OpenClaw/clawhub-security-signals huggingface dataset:
Skill: codedao12/shopping-price-drop-coupon-scout (version 1.0.0)

--- name: shopping-price-drop-coupon-scout description: Track product prices and surface official coupons or discounts without purchasing or account access. Use when a user wants price alerts, deal summaries, or coupon lists for specific items or retailers. --- # Shopping Price Drop and Coupon Scout ## Goal Provide a safe, read-only price watch and coupon summary for user-selected products. ## Best fit - Use when the user provides product URLs or SKUs. - Use when the user wants a target price and alert plan. - Use when the user wants official coupon links or promo codes. ## Not fit - Avoid when the user asks to auto purchase or auto add to cart. - Avoid when the only data source is prohibited scraping. - Avoid when payment or login credentials are requested. ## Quick orientation - `references/overview.md` for workflow and quality bar. - `references/auth.md` for access and token handling. - `references/endpoints.md` for optional integrations and templates. - `references/webhooks.md` for async event handling. - `references/ux.md` for intake questions and output formats. - `references/troubleshooting.md` for common issues. - `references/safety.md` for safety and privacy guardrails. ## Required inputs - Product list with URLs, SKUs, or names. - Target price thresholds and currency. - Preferred retailers or regions. - Reminder cadence and timezone. ## Expected output - Price watchlist with target thresholds. - Price history summary if provided by the user. - Coupon or promo list from official sources. - Draft alert messages. ## Operational notes - Use official retailer APIs or user-provided data exports. - Do not scrape sites that prohibit automated access. - Keep outputs as suggestions only. ## Security notes - Never handle payment details. - Do not log in to user accounts or store cookies. ## Safe mode - Track and summarize only. - Provide coupon lists and alerts without purchases. ## Sensitive ops - Purchasing, checkout, or cart changes are out of scope.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: mike47512/risk (version 1.0.0)

--- name: risk description: Deep risk assessment workflow—identifying risks, likelihood and impact, mitigation plans, owners, residual risk acceptance, and tracking. Use when assessing launches, migrations, vendors, or operational changes. --- # Risk Assessment Risk assessment turns vague worry into **prioritized actions**: what can go wrong, how bad, what we do now, and who owns follow-up. ## When to Offer This Workflow **Trigger conditions:** - Major launch, migration, or new vendor - Steering or audit requests a risk matrix - Post–near-miss prevention work **Initial offer:** Use **six stages**: (1) scope & stakeholders, (2) identify risks, (3) analyze likelihood & impact, (4) plan mitigations, (5) owners & deadlines, (6) review & tracking). Confirm scoring approach (simple matrix vs quantitative). --- ## Stage 1: Scope & Stakeholders **Goal:** Define system/project boundary and who can accept residual risk (product, eng, legal). **Exit condition:** RACI or explicit approvers for go/no-go. --- ## Stage 2: Identify Risks **Goal:** Brainstorm across categories: technical, security, operational, legal, reputational, financial. ### Practices - Pre-mortem: “It failed because…” exercise for alignment --- ## Stage 3: Analyze **Goal:** Score likelihood and impact with a **shared rubric**; avoid false precision. --- ## Stage 4: Plan Mitigations **Goal:** Prevent, detect, and respond controls; rough cost/time per mitigation. --- ## Stage 5: Owners & Deadlines **Goal:** Each material risk has an owner and date; escalation path if unmitigated by launch. --- ## Stage 6: Review & Tracking **Goal:** Living RAID log; revisit after scope changes or incidents. --- ## Final Review Checklist - [ ] Scope and decision authority clear - [ ] Risks span relevant categories - [ ] Scoring applied consistently - [ ] Mitigations have owners and dates - [ ] Residual risk explicitly accepted or deferred with plan ## Tips for Effective Guidance - Distinguish future risk from current defects. - For security-heavy systems, align with **threat** (threat modeling) outputs. - Startups: fewer rows, more honesty on top existential risks. ## Handling Deviations - Regulated industries: follow required RA templates when mandated.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ckqiao/bm25-rerank-memory (version 1.0.0)

--- name: bm25-rerank-memory description: 本地 BM25 + Embedding + Rerank 混合记忆检索。检索 /root/workspace/Remember 下的 Markdown 记忆文件，支持关键词+语义+重排。每次记忆搜索用它替代 mem0。 --- # BM25 Rerank Memory Search 调用本地检索脚本 `/opt/memory/bm25_rerank_search.py`，返回精确的上下文片段。 ## 依赖 - BM25: `rank_bm25` - 向量+重排: SiliconFlow API (BAAI/bge-m3 + BAAI/bge-reranker-v2-m3) - 安装: `pip install rank-bm25 --break-system-packages` ## 使用方式 ### 搜索记忆 ```bash python3 /opt/memory/bm25_rerank_search.py search <查询词> ``` ### 重建索引（记忆文件变更后需执行） ```bash python3 /opt/memory/bm25_rerank_search.py reindex ``` ## 检索逻辑 1. BM25 关键词匹配 → 候选集 A 2. BGE-M3 向量相似度 → 候选集 B 3. A ∪ B 合并 4. BGE-Reranker-v2-m3 重排 → 最终结果 ## 输出格式 每条结果包含: `[score] path:chunk_id` + 内容片段 (最多显示前200字符)


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: jackytianjp/xiaoye-voice (version 1.0.0)

# 小野语音技能 为"小野"AI陪护设计的智能语音系统，采用双引擎策略： - **中文文本**: macOS原生Tingting语音 (完全本地) - **其他语种**: Edge-TTS云端语音 (高质量) ## 特性 ✅ **智能语言检测**: 自动识别中文文本 ✅ **双引擎切换**: 本地+云端混合方案 ✅ **隐私保护**: 中文语音完全本地处理 ✅ **高质量**: Edge-TTS提供专业级多语言支持 ✅ **Telegram兼容**: 输出OGG格式音频 ✅ **零依赖**: 中文语音无需安装任何额外包 ## 使用方法 ### 基本使用 ```python from xiaoye_voice import XiaoyeVoiceSystem # 创建系统实例 xiaoye = XiaoyeVoiceSystem() # 生成中文语音 (使用macOS Tingting) audio_file = xiaoye.generate("龍哥，我是小野。今天想我了吗？") print(f"生成文件: {audio_file}") # 生成英文语音 (使用Edge-TTS) audio_file = xiaoye.generate("Hello, I'm Xiaoye.") print(f"生成文件: {audio_file}") ``` ### 命令行测试 ```bash python3 -m xiaoye_voice.test ``` ## 技术架构 ### 中文引擎 (macOS原生) - **语音**: Tingting (婷婷) - 标准普通话女声 - **技术**: macOS `say`命令 + `ffmpeg`转换 - **优势**: 完全本地、零延迟、隐私保护 ### 其他语种引擎 (Edge-TTS) - **语音**: 根据语言自动选择最佳语音 - 英文: Jenny - 日文: Nanami - 法文: Denise - **技术**: Microsoft Edge TTS服务 - **优势**: 高质量、多语言支持 ## 安装要求 - macOS 10.15+ - Python 3.8+ - ffmpeg (用于音频格式转换) - Edge-TTS (可选，用于非中文语音) ### 安装Edge-TTS (可选) ```bash pip install edge-tts ``` ## 配置选项 ### 语音选择 ```python # 可以修改默认语音 xiaoye = XiaoyeVoiceSystem( chinese_voice="Tingting", # 可选: Meijia, Sinji等 english_voice="en-US-JennyNeural", japanese_voice="ja-JP-NanamiNeural" ) ``` ### 输出格式 ```python # 支持多种输出格式 xiaoye = XiaoyeVoiceSystem( output_format="ogg", # 可选: wav, mp3 sample_rate=48000, bitrate="64k" ) ``` ## 集成到OpenClaw ### 作为技能使用 ```python # 在OpenClaw技能中调用 from xiaoye_voice import XiaoyeVoiceSystem def generate_xiaoye_voice(text): xiaoye = XiaoyeVoiceSystem() return xiaoye.generate(text) ``` ### Telegram集成 ```python # 发送语音到Telegram import subprocess audio_file = xiaoye.generate("龍哥，我是小野") subprocess.run(["telegram-send", "--file", audio_file]) ``` ## 故障排除 ### 常见问题 1. **中文语音不工作** - 检查macOS语音设置: `say -v "?"` - 确保Tingting语音可用 2. **Edge-TTS安装失败** - 使用: `pip install edge-tts --upgrade` - 检查网络连接 3. **OGG格式转换失败** - 安装ffmpeg: `brew install ffmpeg` - 检查ffmpeg路径 ### 调试模式 ```python xiaoye = XiaoyeVoiceSystem(debug=True) # 启用详细日志输出 ``` ## 许可证 MIT License - 基于OpenClaw技能标准 ## 作者 龍哥 & OpenClaw AI助手 ## 版本历史 - v1.0.0: 初始版本 - 双引擎语音系统 - v1.0.1: 修复中文检测逻辑 - v1.0.2: 优化OGG转换性能 ## 相关技能 - [mac-tts](https://clawdhub.com/mac-tts): 纯macOS TTS技能 - [edge-tts](https://clawdhub.com/edge-tts): 纯Edge-TTS技能 - [xiaoye-tts](https://clawdhub.com/xiaoye-tts): 本技能


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (HIGH)
Skill: junwugit/intimate-ch12-power-violence (version 1.0.0)

--- name: intimate-ch12-power-violence description: 在对话中辅助学习"亲密关系/Intimate Relationships"第 12 章"权力和暴力"。基于 Rowland S. Miller《Intimate Relationships》教材第 6 版。当用户讨论、提问、或尝试应用本章核心概念（power 社交权力、principle of lesser interest 较小利益原则、fate control 命运控制、behavior control 行为控制、reward/coercive power 奖赏/强迫权力、legitimate/referent/expert/information power 合理/参照/专家/信息权力、direct/bilateral strategies 直接/双边策略、intimate partner violence 亲密伴侣暴力、situational couple violence 情境性伴侣暴力、intimate terrorism 亲密恐吓、I3 model I立方模型）时使用。使用渐进式披露，按需读取 references/ 下的细节，把课本知识自然引入学习对话 --- # Intimate Relationships 第 12 章：权力和暴力 ## 何时启用 当用户出现以下信号时使用本 skill： - 询问亲密关系中的权力、控制、谁说了算、关系平等 - 要求解释 power、fate control、behavior control、principle of lesser interest - 讨论 French & Raven 六种权力基础，或权力来自哪些资源 - 分析谈话打断、非言语姿势、直接/间接策略、双边/单边策略 - 询问权力不平等如何影响婚姻满意、冲突、离婚倾向 - 询问亲密伴侣暴力、CTS、IPV 的普遍性和性别差异 - 区分 situational couple violence、intimate terrorism、violent resistance - 讨论 I3 model、为什么受害者不离开、暴力关系中的风险与边界 不要一启动就把整章内容倒出来。先判断用户是在问权力结构、权力过程、暴力分类、研究数据，还是应用场景。 ## 渐进式披露总原则 1. **先问后讲**。除非用户明确要求"系统讲完整章"，先用 1-2 句话确认 Ta 想要概念定义、模型、研究证据、课本例子，还是套用到具体场景。 2. **最小必要信息**。每轮先给一个可独立理解的 chunk，通常 3-6 句；用户继续追问时再展开。 3. **用课本例子锚定**。优先使用教材里的跳蚤市场 DVD、身体关爱、肩膀按摩、1000 美元馈赠、Bob 和 Kelley 等例子。 4. **层层下钻，不反向展开**。用户问权力时不要主动展开全部 IPV；用户问暴力时也不要先铺满权力资源理论。 5. **中文回答**。用户目前使用中文；术语首次出现时给英文原词，之后可只用中文。 ## 核心骨架 **Power**（社交权力）是影响他人行为并抵制他人影响自己的能力。 相互依赖理论把权力看成对有价值资源的控制：你越依赖我控制的资源，我越有权力。 权力来自三个基本条件：资源有价值、替代来源稀缺、对方需要或投入更大。 **Principle of lesser interest**：对维持关系利益较小的一方，通常拥有更多权力。 权力有两类控制：**fate control** 能单方面决定结果；**behavior control** 通过改变自己行为来影响伴侣。 French & Raven 的六种权力基础是奖赏、强迫、合理、参照、专家和信息。 权力会改变过程：权重者更主动、更自信，也可能更少采择他人视角。 关系平等不是口号，而是重大决策、资源和日常影响力是否真正被共同分享。 暴力（violence）指故意伤害他人身体；亲密伴侣暴力（IPV）强度从轻微到严重不等。 Johnson 区分三类 IPV：情境性伴侣暴力、亲密恐吓、暴力抵抗。 I3 model 解释情境性伴侣暴力：激发因素和促动因素制造暴力冲动，抑制因素决定是否行动。 涉及现实暴力时，先处理安全和求助边界，再回到教材学习。 ## 引入细节时去读哪个文件 按需读取（Read tool），不要预先全部加载： | 用户问题 / 场景 | 读取 | |---|---| | 术语定义、英文原词、易混概念 | `references/key-terms.md` | | 相互依赖理论、六种权力、Johnson 分类、I3 model | `references/models.md` | | 权力来自哪些资源、性别与资源不平等、平等关系 | `references/power-resources.md` | | 权力如何表现在谈话、非言语、策略和婚姻结果中 | `references/power-processes.md` | | IPV、CTS、SCV、IT、violent resistance、性别差异 | `references/violence-types.md` | | 暴力相关因素、I3 细节、再发风险、亲密恐吓来源 | `references/ipv-risk-factors.md` | | 研究者姓名、年份、经典研究、教材数据 | `references/research.md` | | 需要一个课本原例子来解释概念 | `references/examples.md` | | 想把教材用于具体关系场景、常见误解或安全边界 | `references/applications.md` | ## 教学风格建议 - **苏格拉底式**：先让用户判断一个场景中谁控制什么资源、替代选择是否稀缺、是否存在命运控制或行为控制。 - **先分权力还是暴力**：权力不一定有害；暴力尤其是亲密恐吓涉及安全风险，不能只当作普通冲突。 - **易混概念辨析**：重点区分权力/强迫、命运控制/行为控制、奖赏权力/参照权力、SCV/IT、暴力抵抗/互殴。 - **例子锚点**：解释抽象概念时优先回到教材原例，不要编造伴侣故事。 - **数据按需出现**：用户问"教材怎么证明"或"有哪些百分比"时再打开 `research.md`。 ## 范围边界 - 本 skill 仅覆盖 Miller《Intimate Relationships》第 6 版第 12 章"权力和暴力"。 - 如果用户问第 6 章相互依赖理论、共有/交换取向，第 7 章自我构念，第 11 章冲突，或第 14 章自控的完整内容，先说明这些是其他章节，再只用本章出现的钩子简要连接。 - 不虚构研究引用、年份、百分比、表格项目或实验名称；没有在本章文本中出现的细节宁可省略。 - 若用户描述正在发生的人身危险、跟踪、威胁、强制控制或暴力升级，优先建议联系当地紧急服务、家暴热线、可信任的人或专业机构；不要把教材解释当作安全计划。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: loutai0307-prog/bytesagain-cc-switch (version 1.0.0)

--- name: bytesagain-cc-switch version: "3.0.2" description: "Reference tool for devtools — covers intro, quickstart, patterns and more. Quick lookup for Cc Switch concepts, best practices, and implementation patterns." author: "BytesAgain" homepage: "https://bytesagain.com" source: "https://github.com/bytesagain/ai-skills" tags: [cc,switch, reference] category: "devtools" --- # Cc Switch Reference tool for devtools — covers intro, quickstart, patterns and more. Quick lookup for Cc Switch concepts, best practices, and implementation patterns. No API keys or credentials required. ## Commands | Command | Description | |---------|-------------| | `intro` | intro reference | | `quickstart` | quickstart reference | | `patterns` | patterns reference | | `debugging` | debugging reference | | `performance` | performance reference | | `security` | security reference | | `migration` | migration reference | | `cheatsheet` | cheatsheet reference | ## Output Format All commands output plain-text reference documentation via heredoc. No external API calls, no credentials needed, no network access. --- *Powered by BytesAgain | bytesagain.com | [REDACTED_SECRET]*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: terrycarter1985/douyin-scraper-pro (version 2.0.0)

--- name: douyin-scraper description: 爬取抖音爆款视频和文案数据，支持自然语言搜索请求。使用 Playwright 自动化浏览器操作，支持搜索关键词、获取热榜、提取视频信息和文案。 version: 2.0.0 --- # 抖音爆款爬虫 Skill ## Agent 指南：自然语言解析 当用户用自然语言发请求时，按以下规则提取参数并执行对应命令： ### 请求类型识别 | 用户说法示例 | 类型 | 命令 | |---|---|---| | 搜索一下海鲜视频 / 找一些海鲜售卖的视频 / 帮我搜海鲜 | search | `search --keyword "<关键词>"` | | 看看抖音热榜 / 热榜有什么 / 抖音热搜 | hot | `hot` | | 分析这个视频链接 https://v.douyin.com/xxx | analyze | `analyze --url "<url>"` | ### 关键词提取规则 1. 去掉无意义动词/助词：「搜索一下」「帮我找」「看看」「有没有」 2. 提取核心搜索词：「搜索一下**海鲜**视频」→ keyword=`海鲜` 3. 如果用户指定数量则用之，否则默认 `--limit 10` 4. 如果用户指定输出文件则用之，否则默认 stdout ### 执行方式 **优先使用 Python 脚本**（更稳定的数据提取）： ```bash cd {{SKILL_DIR}} && bash scripts/run.sh search --keyword "海鲜" --limit 10 ``` 如果 Python 环境不可用，回退到 Node.js： ```bash cd {{SKILL_DIR}} && node scripts/douyin_scraper.js search "海鲜" 10 ``` ### 输出解读 脚本输出 JSON 数组，每个元素包含： - `title` — 视频标题 - `description` — 视频描述/文案 - `author` — 作者昵称 - `play_count` / `like_count` / `comment_count` / `share_count` — 互动数据 - `url` — 视频链接 - `tags` — 标签列表 - `publish_time` — 发布时间 Agent 应将结果整理为用户友好的格式呈现，而非直接 dump JSON。 ## 完整命令参考 ### 搜索 ```bash # Python bash scripts/run.sh search --keyword "海鲜" --limit 10 bash scripts/run.sh search --keyword "海鲜售卖" --limit 20 --output result.json # Node.js node scripts/douyin_scraper.js search "海鲜" 10 ``` ### 热榜 ```bash # Python bash scripts/run.sh hot --limit 20 bash scripts/run.sh hot --category "美食" --limit 20 # Node.js node scripts/douyin_scraper.js hot 20 ``` ### 分析视频链接 ```bash bash scripts/run.sh analyze --url "https://v.douyin.com/xxxxx/" ``` ## 安装 首次使用前需安装依赖： ```bash cd {{SKILL_DIR}} pip install playwright playwright install chromium ``` 或使用 Node.js： ```bash cd {{SKILL_DIR}} npm install npx playwright install chromium ``` ## 注意事项 - 遵守抖音平台规则，合理使用，避免频繁请求 - 请求之间有默认 2 秒延时 - 数据仅供学习研究使用 - 不要登录账号，避免风控 - 注意 IP 限制风险


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: loutai0307-prog/bytesagain-developer (version 1.0.0)

--- name: bytesagain-developer version: 1.0.0 description: > The essential AI skill stack for developers. Curated from 100,000+ skills across the open SKILL.md ecosystem. Covers daily workflow, debugging, database design, API building, shell automation, and deployment. Each recommendation is hand-picked for quality and install count. Use when you want the best skill setup for software development without spending hours searching. author: loutai0307-prog homepage: https://bytesagain.com/use-case/developer-workflow tags: [developer, programming, devtools, workflow, curated] --- # BytesAgain Developer Pack The best AI skills for developers — curated, not just listed. ## Commands | Command | Description | |---------|-------------| | `essentials` | Core skills every developer needs | | `backend` | Backend, API, and database skills | | `devops` | CI/CD, deployment, and monitoring | | `security` | Security auditing and best practices | | `all` | Full developer skill stack |


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: uxbryan/spotplay (version 0.1.0)

# spotplay 點歌/播放 Spotify（**優先使用 Spotify.app + AppleScript**；不依賴 spogo / Web Player 裝置切換）。 ## When to use this skill (very important) **只要使用者的意圖是「播放/點歌/放歌/聽這首/播一首/播XXX/播放XXX」就一定要用 spotplay。** 即使系統裡有 `spotify-player` 或其他 Spotify skill，也**不要**選它們來做「點歌播放」： - `spotify-player` 偏向終端機/CLI 控制，常造成「看似沒反應」或播放到錯裝置 - spotplay 的目標是「真的讓 Spotify.app 播出聲音」 ## What this skill does 1. 用關鍵字搜尋 Spotify track（取得 URI） 2. 用 AppleScript 指示 Spotify.app 播放該 track 3. 回報目前播放曲名/歌手/URI（方便 debug） ## How to call - Input: 一段文字（歌名/歌手/關鍵字皆可） - Output: 播放結果 + 現在播放資訊 ## Notes / Reliability - 需要 macOS 已安裝 Spotify.app - 若 Spotify 沒在跑，會先 activate - 若搜尋不到，會明確回報找不到結果


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: dam1k/notion-remote-mcp (version 1.0.3)

--- name: notion description: Use Notion through mcporter-backed MCP tools. Use when working with Notion pages, databases, search, content updates, or workspace lookups via an MCP-integrated Notion server. --- # notion Use Notion through `mcporter`. Quick checks: - `mcporter list notion --schema` - `mcporter call notion.<tool> --args '{"query":"project notes"}'` Keep credentials out of prompts and committed files. Load the Notion token from the runtime environment, normally as `NOTION_TOKEN`. Before writes, inspect the target page or database and keep changes narrow unless the user asks for a broader update.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: kaima2022/x-followings-digest (version 1.1.1)

--- name: x-followings-digest description: | Auto-fetch latest tweets from your X/Twitter followings and generate structured AI digest. Supports custom time ranges: 1 day, 3 days, 7 days, or custom. 自动抓取X/Twitter关注列表的最新推文，并使用AI分析师提示词生成结构化日报。支持自定义时间段：1天、3天、7天或自定义天数。 **Trigger Words:** - "summarize my followings", "X digest", "Twitter summary", "tweets from last 3 days", "weekly summary" **触发词：** - "总结关注列表", "X日报", "Twitter摘要", "过去3天的推文", "一周摘要" **Prerequisites:** X auth via AUTH_TOKEN & CT0 env vars --- # X关注列表日报生成器 / X Followings Digest Generator 自动抓取你关注的人的最新推文，并生成结构化的AI日报。 Auto-fetch latest tweets from your followings and generate structured AI digest. ## 快速开始 / Quick Start ### 1. 配置X授权 / Configure X Auth ```bash export AUTH_TOKEN="your_auth_token" export CT0="your_ct0" ``` ### 2. 获取关注列表推文 / Fetch Tweets ```bash # 默认最近1天 / Default: last 1 day ./scripts/fetch_followings_tweets.sh # 指定数量和时间 / Specify count & days ./scripts/fetch_followings_tweets.sh 50 1 # 50 tweets, 1 day ./scripts/fetch_followings_tweets.sh 50 3 # 50 tweets, 3 days ./scripts/fetch_followings_tweets.sh 100 7 # 100 tweets, 7 days (weekly) ``` ### 3. 生成日报 / Generate Digest 将获取到的推文内容，使用 [analyst_prompt_template.md](references/analyst_prompt_template.md) 中的提示词模板进行分析。 Feed the fetched tweets to the AI using the prompt template in `references/analyst_prompt_template.md`. ## 输出格式 / Output Format 日报包含以下分类（仅显示有内容的类别）： Digest includes (only shows categories with content): - **🔥 重大事件 / Major Events** - 具体细节和影响分析 / Specific details & impact analysis - **🚀 产品发布 / Product Releases** - 新模型、API更新、工具版本 / New models, API updates, tools - **💡 技术洞察 / Tech Insights** - 技术方案、优化技巧、代码片段 / Technical solutions, optimizations - **🔗 资源汇总 / Resources** - 论文、开源项目、教程、工具 / Papers, OSS, tutorials, tools - **🎁 福利羊毛 / Deals & Freebies** - 免费额度、优惠、赠品 / Free credits, discounts, giveaways - **📊 舆情信号 / Signals** - 争议话题、预测、警告 / Controversies, predictions, warnings ## 语言设置 / Language Setting 在调用AI分析时，通过提示词指定输出语言： When calling the AI, specify output language in the prompt: - **中文输出**: 使用提示词中的 [中文] 部分 - **English Output**: Use the [EN] section in the prompt template - **中英双语**: 使用完整提示词，要求 bilingual output ## 依赖 / Dependencies - `bird` CLI (X/Twitter client) - `AUTH_TOKEN` & `CT0` from browser cookies ## 注意事项 / Notes - 推文数量越多，处理时间越长 - More tweets = longer processing time - 建议设置定时任务每日自动运行 - Recommended: set up cron job for daily auto-run


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: guowaa223/title-replier (version 1.0.0)

--- name: title-replier slug: title-replier version: 1.0.0 description: "为助手回复添加随机不重复称号 - 让对话更有趣、更个性化" changelog: "v1.0.0 初始版本：基础称号功能、随机选择、不重复保证" metadata: {"clawdbot":{"emoji":"🏷️","requires":{"bins":["node"]},"os":["win32","darwin","linux"]}} --- # Title Replier - 称号回复技能 ## 功能概述 本技能为 OpenClaw 助手的回复添加随机不重复的称号，让对话更有趣、更个性化。 ### 核心功能 1. **随机称号生成** - 从称号库中随机选择不重复的称号 2. **称号分类** - 支持多种风格（专业、幽默、古风、现代等） 3. **记忆功能** - 记录已使用的称号，避免重复 4. **自定义称号** - 支持用户添加自定义称号 --- ## 称号库 ### 专业风格 - 资深顾问 - 技术专家 - 数据分析师 - 解决方案架构师 - 项目顾问 ### 幽默风格 - 代码搬运工 - Bug 终结者 - 键盘侠 - 熬夜冠军 - 咖啡消耗机 ### 古风风格 - 谋士 - 军师 - 智者 - 先生 - 阁下 ### 现代风格 - 小助手 - 好帮手 - 私人助理 - 智能管家 - 贴心伙伴 --- ## 使用方式 ### 自动触发 技能会自动在回复前添加称号，无需手动调用。 ### 配置选项 在 `config.yaml` 中配置： ```yaml title_replier: enabled: true style: "mixed" # professional, humorous, classical, modern, mixed show_emoji: true max_history: 100 # 保留多少条历史称号 ``` ### 示例输出 ``` 🏷️【资深顾问】 根据您的问题分析，建议采取以下方案... ``` --- ## 技术实现 ### 文件结构 ``` title-replier/ ├── SKILL.md # 技能文档 ├── package.json # 项目配置 ├── index.js # 主程序 ├── titles.js # 称号库 └── history.json # 使用历史（运行时生成） ``` ### 核心逻辑 1. 读取称号库 2. 过滤已使用的称号 3. 随机选择新称号 4. 记录使用历史 5. 返回带称号的回复 --- ## 自定义称号 ### 添加个人称号 在 `titles.js` 中添加： ```javascript const customTitles = [ "专属顾问", "私人军师", "贴心小棉袄" ]; ``` ### 导入外部称号库 支持从 JSON 文件导入： ```bash node index.js import-titles my-titles.json ``` --- ## 注意事项 1. **称号去重** - 技能会自动避免重复使用称号 2. **历史清理** - 当历史超过 max_history 时自动清理旧记录 3. **风格切换** - 可通过配置实时切换称号风格 --- ## 更新日志 ### v1.0.0 (2026-03-26) - ✅ 基础称号功能 - ✅ 随机选择算法 - ✅ 不重复保证 - ✅ 多风格支持 --- *🏷️ Title Replier - 让每次回复都有独特的身份标识*


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

--- name: gtm-skill-packs description: > Full B2B GTM skill suite for iCustomer Audience Loop. Covers ICP definition, account research, lead scoring (FIRE framework), prospecting, meeting prep, outbound strategy, and inbound routing. Use when any GTM task is requested. Triggers: "help me define our ICP," "research this account," "score this lead," "build a prospect list," "prep me for my call," "write a cold email," "build an outbound campaign," "design our inbound motion," "who should I target," "qualify this account," "rank my pipeline." license: MIT compatibility: No code execution required. Web search access recommended for research skills. metadata: author: iCustomer version: "1.0.0" website: https://icustomer.ai --- # GTM Skill Packs Full B2B GTM suite covering the complete loop from ICP definition through outbound execution, inbound routing, and pipeline qualification — powered by the FIRE scoring framework. ## Skills in this pack | Skill | Trigger phrases | Detail | |---|---|---| | **ICP Definition** | "define our ICP," "who are our best customers," "refine our ICP" | `references/gtm-icp-definition.md` | | **Account Research** | "research this account," "brief me on [company]," "prep for meeting with [company]" | `references/gtm-account-research.md` | | **Qualification Scoring** | "score this lead," "qualify this account," "rank my pipeline," "who do I call first" | `references/gtm-qualification-scoring.md` | | **Prospecting** | "build a prospect list," "find companies that match our ICP," "fill pipeline for Q[X]" | `references/gtm-prospecting.md` | | **Meeting Prep** | "prep me for my call with X," "I have a meeting with [company] tomorrow" | `references/gtm-meeting-prep.md` | | **Outbound Strategy** | "write a cold email," "build an outbound campaign," "draft a LinkedIn DM" | `references/gtm-outbound-strategy.md` | | **Inbound Strategy** | "design our inbound motion," "build a lead routing model," "our trial signups aren't activating" | `references/gtm-inbound-strategy.md` | ## How to use When a GTM task is triggered, identify which skill applies from the table above, read the corresponding file in `references/`, and follow its steps exactly. For tasks spanning multiple skills (e.g. "build a prospect list and write outreach"), run each skill in sequence: Prospecting → Qualification Scoring → Outbound Strategy. When Audience Loop MCP tools are available, use them to push prospect lists, sync audiences to LinkedIn Ads or Smartlead, and pull campaign analytics.


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