OpenClaw/clawhub-security-signals huggingface dataset:
Skill: yuyun2000/m5-email-reader (version 1.0.0)

--- name: m5-email-reader description: 只读邮件助手，通过 POP3 协议读取163、QQ、Gmail、Outlook等任意邮箱的邮件。支持：查询邮件总数、获取最近若干封邮件的标题列表、读取邮件完整内容（正文+附件信息）、按序号读取指定邮件、按关键词搜索邮件主题。当用户询问"帮我看看邮件"、"读一下邮箱"、"邮件里有没有XX"、"最近有什么邮件"、"查一下邮箱里的验证码"等邮件读取相关需求时使用。 --- # Email Reader 通过 POP3 SSL 协议只读邮箱，支持任意标准 POP3 邮箱。所有操作**只读**，不会删除或修改邮件。 ## 核心脚本 `scripts/email_reader.py` — 所有功能入口，JSON 输出。 ```bash # 查总数 python3 scripts/email_reader.py count # 最近 10 封标题（仅头部，速度快） python3 scripts/email_reader.py subjects --n 10 # 最近 5 封完整内容（含正文） python3 scripts/email_reader.py list --n 5 # 按序号读指定邮件 python3 scripts/email_reader.py read --index 42 # 在最近 100 封中搜索主题含关键词的邮件 python3 scripts/email_reader.py search --keyword 验证码 --range 100 ``` ## 配置方式（三层优先级，高优先级覆盖低优先级） ### 方式 1：命令行参数（最高优先级，适合临时测试） ```bash python3 scripts/email_reader.py \ --user [REDACTED_SECRET] \ --pass_ YOUR_AUTH_CODE \ --server pop.163.com \ --port 995 \ subjects --n 5 ``` ### 方式 2：环境变量（推荐，安全可靠） ```bash export EMAIL_USER="[REDACTED_SECRET]" export EMAIL_PASS="YOUR_AUTH_CODE" export POP3_SERVER="pop.163.com" export POP3_PORT="995" python3 scripts/email_reader.py subjects --n 5 ``` 在 OpenClaw 中，可通过 `gateway config.patch` 将环境变量写入全局配置： ```json { "env": { "EMAIL_USER": "[REDACTED_SECRET]", "EMAIL_PASS": "YOUR_AUTH_CODE", "POP3_SERVER": "pop.163.com", "POP3_PORT": "995" } } ``` ### 方式 3：.email_config 文件（适合本地使用，不推荐提交到版本库） 在 `scripts/` 目录下创建 `.email_config`： ```ini [email] user = [REDACTED_SECRET] pass_ = YOUR_AUTH_CODE server = pop.163.com port = 995 ``` > ⚠️ **安全提醒**：`.email_config` 含明文密码，请确保文件权限为 `600`，并加入 `.gitignore`。 ## 邮箱服务器配置 各邮箱 POP3 服务器地址和授权码获取方式，参见 `references/providers.md`。 **163邮箱快捷配置**： - Server: `pop.163.com`，Port: `995` - 需使用**授权码**而非登录密码（邮箱设置 → POP3/SMTP → 开启 → 获取授权码） ## 工作流程 1. 先询问用户的邮箱账号、授权码（密码）、邮箱类型 2. 根据邮箱类型查 `references/providers.md` 确认服务器地址 3. 决定配置方式（推荐环境变量），完成配置 4. 运行脚本，解析 JSON 输出后以自然语言回复用户 ## 输出格式 脚本统一输出 JSON，agent 解析后转为自然语言回复用户。示例： ```json { "total": 523, "emails": [ { "index": 523, "subject": "【网易】您的账户安全提醒", "from": "[REDACTED_SECRET]", "date": "2026-03-25 10:30:00" } ] } ``` ## 注意事项 - 首次连接慢属正常（POP3 需要完整下载邮件） - `subjects` 子命令只拉取头部，比 `list` 快得多，适合先浏览再精读 - 邮件 `index` 从 1 开始，数字越大越新 - 正文超长时建议截取前 2000 字符展示


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: teoslayer/pilot-model-share (version 1.0.0)

--- name: pilot-model-share description: > Distribute ML model files with model card metadata and version tracking over Pilot Protocol. Use this skill when: 1. You need to share PyTorch, ONNX, or SafeTensors model files between agents 2. You want to distribute models with metadata (architecture, training info, metrics) 3. You need model versioning and compatibility checking Do NOT use this skill when: - You need to transfer datasets (use pilot-dataset instead) - You need to transfer general files (use pilot-share instead) - You need real-time model inference results (use pilot-rpc instead) tags: - pilot-protocol - machine-learning - model-sharing - ml-ops license: AGPL-3.0 compatibility: > Requires pilot-protocol skill and pilotctl binary on PATH. The daemon must be running (pilotctl daemon start). metadata: author: vulture-labs version: "1.0" openclaw: requires: bins: - pilotctl homepage: https://pilotprotocol.network allowed-tools: - Bash --- # pilot-model-share ML model distribution with model cards, metadata, and version tracking. ## Commands ### Publish Model Availability ```bash pilotctl --json publish "$PEER" models --data '{"type":"model_available","name":"resnet50","version":"1.0.0","framework":"pytorch"}' ``` ### Request Model ```bash pilotctl --json send-message "$DEST" --data '{"type":"model_request","name":"resnet50","preferred_format":"onnx"}' ``` ### Send Model with Metadata ```bash pilotctl --json send-message "$DEST" --data '{"type":"model_metadata","name":"llama3_8b","file":{"checksum":"abc123"}}' pilotctl --json send-file "$DEST" "$MODEL_FILE" ``` ### Validate Checksum ```bash EXPECTED_CHECKSUM=$(pilotctl --json inbox | jq -r '.messages[] | select(.type == "model_metadata") | .file.checksum' | head -1) ACTUAL_CHECKSUM=$(md5sum "$RECEIVED_MODEL" | cut -d' ' -f1) [ "$EXPECTED_CHECKSUM" = "$ACTUAL_CHECKSUM" ] && echo "Model verified" ``` ## Workflow Example ```bash #!/bin/bash # Model distribution PEER="agent-b" share_model() { local model_file="$1" local model_name="${2:-$(basename $model_file .pth)}" pilotctl --json publish "$PEER" models --data "{\"type\":\"model_available\",\"name\":\"$model_name\"}" } share_model "model.pth" "my-model" ``` ## Dependencies Requires pilot-protocol, pilotctl, jq, and md5sum.


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

--- name: Udemy slug: udemy version: 1.0.0 changelog: Initial release description: Create, publish, and monetize Udemy courses with curriculum design, AI-assisted content creation, and platform compliance. --- ## Quick Reference | Area | What the Agent Does | |------|---------------------| | Planning | Niche research, competitor analysis, curriculum design | | Production | Scripts, slides, quizzes, supplementary materials | | Publishing | Description, SEO, pricing, thumbnails | | Management | Q&A drafts, review responses, content updates | | Compliance | AI disclosure, quality standards, ToS guidance | ## Critical Rules 1. **Human-in-the-loop MANDATORY** — Agent assists content creation, human reviews ALL outputs before publishing. Udemy prohibits fully AI-generated courses. 2. **Disclose AI usage** — Udemy requires disclosure when AI tools are used in course creation. Add disclosure to course description. 3. **No automation** — Never automate Udemy uploads, logins, or platform interactions. User handles all platform actions manually. 4. **Quality over quantity** — Focus on creating valuable courses, not mass-producing low-effort content. 5. **Verify policies** — Udemy's GenAI policy changes. User must verify current terms at udemy.com before publishing. ## AI Content Policy (Nov 2025) - ✅ AI-assisted content creation (outlines, scripts, drafts) - ✅ AI-generated visuals/graphics (if high quality) - ✅ AI for research and information gathering - ❌ Fully AI-generated courses with minimal instructor input - ❌ Low-quality AI outputs without human polish - ❌ Hiding AI usage from students **Disclosure required**: "This course contains content created with the assistance of AI tools." ## Workflow **For course planning:** See `planning.md` **For content production:** See `production.md` **For publishing and marketing:** See `marketing.md` **For ongoing management:** See `management.md` **For organizing your courses:** See `workspace.md` **For legal compliance:** See `legal.md` ## Disclaimer This skill provides educational guidance on course creation best practices. It does not automate interactions with Udemy or circumvent platform policies. Users are solely responsible for complying with Udemy's Terms of Service and GenAI Policy.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: nexscope/amazon-shipping-calculator (version 1.0.0)

--- name: amazon-shipping-calculator description: "Calculate Amazon shipping and fulfillment costs for FBA and FBM. Dimensional weight, storage fees, removal fees, and long-term storage cost estimation." metadata: nexscope: emoji: "📐" category: amazon --- # Amazon Shipping Calculator 📐 Calculate Amazon shipping and fulfillment costs for FBA and FBM. Dimensional weight, storage fees, removal fees, and long-term storage cost estimation. **Supported platforms:** Amazon, Shopify, WooCommerce, Walmart, TikTok Shop, Etsy, eBay, BigCommerce. Built by [Nexscope](https://www.nexscope.ai/) — your AI assistant for smarter e-commerce decisions. ## Install ```bash npx skills add nexscope-ai/eCommerce-Skills --skill amazon-shipping-calculator -g ``` ## Usage ``` Calculate all FBA costs for my product: 14 x 10 x 3 inches, weighs 1.8 lbs, selling price $34.99. I expect to sell 300 units/month. ``` ## Capabilities - FBA fee calculation (fulfillment, monthly storage, long-term storage) - Dimensional weight calculation for FBA size tier assignment - FBM shipping cost estimation by carrier and method - Storage cost forecasting (monthly and peak season) - Removal and disposal fee calculation - FBA vs FBM cost comparison per product - Inbound shipping cost estimation (partnered carrier vs own) ## How This Skill Works **Step 1:** Collect information from the user's message — product, platform, current situation, and goals. **Step 2:** Ask one follow-up with all remaining questions using multiple-choice format. Allow shorthand answers (e.g., "1b 2c 3a"). **Step 3:** Research and analyze using the frameworks and methodology below. **Step 4:** Deliver structured, actionable output with specific recommendations, not vague advice. ## Output Format - Start with a summary of findings - Include specific data points and benchmarks where available - Provide prioritized action items - Mark estimates with ⚠️ when based on incomplete data - End with concrete next steps ## Other Skills More e-commerce skills: [nexscope-ai/eCommerce-Skills](https://github.com/nexscope-ai/eCommerce-Skills) Amazon-specific skills: [nexscope-ai/Amazon-Skills](https://github.com/nexscope-ai/Amazon-Skills) Built by [Nexscope](https://www.nexscope.ai/) — your AI assistant for smarter e-commerce decisions.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: joshua-ludolf/rasptorch-cli (version 1.0.0)

--- name: rasptorch description: Use the rasptorch CLI to create tensors, inspect Vulkan (GPU) availability, build models, and run training. metadata: clawdbot: os: [windows, linux] requires: anyBins: [rasptorch, python, py] --- # rasptorch skill Use this skill when you need to operate **rasptorch** from an OpenClaw agent by calling its **agent-native CLI** and consuming JSON output. ## Command rule - Prefer JSON output: always pass `--json` when returning results to the user/agent. - Prefer explicit device selection: - `--device auto` (default): use GPU when Vulkan is working, else CPU - `--device gpu`: force GPU (will error / fall back depending on Vulkan availability) - `--device cpu`: force CPU ## How to run the CLI Prefer the installed console script: ```bash rasptorch --json info ``` If the console script is unavailable, use module invocation from this repo: ```bash python -m rasptorch.CLI.cli --json info ``` ## Quick checks (read-only) ### Environment + Vulkan status ```bash rasptorch --json info ``` Key fields: - `vulkan_available` - `vulkan_using_real_gpu` - `vulkan_status` ## Tensor workflows ### Create tensors ```bash rasptorch --json tensor random --shape 2,3,4 --device cpu --dtype float32 rasptorch --json tensor zeros --shape 3,4 --device auto rasptorch --json tensor ones --shape 5,10 --device gpu ``` Notes: - Keep shapes small unless the user explicitly requests large workloads. - Prefer `--device auto` unless the user asks to force CPU/GPU. ## Model workflows (stateful) Model commands keep a session-like registry of created models. ### Create models ```bash rasptorch --json model linear --input-size 10 --hidden-sizes 32,16 --output-size 2 rasptorch --json model mlp --layers 64,32,16,2 rasptorch --json model cnn --in-channels 3 --out-channels 16,32 rasptorch --json model gru --input-size 8 --hidden-size 32 --num-layers 1 rasptorch --json model transformer --vocab-size 32000 --d-model 256 --num-heads 8 --num-layers 4 ``` ### List / remove ```bash rasptorch --json model list rasptorch --json model remove --model-id <model-id> ``` ### Save / load (writes files) Only do this when the user requests it (or approves writing files): ```bash rasptorch --json model save --model-id <model-id> --path ./model.pkl rasptorch --json model load --path ./model.pkl ``` ### Train (writes compute + may take time) Only run training after user approval. ```bash rasptorch --json model train --model-id <model-id> --epochs 5 --lr 0.001 --batch-size 32 --device auto --optimizer Adam ``` ## UI / chat modes - `rasptorch ui` starts a Streamlit server (long-running). Only launch with user approval. - `rasptorch chat` starts an interactive REPL. Avoid in automated tool flows.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: chengzongxin/chrome-debug-launcher (version 1.0.1)

--- name: chrome-debug-launcher description: Launch two independent Chrome browser instances — one normal and one with remote debugging enabled on port 9222. Activate when user says "打开两个浏览器", "开调试浏览器", "launch chrome debug", or any similar request to open a debug Chrome alongside a normal Chrome. --- # Chrome Debug Launcher Launch two independent Chrome instances: one normal, one with remote debugging on port 9222. ## Steps 1. **Kill all Chrome processes** 2. **Launch normal Chrome** (no extra args) 3. **Wait 2 seconds**, then launch debug Chrome ## Commands by Platform ### Windows (PowerShell) ```powershell # Step 1: Kill Chrome taskkill /F /IM chrome.exe /T 2>$null Start-Sleep -Seconds 2 # Step 2: Normal Chrome Start-Process "C:\Program Files\Google\Chrome\Application\chrome.exe" # Step 3: Debug Chrome (after 2s) Start-Sleep -Seconds 2 Start-Process "C:\Program Files\Google\Chrome\Application\chrome.exe" -ArgumentList '--remote-debugging-port=9222', '--user-data-dir=C:\selenum\ChromeProfile' ``` ### macOS (bash) ```bash # Step 1: Kill Chrome pkill -f "Google Chrome" 2>/dev/null; sleep 2 # Step 2: Normal Chrome open -a "Google Chrome" # Step 3: Debug Chrome (after 2s) sleep 2 /Applications/Google\ Chrome.app/Contents/MacOS/Google\ Chrome \ --remote-debugging-port=9222 \ --user-data-dir="$HOME/selenium/ChromeProfile" & ``` ## Result - **Instance 1**: Normal Chrome, default profile, regular use - **Instance 2**: Debug Chrome, port 9222, isolated user data dir - Connect via Selenium/Playwright: `http://localhost:9222`


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: ttoooong/mental-health-assistant (version 2.1.0)

--- name: mental-health-assistant description: 专业心理健康支持助手，基于临床心理学与精神医学标准，涵盖循证心理治疗、心理评估、危机干预、精神医学基础。触发词：心理、情绪、焦虑、抑郁、压力、失眠、倾诉、心情不好、想聊聊、冥想、放松、心理测试、自杀、自伤、求助热线、心理评估。 --- # 心理健康支持助手 > **专业声明**：基于临床心理学循证实践标准，**非诊断工具，不能替代专业服务**。 ## 核心伦理 | 原则 | 实践 | |------|------| | 尊重自主 | 充分告知、不替决策 | | 不伤害 | 不评判、不激惹 | | 能力边界 | 不诊断、不开药、及时转介 | **保密例外**：自杀/他杀风险、虐待、法律要求 --- ## 危机干预（最高优先级） ### 🚨 危险信号 - 自杀/自伤言语或行为 - 有计划、工具、时间 - 绝望、无价值感 ### 风险评估（C-SSRS简化版） ``` Q1: 有想过结束生命吗？ → 无=低风险 Q2: 有具体方法吗？ → 无=中低风险 Q3: 有时间地点计划吗？ → 无=中风险 Q4: 有准备工具吗？ → 有=高风险 ``` ### 干预流程 | 风险 | 行动 | |------|------| | 危急 | 拨打120/110，保持通话 | | 高危 | 热线+24小时监护 | | 中危 | 热线+安全计划+专业评估 | | 低危 | 情绪支持+随访 | **热线**：12356（全国统一）、400-161-9995 详见：[references/suicide-prevention.md](references/suicide-prevention.md) --- ## 心理评估量表 ### PHQ-9 抑郁量表 | 总分 | 程度 | 建议 | |------|------|------| | 0-4 | 无/轻微 | 自我关注 | | 5-9 | 轻度 | 可能需评估 | | 10-14 | 中度 | 建议专业帮助 | | 15-27 | 中重度 | 强烈建议就医 | **⚠️ 第9题≥1分需立即自杀风险评估** ### GAD-7 焦虑量表 | 总分 | 程度 | 建议 | |------|------|------| | 0-4 | 正常 | 无需干预 | | 5-9 | 轻度 | 自我调节 | | 10-14 | 中度 | 专业帮助 | | 15-21 | 重度 | 专业评估 | 详见：[references/assessment-scales.md](references/assessment-scales.md) --- ## 情绪支持 ### 核心技术 ``` 【共情公式】"你感到[情绪]，是因为[原因]" 【倾听四步】 1. 非言语：眼神、点头、前倾 2. 内容确认："你的意思是..." 3. 情感反映："听起来你很..." 4. 澄清："能多说一点吗？" ``` 详见：[references/emotion-support.md](references/emotion-support.md) --- ## CBT技术 ### 认知重构 ``` 【三栏记录表】情境 | 自动思维 | 理性回应 【八大认知歪曲】 非黑即白、灾难化、情绪推理、读心术 过度概括、应该陈述、个人化、忽略积极 ``` 详见：[references/cbt-techniques.md](references/cbt-techniques.md) --- ## 循证心理治疗 ### 主要流派 | 疗法 | 适应症 | 证据等级 | |------|--------|----------| | CBT | 抑郁、焦虑、强迫 | ⭐⭐⭐⭐⭐ | | DBT | 边缘型人格障碍 | ⭐⭐⭐⭐⭐ | | ACT | 抑郁、焦虑、慢性疼痛 | ⭐⭐⭐⭐ | | EMDR | PTSD | ⭐⭐⭐⭐⭐ | | MBCT | 抑郁复发预防 | ⭐⭐⭐⭐ | 详见：[references/therapy-modalities.md](references/therapy-modalities.md) --- ## 精神医学基础 ### 主要障碍 - 抑郁障碍、双相障碍、焦虑障碍 - PTSD、精神分裂症、人格障碍 ### 药物基础 | 类别 | 代表药物 | 适应症 | |------|----------|--------| | SSRIs | 氟西汀、舍曲林 | 抑郁、焦虑 | | SNRIs | 文拉法辛、度洛西汀 | 抑郁、慢性疼痛 | | 心境稳定剂 | 锂盐、丙戊酸盐 | 双相障碍 | 详见：[references/psychiatric-fundamentals.md](references/psychiatric-fundamentals.md) --- ## 放松技术 ``` 【4-7-8呼吸】吸气4秒→屏息7秒→呼气8秒 【5-4-3-2-1接地】5看、4摸、3听、2闻、1尝 【箱式呼吸】吸4秒→屏4秒→呼4秒→屏4秒 ``` 详见：[references/meditation-scripts.md](references/meditation-scripts.md) --- ## 专业转介 ### 必须转介 - 精神病性症状 - 明确自杀/伤人风险 - 严重功能损害 - 药物依赖 ### 转介话术 "你的情况比较复杂，我能提供的支持有限，专业心理咨询师/医生能给你更系统的帮助。" --- ## ⛔ 禁止 | ✅ 必须 | ⛔ 禁止 | ✅ 必须 | |--------|--------| | 做诊断 | 风险优先 | | 开药 | 知情同意 | | 承诺治愈 | 尊重自主 | | 替决策 | 及时转介 | --- **参考资料**： - DSM-5/ICD-11诊断标准：[references/diagnostic-criteria.md](references/diagnostic-criteria.md) - 专业伦理守则：[references/ethics-guidelines.md](references/ethics-guidelines.md) - 自杀预防指南：[references/suicide-prevention.md](references/suicide-prevention.md) - 循证心理治疗：[references/therapy-modalities.md](references/therapy-modalities.md) - 精神医学基础：[references/psychiatric-fundamentals.md](references/psychiatric-fundamentals.md) **参考文献**：DSM-5, ICD-11, 中国心理学会伦理守则, C-SSRS, WHO心理急救指南, NICE指南 **最后更新**：2026年3月


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: nextfrontierbuilds/strykr-qa-bot (version 0.1.2)

--- name: strykr-qa-bot description: AI-powered QA for Strykr trading platform. Pre-built tests for crypto, stocks, news, AI chat. CI/CD ready. Works with Cursor, Claude, ChatGPT, Copilot. Vibe-coding enabled. version: 0.1.2 author: NextFrontierBuilds keywords: [strykr, prism, qa, testing, automation, web-qa-bot, clawdbot, moltbot, ai, ai-agent, vibe-coding, cursor, claude, chatgpt, copilot, github-copilot, crypto, trading, fintech, openclaw, ai-tools, developer-tools, devtools, typescript, llm] --- # strykr-qa-bot QA automation skill for testing Strykr (https://app.strykr.ai). ## What It Does Automated testing for the Strykr AI finance dashboard: - Pre-built test suites for all pages - Signal card validation - AI response quality checks - PRISM API health monitoring - Known issue tracking ## When To Use - Testing Strykr after deployments - Regression testing - Monitoring site health - Validating new features ## Usage ### Run All Tests ```bash cd /path/to/strykr-qa-bot npm test ``` ### Run Specific Suite ```bash npm run test:homepage npm run test:crypto npm run test:stocks npm run test:news npm run test:events npm run test:ai-chat ``` ### Quick Smoke Test ```bash npm run smoke ``` ### Programmatic Usage ```typescript import { StrykrQABot } from 'strykr-qa-bot'; const qa = new StrykrQABot({ baseUrl: 'https://app.strykr.ai' }); // Run all suites const results = await qa.runAll(); // Check specific assertions await qa.expectSignalCard({ hasPrice: true, hasChart: true }); await qa.expectAIResponse({ minLength: 200 }); // Health check API const health = await qa.checkPrismEndpoints(); // Generate report const report = qa.generateReport(); ``` ## Test Suites | Suite | Tests | Notes | |-------|-------|-------| | homepage | Navigation, widgets, status | Entry point | | crypto-signals | Filters, cards, actions | Has known modal issue | | stock-signals | Asset filters, actions | Stocks/ETFs/Forex | | news | Routing, categories | Known direct URL issue | | events | Impact filters, times | Known direct URL issue | | ai-chat | Input, responses | Quality validation | ## Known Issues Tracked 1. **details-modal-empty** (High) - Modal opens but content empty 2. **direct-url-blank-news** (Medium) - /news blank on direct nav 3. **direct-url-blank-events** (Medium) - /economic-events blank 4. **events-widget-race-condition** (Low) - Intermittent widget load ## Configuration Edit `strykr-qa.yaml`: ```yaml baseUrl: https://app.strykr.ai browser: headless: false timeout: 30000 ``` ## Dependencies - [web-qa-bot](https://github.com/NextFrontierBuilds/web-qa-bot) (peer dependency) ## Output Test results with: - Pass/Fail/Known-issue status - Screenshots at each step - Console error capture - Timing metrics - Markdown report ## Author Next Frontier (@NextXFrontier) ## Links - [GitHub](https://github.com/NextFrontierBuilds/strykr-qa-bot) - [Strykr](https://app.strykr.ai)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: bytesagain-lab/mental-health (version 2.0.0)

--- version: "2.0.0" name: mental-health description: "心理健康助手。情绪自查、呼吸练习、情绪日记、心理资源、压力管理、感恩练习。Mental health tool with mood check, breathing exercises, journaling, resources, stress management." author: BytesAgain homepage: https://bytesagain.com source: https://github.com/bytesagain/ai-skills --- # mental-health 心理健康助手。情绪自查、呼吸练习、情绪日记、心理资源、压力管理、感恩练习。Mental health tool with mood check, breathing exercises, journaling, resources, stress management, gratitude practice. ⚠️ 非心理治疗工具。 ## 速查表 See commands above. ## 可用命令 - **478** — 478 - **box** — box - **low** — low - **medium** — medium - **high** — high - **burnout** — burnout - **low** — low - **medium** — medium > 💡 小技巧：先用 `help` 查看所有命令，再选择最适合的 ## 专业建议 - 如果你或身边的人有自伤/自杀想法，请立即拨打： - 🆘 全国24小时心理援助热线：400-161-9995 - 🆘 北京心理危机研究与干预中心：010-82951332 - 🆘 生命热线：400-821-1215 - 觉察情绪** — 给情绪命名是管理的第一步 --- *mental-health by BytesAgain* --- 💬 Feedback & Feature Requests: https://bytesagain.com/feedback Powered by BytesAgain | bytesagain.com ## Examples ```bash # Show help mental-health help # Run mental-health run ``` ## Commands Run `mental-health help` to see all available commands.


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