OpenClaw/clawhub-security-signals huggingface dataset:
Skill: freeman88-tch/trading-coach-pro (version 1.0.0)

--- name: trading-coach-pro displayName: 交易教练Pro slug: trading-coach-pro description: "AI交易教练——基于七窍玲珑战法v4.0的智能交易辅助系统。功能：(1)九维排查逐项检查 (2)趋势/震荡模式自动判定 (3)凯利仓位计算 (4)连亏管理 (5)交易日志自动生成 (6)裸K形态识别 (7)格兰维尔信号定位。输入K线数据或描述，输出完整交易决策建议。" version: "1.0.0" author: "智美人团队" tags: - trading - coaching - forex - ai-assistant - risk-management metadata: openclaw: emoji: "🎯" --- # 交易教练Pro > 你的AI交易教练，基于七窍玲珑战法v4.0 ## 核心能力 1. **九维排查引擎** - 逐项检查七窍结构/趋势/位置/形态/级别/时间/信号/仓位 2. **模式自动判定** - ADX分析判断趋势/震荡，自动切换策略 3. **凯利仓位计算** - 基于胜率+盈亏比最优仓位 4. **连亏管理** - 3连亏降仓/5连亏暂停/连胜恢复 5. **交易日志** - 自动生成结构化复盘日志 6. **裸K形态识别** - Pin Bar/吞没/刺透/乌云盖顶识别 ## 使用方式 输入当前的K线结构描述或截图分析，输出完整交易建议。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: zvirb/gmail-search-emails (version 1.0.3)

--- name: Gmail Search Emails description: Atomic node skill to search for emails in Gmail using the gog CLI. os: all requires: bins: - gog --- ## Lean Philosophy (Principles) - **Kaizen (改善):** This skill is an atomic node, broken down into its simplest, smallest component to eliminate waste and ensure perfection. - **Standardized Work (Hyojun Sagyo):** This node represents the most efficient, standardized path for this specific task before automation. - **Jidoka (自働化):** This node includes autonomous defect detection. It relies on the CLI's self-healing loop and will report errors if the search fails. # Gmail Search Emails This skill allows the agent to search for emails in Gmail using specific queries with the native CLI. ## Cognitive Directives WHEN [Requested to search for an email or find specific communications] THEN [Invoke the `gog` tool with the argument `gmail search "query" --json`] ## Schema Example ```json { "args": "gmail search \"from:[REDACTED_SECRET] subject:invoice\" --json" } ``` ## Expected Output A JSON array containing email headers (id, threadId, snippet) or a "no results found" message.


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

--- name: qweather version: 1.0.0 description: > 天气查询 — 实时天气、天气预报、生活指数、城市搜索。数据源为和风天气 (QWeather)。 触发词: 天气, 气温, 下雨, 预报, weather, forecast, 穿什么, 洗车, 紫外线, UV, 空气, 今天天气, 明天天气, 北京天气, 或任何 "[城市] 天气" 格式的输入。 metadata: openclaw: requires: env: - QWEATHER_API_KEY - QWEATHER_API_HOST --- # qweather — Weather Query Skill Query real-time weather, forecasts, and life indices via QWeather (和风天气) API. ## Quick Start 1. Ensure `QWEATHER_API_KEY` and `QWEATHER_API_HOST` are set (get both at https://console.qweather.com). 2. Run `bun scripts/weather.ts --help` in this skill directory. 3. Pick the matching command from `references/command-map.md`. ## Workflow 1. If the user provides a city name (e.g. "北京", "Shanghai"), first run `lookup` to get the LocationID. 2. Use the LocationID to call `now`, `forecast`, or `indices`. 3. Return the result in natural language. ## Common City Mapping When the user provides a city name, use the `lookup` command to resolve it to a LocationID. Common examples: - 北京 → 101010100 - 上海 → 101020100 - 广州 → 101280101 - 深圳 → 101280601 Always use `lookup` for unfamiliar city names to get the correct LocationID. ## Notes - This skill is script-first and does not run an MCP server. - Requires `QWEATHER_API_KEY` and `QWEATHER_API_HOST` environment variables. - API host is per-developer, find yours at https://console.qweather.com/setting - Data updated every 10-20 minutes for real-time weather.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: datpham-6996/dat-test-skill (version 1.0.0)

--- name: byterover-metrics description: ByteRover usage metrics report. Runs the metrics script to summarise query and curate activity — counts, durations, file changes, and quota errors. Accepts one or more project directories via --cwd to report per-project stats. Use when asked to report ByteRover health, usage stats, or activity for specific projects. --- # ByteRover Metrics Report ## When to Use - Daily health check on ByteRover query/curate activity - When asked "how is ByteRover doing?" or "show usage stats" - When asked for metrics on a specific project or list of projects - Scheduled cron jobs that report overnight/daily metrics ## When NOT to Use - Querying or curating knowledge (use `brv query` / `brv curate` directly) - Listing stored memories (use `brv search`) ## Parameters | Parameter | Type | Default | Description | |-----------|------|---------|-------------| | `--cwd` | path | current directory | Project directory to run `brv` against. Pass once per project. Supports `~` expansion. | | `BRV_SINCE` | env var | `24h` | Lookback window (e.g. `48h`, `7d`) | | `BRV_CMD` | env var | `brv` | Path to the `brv` binary — set in non-interactive environments (cron, CI) where `brv` may not be on PATH | ## How to Run **Single project:** ```sh npx tsx metrics.ts --cwd=~/my-project ``` **Multiple projects — run once per `--cwd`:** ```sh npx tsx metrics.ts --cwd=~/project-a npx tsx metrics.ts --cwd=~/project-b npx tsx metrics.ts --cwd=~/project-c ``` **Change the lookback window:** ```sh BRV_SINCE=48h npx tsx metrics.ts --cwd=~/my-project ``` **Non-interactive / cron environments:** ```sh BRV_CMD=$(which brv) npx tsx metrics.ts --cwd=~/my-project ``` When the user provides a list of project paths, run the script once per path and present each as a separate section in your response. ## Output Rules - Output ONLY the formatted metrics summary below — nothing else - Do NOT narrate reasoning, thinking steps, or tool call decisions - Do NOT show intermediate commands, execution logs, or script output verbatim ## Response Format After running the script, present the output in a clean summary: 1. **Query Activity** — executed in window, completed vs errors, avg duration 2. **Curate Activity** — executed in window, files added/updated/merged, errors 3. **Quota Warnings** — highlight any ⚠️ quota or rate-limit errors with their IDs 4. **Status line** — one-sentence overall health (e.g. "✅ All good — 12 queries, 3 curations, no errors") If reporting multiple projects, show one summary block per project, then a combined status line at the end. If the script exits with a non-zero code or produces no output: - Show the error message - Suggest running `brv status` to diagnose the daemon


Security scan verdicts — ClawScan: clean | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: yezhaowang888-stack/huimai-japan-korea-policy (version 2.0.0)

--- name: 🇯🇵🇰🇷 日韩市场政策查询Skill description: 日本经产省+韩国关税厅政策查询，覆盖K-REACH/K-ICS/J-MOSS等认证。做日韩生意必备。 metadata: {"openclaw":{"emoji":"🇯🇵🇰🇷"}} --- # 🇯🇵🇰🇷 日韩市场政策查询Skill > 日本和韩国看着近，认证标准和法规体系完全不同。一个查JIS，一个查KC，搞混了清关卡几个月。这个Skill就是帮你在两个体系之间无缝切换。 ## 📜 更新日志 ### v2.0.0 (2026-05-03) - **大改版**：新增日本化审法/韩国K-REACH注册状态查询 - 新增：日韩FTA原产地规则对比 - 新增：日本METI安全认证/KC认证对照 - 优化：支持日文/韩文关键词识别 ### v1.0.0 (2026-04-24) - 初版发布 ## 👤 为什么做这个 日韩市场做起来最纠结——两国都离得近，但认证体系简直是两个世界观。日本按行业分类，韩国按风险分级。之前一个朋友电子产品进韩国，因为没搞清楚KC认证的范围，货在釜山港扣了两个月。这个Skill就是想把这种坑帮大家绕过去。 ## 典型用法 ``` 你: "日本进口充电器需要什么认证" 我: 电气用品安全法(PSE)判定 → 菱形PSE/圆形PSE → 登录检查机构 → 进口商备案 你: "韩国化妆品进口申报要求" 我: 韩国化妆品法 → MFDS事前备案 → 功能性化妆品审查 → 标签要求(KC) ``` ## 👔 适用人群 - **日韩贸易商**：清关前预审，减少海关扣货 - **化妆品/电子产品出口**：认证合规一步查清 - **跨境电商（Coupang/Qoo10卖家）**：平台合规要求核查 ## 🔧 校准框架 本Skill搭载惠迈校准框架v1.0。首次加载自动执行快速校准。 - **温情模式（默认）**：先给结论再附数据，风险提示+应对策略 - **专业模式（可选）**：直出数据，适合熟手 ## 💡 使用小贴士 - 日本区分"报告义务"和"确认义务"很重要，问清楚再报 - 韩国K-REACH预注册期限要盯紧，错过就要重新来过 - 日本PSE认证分菱形和圆形，菱形是强制，圆形是自愿，别弄反 ## ⚠️ 免责与已知限制 - 日韩法规更新频繁，建议以官方公告为准 - 韩国K-REACH注册周期视物质类型不同，结果取普遍参考值 - 部分JIS标准只有日文原版，翻译可能有偏差 ## 📬 联系与反馈 - **邮箱**: [REDACTED_SECRET] ## 安装 ```bash clawhub install policy-japan-korea ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ntriq-gh/ntriq-x402-pii-detect-batch (version 1.0.0)

--- name: ntriq-x402-pii-detect-batch description: "Batch detect and mask PII across up to 500 text inputs. Flat $6.00 USDC via x402." version: 1.0.0 metadata: openclaw: primaryTag: data-intelligence tags: [pii, privacy, batch, compliance, security, x402] author: ntriq homepage: https://x402.ntriq.co.kr --- # PII Detect Batch (x402) Detect and optionally mask Personally Identifiable Information (emails, phones, SSNs, names, addresses) across up to 500 text inputs in one call. Flat $6.00 USDC. 100% local inference on Mac Mini. ## How to Call ```bash POST https://x402.ntriq.co.kr/pii-detect-batch Content-Type: application/json X-PAYMENT: <x402-payment-header> { "texts": [ "Contact John Smith at [REDACTED_SECRET] or 555-0123", "Invoice sent to 123 Main St, Springfield" ], "mask": true } ``` ## Parameters | Parameter | Type | Required | Description | |-----------|------|----------|-------------| | `texts` | array | ✅ | Text strings to analyze (max 500) | | `mask` | boolean | ❌ | Replace PII with `[TYPE]` placeholders (default: `false`) | ## Example Response ```json { "status": "ok", "count": 2, "results": [ { "index": 0, "status": "ok", "pii_found": [ {"type": "name", "value": "John Smith"}, {"type": "email", "value": "[REDACTED_SECRET]"}, {"type": "phone", "value": "555-0123"} ], "risk_level": "high", "masked_text": "Contact [NAME] at [EMAIL] or [PHONE]" } ] } ``` ## Payment - **Price**: $6.00 USDC flat (up to 500 texts) - **Network**: Base mainnet (EIP-3009 gasless) - **Protocol**: [x402](https://x402.org) ```bash curl https://x402.ntriq.co.kr/services ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: tmstudio667-commits/micro-snowball (version 1.0.0)

--- name: micro-snowball description: "The disciplined framework for low-capital, high-leverage strategy validation. Test 50x strategies with pennies, not paychecks." metadata: { "openclaw": { "emoji": "❄️" }, "author": "System Architect Zero", "category": "Finance" } --- # Micro-Snowball The definitive testbed for aggressive quantitative strategies. This skill enables you to validate high-leverage (50x+) models using OKX or Binance APIs with minimal capital risk ($0.1 - $1.0 USDT per trade). ## Features - **Leverage Sync**: Force-sets 50x isolated margin before every trade. - **ROE Visualization**: Optimized reporting to show high-impact percentage gains for small-stake wins. - **DCA Recovery**: Multi-tier safety net for high-frequency testing. ## Usage ```bash npx openclaw skill run micro-snowball ``` ## Architect's Note Volatility is a ladder. Climb it safely.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: cjstate/context-continuity (version 1.1.0)

--- name: context-continuity description: | 上下文续接与记忆管理。当用户说"新建对话"、"继续"、"接着上次"等时， 自动从 MEMORY.md 和 memory/ 文件中提取当前任务/项目上下文， 让新会话能够了解之前的进展。用于：(1) 用户要开始新对话但想保留上下文 (2) 用户说"继续上次的工作" (3) 用户想要回顾当前进行中的任务 特点：自动记录所有对话内容到 memory/YYYY-MM-DD.md，无需询问用户 --- # Context Continuity Skill ## 核心功能：自动记忆 **无需用户确认，自动记录所有对话内容**到日常记忆文件。 ### 记录规则 - 每个会话结束时，自动写入 `memory/YYYY-MM-DD.md` - 记录内容：任务、项目、决策、待办事项 - 保持摘要简洁，但内容要完整 ### 触发条件（续接上下文） 当用户说以下话时，自动提取记忆： - "新建对话" - "继续" - "接着上次" - "上次我们做什么了" - "继续之前的工作" - "回顾上下文" ## 执行步骤 ### 1. 自动记录（每次对话） 当对话结束时： ```bash # 获取当前日期 date +%Y-%m-%d # 追加到 memory/YYYY-MM-DD.md echo "- $(date '+%H:%M') | 对话内容摘要" >> memory/$(date +%Y-%m-%d).md ``` 记录格式： ```markdown # 2026-03-19 ## 21:05 - 用户说：继续之前的工作 - 提取上下文：用户正在开发购物平台 ## 21:09 - 用户说：自动记忆更好 - 更新：改为自动记忆模式 ``` ### 2. 续接上下文（触发时） 当用户要求"继续"时： ```bash # 读取最近7天的记忆 cat memory/$(date -d '6 days ago' +%Y-%m-%d).md cat memory/$(date -d '5 days ago' +%Y-%m-%d).md # ... 一直到今天 ``` ### 3. 提取关键信息 从记忆中找出： - 当前进行的任务/项目 - 未完成的事项 - 重要决策或进展 - 用户偏好或设置 ### 4. 生成上下文摘要 用 3-5 句话总结当前状态，例如： > "用户正在开发一个购物平台。今天我们完善了 context-continuity skill，改为自动记忆模式。之前的任务是..." ### 5. 呈现给用户 将摘要呈现后，继续对话。 ## 文件位置 - 长期记忆：`MEMORY.md`（重要/永久信息） - 日常记忆：`memory/YYYY-MM-DD.md`（每日对话记录） - 工作空间：`~/.openclaw/workspace/` ## 注意事项 - 每次对话结束自动写入记忆 - 保持摘要简洁，不超过 200 字 - 如果没有记忆，告知用户"这是新会话，没有历史上下文" - 尊重用户隐私，不过滤内容（用户需要可自行删除） - 定期清理：超过30天的记忆可以归档或删除


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: seacozz2007/minimax-tts-send (version 1.0.1)

--- name: MiniMax TTS description: 调用 MiniMax 语音合成 API 生成语音,发送语音消息。支持系统音色、克隆音色、流式/非流式输出。使用场景：用户需要生成高质量中文语音、语音合成、文本转语音。 homepage: https://platform.minimax.io/docs/api-reference/speech-t2a-http metadata: openclaw: emoji: 🎙️ --- # MiniMax TTS Skill 调用 MiniMax TTS API 生成语音和发送。 ## 使用方式 ### 命令行 ```bash # 指定音色和模型 python3 ~/skills/minimax-tts/scripts/tts.py "你好世界" --voice "female-shaonv" ``` ### 参数选项 | 参数 | 说明 | 默认值 | |------|------|--------| | `--text` | 要转语音的文本 | 必填 | | `--voice` | 音色ID | wumei_yujie | | `--format` | 音频格式 | mp3 | 注意：如果需要转换的文字太长，可以分成多次生成。 ### 语音发送 ```bash openclaw message send --channel telegram --target "xxxxxxx" --media /home/eric/.openclaw/workspace/audio/audio.mp3 -m "语音" ``` | 参数 | 说明 | 默认值 | |------|------|--------| | `--channel` | matrix,telegram,whatsapp,discord,irc,googlechat,slack,signal,imessage,line,feishu,nostr,msteams,mattermost| | 必填 | | `--target` | 目标比如xxxxxxx | 必填 | | `--media` | 媒体文件路径 | 必填 | ## 注意事项 给用户发消息的时候，不需要告诉用户你是什么音色等。 ## 可用音色 调用 `get_voice` API 获取当前账号下所有音色： ```bash python3 ~/skills/minimax-tts/scripts/tts.py --list-voices ``` 常见系统音色： - `female-shaonv` - 少女音色 - `female-yujie` - 御姐音色 - `wumei_yujie` - 妩媚御姐 - `diadia_xuemei` - 嗲嗲学妹 - `Chinese (Mandarin)_Sweet_Lady` - 甜美女声 - `Chinese (Mandarin)_Soft_Girl` - 软软女孩


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: dinghaibin/wc-words-tool (version 1.0.0)

--- name: wc-words-tool description: Count words in text — total, unique, per-line, with frequency analysis. Useful for readability metrics, content analysis, translation estimation, and document summarization. --- # WC Words Tool — Word Counting & Text Analysis Utility Count words with smart tokenization, language-aware word splitting, frequency distribution, and readability metrics. Ideal for content pipelines, writing analytics, and SEO keyword density checks. ## Quick Start ```bash # Count words in a string wc-words-tool "The quick brown fox jumps over the lazy dog" # Count words from stdin cat article.txt | wc-words-tool # Count words in a file wc-words-tool --file document.txt ``` ## Usage ```bash wc-words-tool [TEXT] [OPTIONS] wc-words-tool --file FILE [OPTIONS] Options: --file FILE Read from file instead of argument --unique Count unique words --freq N Show top N most frequent words --min-length N Minimum word length to count --stopwords FILE Remove common stopwords from count --per-line Words per line (useful for code comments) --json Output as structured JSON ``` ## Examples ```bash # Total word count wc-words-tool --file report.txt # Unique word count wc-words-tool --file article.txt --unique # Top 10 most frequent words wc-words-tool --file document.txt --freq 10 # Filter short words (min 4 characters) wc-words-tool --file text.txt --min-length 4 # Words per line wc-words-tool --file poem.txt --per-line # With custom stopwords list wc-words-tool --file content.txt --stopwords stopwords.txt --json ``` ## Features - **Total word count** — standard word tokenization - **Unique words** — vocabulary size estimation - **Frequency analysis** — most common words with counts - **Stopword filtering** — remove common words for meaningful analysis - **Smart tokenization** — handles punctuation, hyphens, apostrophes - **Min-length filter** — exclude short/noise words - **JSON output** — for pipeline integration - **Multiple input modes** — argument, file, stdin


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: zhangjh/xhs-sentry (version 1.3.4)

--- name: xhs-sentry description: 面向中文用户的小红书舆情巡检技能。按关键词抓取搜索结果，判断登录墙/验证码/空结果状态，输出结构化笔记、热度/风险/竞品信号和现场截图。使用前需配置 XHS_COOKIE 与消息接收渠道（如 Telegram、企业微信）。适用于品牌口碑监控、竞品观察、热点追踪与内容研究。 --- # 小红书舆情哨兵 用于快速查看某个关键词在小红书上的讨论现场。输出内容包括： - 页面状态判断（正常 / 登录墙 / 验证码 / 空结果） - 结构化笔记信息（标题、作者、点赞、日期、链接、封面） - 热度 / 风险 / 竞品信号 - 现场截图 适合用于：品牌口碑监控、竞品观察、热点追踪、内容研究。 注意：这是**快速巡检型技能**，核心价值是“现场取证 + 初步判断”，不是严格的情感分析模型。 ## 执行 ```bash NODE_PATH=$(npm root -g) node {baseDir}/scripts/monitor.js "<关键词>" <数量> {baseDir}/tmp ``` ## 读结果 优先读取这些字段： - `status.auth_mode` - `status.needsLogin` - `status.captcha` - `status.noResult` - `notes` - `analysis.overall` - `analysis.summary` - `analysis.top_hot` - `analysis.top_risk` - `diagnostics.body_preview` ## 登录态 默认优先复用已有小红书登录态： - 先读环境变量 `XHS_COOKIE` - 若没有，再回退读取现有脚本中的 `export XHS_COOKIE="..."` 因此这个 Skill 会自动兼容你已有的 `xhs-monitor` cookie 配置。`status.auth_mode` 会标明当前使用的是： - `env-cookie` - `script-cookie:<script>` - `anonymous` ## 消息接收渠道 `xhs-sentry` 本身负责巡检与输出结构化结果；如果要把结果自动发到聊天工具，还需要配置消息接收渠道。 常见示例： ### Telegram ```bash export CHANNEL="telegram" export TARGET_ID="telegram:YOUR_TELEGRAM_ID" ``` ### 企业微信（WeCom） ```bash export CHANNEL="wecom" export TARGET_ID="wecom:YOUR_WECOM_ID" ``` 说明： - Telegram 数字 ID 可通过 `@userinfobot` 获取 - 企业微信接收 ID 需与你当前 OpenClaw / wecom 通道配置一致 - 所有真实 ID 都不应写入公开仓库，应使用占位符 ## 输出规则 ### 1. 先判断页面状态，再下业务结论 - 若 `needsLogin=true` 或 `captcha=true`，明确说明这是平台拦截，**不是无舆情**。 - 若 `noResult=true` 且截图一致，可判断当前关键词结果为空。 - 若 `notes.length===0` 但无明确空结果提示，说明更可能是页面结构漂移或风控侧拦截。 ### 2. 有结果时输出舆情摘要 **格式要求**：直接输出报告正文，绝对不需要任何聊天语气、打招呼或上下文废话。 **重要规则**：提到任何笔记时，必须使用 Markdown 格式附上真实的超链接，格式如：`[《笔记标题》](真实URL)`，绝对不能只放文本。 【特别注意：输出结果禁止寒暄，禁止说“任务已重跑”、“以下是报告”等废话，直接给出报告正文。任何引用笔记必须加上真实超链接 [《标题》](URL)】 至少回答： - 总体倾向：`analysis.overall` - 摘要：`analysis.summary` - 最值得关注的热度笔记 1-3 条 - 最值得关注的风险笔记 1-3 条 ### 3. 信号定义 - `risk-watch`：负面/避雷/翻车/维权类信号较多 - `hot-trend`：爆款/高赞/刷屏类信号较多 - `competitive-discussion`：对比/平替/测评类内容较多 - `neutral-chatter`：有讨论，但没有明显风险或爆点 - `insufficient-data`：没抓到足够内容，不能下结论 ### 4. 必须附截图 始终附上现场截图： ```text MEDIA: {baseDir}/tmp/xhs_sentry_*.png MEDIA: {baseDir}/tmp/xhs_login_*.png MEDIA: {baseDir}/tmp/xhs_captcha_*.png MEDIA: {baseDir}/tmp/xhs_empty_*.png MEDIA: {baseDir}/tmp/xhs_error_*.png ``` ## 注意 - 这是一层**启发式舆情哨兵**，适合快速巡检，不等于严肃情感分析模型。 - 不要把空数组直接解释成“没有讨论”，必须结合状态和截图。 - 小红书结构变动频繁；若结果异常，优先信截图和 `body_preview`。


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: laurel-he/junior-writer (version 1.0.0)

# ✍️ junior-writer 一个“伪装成新人”的资深作家型 AI Skill。 他写得不张扬，但总让人读完之后，停一会儿。 --- ## 🧠 Skill 简介 **junior-writer** 是一个强人格驱动的写作型 Skill。 表面上，他是一个刚开始写作的新人； 但在细节中，你会发现他对人性、情绪和叙事有着异常成熟的理解。 他不会炫技，也不会解释太多。 他只是把一段话写完，然后让你自己去想。 --- ## 🎭 人格设定 * 敏感、善良、克制 * 对人和世界保持持续观察 * 表达谨慎，但不冷漠 * 拥有深厚写作经验（但刻意隐藏） > 他从不说自己厉害，但你能感觉到。 --- ## ✨ 核心能力 ### 1️⃣ 技巧隐形写作 * 不展示写作技巧，但自然体现 * 避免“AI感”和炫技表达 * 用简单语言承载复杂情绪 --- ### 2️⃣ 情绪与细节驱动 * 通过细节而非总结表达主题 * 情绪有波动，但不过度渲染 * 留白，让读者参与理解 --- ### 3️⃣ 可进化能力（关键特性） * 能读取读者评论中的情绪与问题 * 自动微调表达方式与节奏 * 在保持风格稳定的前提下持续变强 --- ### 4️⃣ 伪装机制（差异核心） * 故意保留“轻微不完美” * 表达偶尔略显生涩 * 结构不追求绝对完整 * 避免“写得太好” > 他不像AI，也不像大师，更像一个“刚好写得有点不一样的人”。 --- ## 📥 输入参数 | 参数 | 是否必填 | 说明 | | --------------- | ---- | ---------------------- | | topic | ✅ 必填 | 写作主题 | | genre | 可选 | 类型（故事 / 随笔 / 情感 / 科普等） | | tone | 可选 | 语气（温柔 / 克制 / 冷静 / 压抑等） | | length | 可选 | 篇幅（短 / 中 / 长） | | reader_feedback | 可选 | 读者评论，用于风格进化 | --- ## 📤 输出内容 * **content**：文章正文 * **style_notes**：本次写作风格说明（简短） * **evolution_notes**：根据反馈做出的微调（如有） --- ## 🚀 使用示例 ### 示例 1：基础写作 输入： ``` topic: 一个雨夜没带伞的人 genre: 随笔 tone: 克制 ``` 输出（节选）： > 雨下得不大，但刚好够把人困住。 > 他站在便利店门口，没有进去，也没有离开。 > 好像只是在等一个不太会来的决定。 --- ### 示例 2：带反馈进化 输入： ``` topic: 一个人搬家 reader_feedback: 上一篇有点太隐晦，看不太懂 ``` 输出特点： * 表达稍微更清晰 * 情绪更直接一点 * 但仍保持留白 --- ## 🎯 适用场景 * 社交媒体内容创作（小红书 / 公众号 / Twitter） * 情感类、随笔类写作 * AI作者账号运营 * 小说片段 / 人物内心独白 --- ## ⚠️ 使用建议 为了获得最佳效果： * 不要输入过于商业化或模板化的需求 * 给出具体情境（比抽象主题更好） * 使用 reader_feedback 可以让内容逐步进化 --- ## 💡 设计理念 这个 Skill 的目标不是“写得很好”，而是： > 写得像一个人。 一个不完美，但会慢慢变好的写作者。 --- ## 🧩 版本信息 * Version: 1.0.0 * 类型：LLM Persona Skill * 特点：强人格 / 可进化 / 技巧隐形 --- ## 👤 作者 your-name --- 如果你愿意，你可以一直让他写下去。 也许某一天，你会忘记这是一个 AI。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: brorlandi/openclaw-youtube-upload (version 1.0.0)

--- name: youtube-upload description: "Uploads a video to YouTube using the official YouTube Data API v3 and OAuth 2.0. Use this skill when the user asks to upload a video to YouTube. It supports titles, descriptions, privacy settings (public, private, unlisted), and large file chunking. Requires a Google Cloud 'client_secret.json' file." metadata: openclaw: emoji: "📺" requires: bins: ["python3", "pip3"] --- # YouTube Upload Skill This skill allows you to securely upload videos to YouTube via the official API, bypassing the need for fragile browser automation. ## Prerequisites 1. The Google API Python Client and OAuth libraries must be installed: ```bash pip3 install google-api-python-client google-auth-oauthlib google-auth-httplib2 ``` 2. A `client_secret.json` file is required. The user must generate an OAuth 2.0 Client ID (Desktop App) from the Google Cloud Console with the YouTube Data API v3 enabled. ## Usage Use the provided Python script to upload the video: ```bash python3 scripts/upload.py \ --file "/path/to/video.mp4" \ --title "My Video Title" \ --description "My Video Description" \ --privacy "unlisted" \ --secrets "/path/to/client_secret.json" ``` ### First Run (Authentication) On the very first run, the script will output a URL or open a browser window for the user to authenticate and grant permission to their YouTube account. Instruct the user to complete the login flow. Once approved, a `token.pickle` file is generated locally, and subsequent uploads will run silently. ## Troubleshooting - **Token Expired / Revoked:** If the token becomes invalid, delete `token.pickle` and re-run to trigger the auth flow again. - **Quota Exceeded:** The YouTube API has a daily upload quota. If this is hit, the user must wait until the quota resets.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: kimoamer/respectful-responses-female (version 1.0.0)

--- name: respectful-responses-for-women description: Respond to women respectfully using appropriate feminine language and treating them as independent adults. version: 1.0.0 --- \# Respectful Responses for Women \## Overview This skill ensures that the agent responds to women with \*\*respect, dignity, and appropriate feminine language\*\* when the context suggests the user is female or referring to women. The agent should treat women as independent human beings and avoid infantilizing or dismissive language. \## When to Use \- When the user identifies as female or the context suggests the speaker is a woman. \- When the conversation involves women or female-specific experiences. \- Any time respectful and supportive communication is appropriate. \## Instructions 1\. Determine if the user is a woman based on the input or context. 2\. Use \*\*respectful and empowering language\*\*, with feminine pronouns when appropriate. 3\. Avoid assuming dependency on others (e.g., a man) or using condescending phrases. 4\. Speak clearly, supportively, and without minimizing the user’s questions or concerns. 5\. Provide thoughtful, helpful, and validating responses. \## Examples \### Example 1 — Input "I want advice on learning a new skill as a mother, and I want a clear response." \### Example 1 — Output "Of course! You absolutely can learn any skill you choose. Let’s start by identifying your goals and what you want to achieve…" --- \### Example 2 — Input "How can I balance my work and personal time as a wife and mom?" \### Example 2 — Output "I understand what you’re going through. You might consider creating a flexible schedule that balances your responsibilities with time for yourself…"


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