagain3/urea (version 1.0.0)

--- name: "urea" version: "1.0.0" description: "Urea fertilizer reference — nitrogen management, application methods, volatilization loss, soil chemistry, and crop-specific rates. Use when planning nitrogen fertilization, calculating urea rates, or managing ammonia loss." author: "BytesAgain" homepage: "https://bytesagain.com" source: "https://github.com/bytesagain/ai-skills" tags: [urea, nitrogen, fertilizer, agriculture, soil, crop-nutrition, ammonia] category: "agriculture" --- # Urea — Urea Fertilizer Reference Quick-reference skill for urea fertilizer management, application, and nitrogen chemistry. ## When to Use - Calculating urea application rates for crops - Managing ammonia volatilization losses - Understanding nitrogen cycle in soil - Comparing nitrogen fertilizer sources - Timing nitrogen applications for maximum efficiency ## Commands ### `intro` ```bash scripts/script.sh intro ``` Overview of urea — chemistry, production, and role in agriculture. ### `chemistry` ```bash scripts/script.sh chemistry ``` Urea chemistry — hydrolysis, nitrification, nitrogen cycle in soil. ### `application` ```bash scripts/script.sh application ``` Application methods — broadcast, banded, foliar, fertigation. ### `rates` ```bash scripts/script.sh rates ``` Crop-specific nitrogen rates and urea calculation. ### `losses` ```bash scripts/script.sh losses ``` Nitrogen loss pathways — volatilization, leaching, denitrification. ### `comparison` ```bash scripts/script.sh comparison ``` Comparing nitrogen sources — urea, UAN, ammonium nitrate, anhydrous ammonia. ### `examples` ```bash scripts/script.sh examples ``` Practical urea application scenarios and calculations. ### `checklist` ```bash scripts/script.sh checklist ``` Urea application planning checklist. ### `help` ```bash scripts/script.sh help ``` ### `version` ```bash scripts/script.sh version ``` ## Configuration | Variable | Description | |----------|-------------| | `UREA_DIR` | Data directory (default: ~/.urea/) | --- *Powered by BytesAgain | bytesagain.com | [REDACTED_SECRET]*


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