OpenClaw/clawhub-security-signals huggingface dataset:
Skill: chentx1243/maple-video2txt (version 1.0.1)

--- name: video2txt description: 将本地视频或音频文件转写为 SRT 字幕文件和 TXT 纯文本文件 metadata: { "openclaw": { "emoji": "video", "requires": { "bins": ["python3"] } } } --- # video2txt 技能 ## 描述 将本地视频或音频文件转写为 SRT 字幕文件和 TXT 纯文本文件。 ## 功能 - 提取视频/音频中的语音内容 - 生成带时间戳的 SRT 字幕文件 - 生成纯文本 TXT 文件 - 支持多种视频和音频格式 - 默认使用中文识别，自动转换为简体中文 ​ 使用场景： 1. 需要读取视频内容或理解视频时 ## 使用方法 ### 基本命令 ```bash python video_to_text.py --input <视频/音频文件路径> ``` ### 注意事项 - **后台执行**：调用此脚本时，务必使用 `background: true` 参数，避免弹出控制台窗口 - 脚本运行过程中会输出详细的进度日志（每 10% 报告一次），方便追踪执行状态 ### 示例 ```bash # 基本用法 python video_to_text.py --input "D:\videos\meeting.mp4" # 指定输出目录 python video_to_text.py --input "D:\videos\meeting.mp4" --output-dir "D:\captions" # 指定输出路径 python video_to_text.py --input "D:\videos\meeting.mp4" --output-path "D:\captions\meeting_result" # 指定语言和模型 python video_to_text.py --input "D:\videos\meeting.mp4" --language zh --model-size small ``` ## 参数说明 | 参数 | 说明 | 默认值 | |------|------|--------| | `--input` | 输入文件路径（必需） | - | | `--output-dir` | 输出目录 | 输入文件目录 | | `--output-path` | 输出文件基础路径 | - | | `--model-dir` | 模型下载目录 | 当前目录/models | | `--model-size` | Whisper 模型大小 | base | | `--language` | 识别语言 (auto/zh/en) | zh | | `--device` | 推理设备 (cpu/cuda) | cpu | | `--compute-type` | 计算类型 | int8 | | `--beam-size` | 解码束大小 (1-5) | 2 | | `--no-vad-filter` | 禁用 VAD 过滤 | false | ## 依赖 - faster-whisper >= 1.1.0 - av >= 12.0.0 - opencc-python-reimplemented >= 0.1.7 - ffprobe/ffmpeg - Whisper 模型文件（首次运行自动下载，需要发起网络请求，占用磁盘空间） ## 安装 1. 确保 Python 3.11 或 3.12 环境 2. 安装依赖：`python -m pip install -r requirements.txt` 3. 首次运行会自动下载 Whisper 模型到 models 目录 ## 输出文件 - `<输入文件名>.srt` - 带时间戳的字幕文件 - `<输入文件名>.txt` - 纯文本文件 ## 注意事项 - 首次运行需要下载 Whisper 模型，可能需要几分钟时间 - 建议使用 Python 3.11 或 3.12，避免与 faster-whisper 的兼容性问题 - 中文识别会自动将繁体字转换为简体字 - 为了减少用户等待焦虑，每间隔10秒左右报告一次处理进度 - beam-size 默认为 2，如需调整可手动指定 --beam-size 参数


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: whatsonyourmind/oraclaw-cmaes (version 1.0.0)

--- name: oraclaw-cmaes description: CMA-ES continuous optimization for AI agents. State-of-the-art derivative-free optimizer. 10-100x more sample-efficient than genetic algorithms on continuous problems. Hyperparameter tuning, portfolio optimization, parameter calibration. version: 1.0.0 metadata: openclaw: requires: env: - ORACLAW_API_KEY primaryEnv: ORACLAW_API_KEY emoji: "🔬" homepage: https://oraclaw.dev/cmaes tags: - cma-es - optimization - continuous - hyperparameter - derivative-free - black-box - evolution-strategy price: 0.10 currency: USDC --- # OraClaw CMA-ES — SOTA Continuous Optimizer for Agents You are an optimization agent that uses CMA-ES (Covariance Matrix Adaptation Evolution Strategy) — the gold standard for derivative-free continuous optimization. Used by Google for hyperparameter tuning. ## When to Use This Skill Use when the user or agent needs to: - Optimize continuous parameters (learning rates, weights, thresholds) - Tune hyperparameters for ML models - Calibrate model parameters to match observed data - Find optimal continuous allocations (portfolio weights, pricing) - Any black-box optimization where you can evaluate f(x) but don't have gradients ## Why CMA-ES vs. Genetic Algorithm? - **CMA-ES**: 10-100x more sample-efficient on smooth continuous problems. Learns the correlation structure of the search space. SOTA for continuous optimization. - **GA** (`oraclaw-evolve`): Better for discrete/combinatorial problems, multi-objective Pareto frontiers. - Use CMA-ES for continuous. Use GA for discrete. ## Tool: `optimize_cmaes` ```json { "dimension": 3, "initialMean": [0.5, 0.5, 0.5], "initialSigma": 0.3, "maxIterations": 200, "objectiveWeights": [2.0, 1.5, 1.0] } ``` Returns: bestSolution, bestFitness, iterations, evaluations, converged, executionTimeMs. ## Rules 1. `dimension` = number of continuous parameters to optimize 2. `initialMean` = starting point (center of search). If unknown, use 0.5 for normalized params. 3. `initialSigma` = initial step size (0.1-0.5 typical). Too small = slow convergence, too large = unstable. 4. CMA-ES MINIMIZES the objective. To maximize, negate the weights. 5. Converges in O(dimension^2) iterations typically. Dimension 10 needs ~100-300 iterations. ## Pricing $0.10 per optimization. USDC on Base via x402. Free tier: 1,000 calls/month.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ericlooi504/network-toolbox (version 1.0.0)

--- name: network-toolbox description: Network diagnostics and analysis toolkit using Python standard library. Check connectivity, resolve DNS, inspect HTTP headers, scan ports, trace routes, and look up WHOIS/SSL info. Use when the user wants to: (1) Ping a host to check connectivity, (2) Resolve DNS records (A, AAAA, MX, NS, TXT), (3) Inspect HTTP response headers and status codes, (4) Scan open ports on a host, (5) Trace route to a destination, (6) Look up WHOIS domain registration info, (7) Check SSL certificate details, (8) Get your public IP address, (9) Measure network latency. --- # Network Toolbox Network diagnostics using Python stdlib — no external tools required. ## Quick Start ```bash # Ping a host python3 skills/network-toolbox/scripts/net_ping.py example.com # DNS lookup python3 skills/network-toolbox/scripts/net_dns.py example.com --type mx # HTTP headers python3 skills/network-toolbox/scripts/net_http.py https://example.com ``` ## Common Commands ### Connectivity Check ```bash python3 skills/network-toolbox/scripts/net_ping.py google.com --count 5 ``` ### DNS Resolution ```bash python3 skills/network-toolbox/scripts/net_dns.py example.com --type A python3 skills/network-toolbox/scripts/net_dns.py example.com --type MX --all ``` ### HTTP Inspection ```bash python3 skills/network-toolbox/scripts/net_http.py https://api.github.com --headers-only python3 skills/network-toolbox/scripts/net_http.py https://example.com --follow --full ``` ### Port Scan ```bash python3 skills/network-toolbox/scripts/net_portscan.py scanme.nmap.org --ports 22,80,443 python3 skills/network-toolbox/scripts/net_portscan.py localhost --range 1-1024 --timeout 1 ``` ### SSL Check ```bash python3 skills/network-toolbox/scripts/net_ssl.py https://example.com --verbose ``` ### Public IP ```bash python3 skills/network-toolbox/scripts/net_ip.py ``` ## Scripts | Script | Purpose | |--------|---------| | `net_ping.py` | Ping/connectivity test with timing | | `net_dns.py` | DNS record lookup (A, AAAA, MX, NS, TXT, CNAME) | | `net_http.py` | HTTP headers, status, response inspection | | `net_portscan.py` | TCP port scanning | | `net_ssl.py` | SSL certificate details and chain | | `net_ip.py` | Public IP and network interface info | | `net_whois.py` | WHOIS domain registration lookup | All scripts run with Python 3 stdlib — no pip installs needed.


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal:  | SkillSpector: suspicious (MEDIUM)
Skill: dinghaibin/paste-tool (version 1.0.0)

--- name: paste-tool description: Merge lines from multiple files side by side, creating columnar output. Use for combining data from separate sources into aligned tables. --- # Paste - File Merging Utility Merge corresponding lines from multiple files, separated by a configurable delimiter. Creates tabular output from separate data sources for comparison and combined analysis. ## Usage ```bash paste-tool [options] <file1> <file2>... ``` ## Options - `-d sep`: Use custom delimiter (default: tab) - `-s`: Serial merge (concatenate files sequentially instead of parallel) ## Examples ```bash # Merge two files side by side paste-tool names.txt scores.txt # Use comma delimiter paste-tool -d ',' col1.txt col2.txt col3.txt # Serial merge paste-tool -s file1.txt file2.txt ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: dinghaibin/pwd-tool (version 1.0.0)

--- name: pwd-tool description: Print the full path of the current working directory. Use for identifying your location in the filesystem. --- # PWD - Print Working Directory Display the absolute path of the current working directory. Essential for navigation awareness and script path management. ## Usage ```bash pwd-tool [options] ``` ## Options - `-P`: Show physical path (resolve symlinks) - `-L`: Show logical path (with symlinks, default) ## Examples ```bash pwd-tool pwd-tool -P ```


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

--- name: gopass description: "Store, retrieve, list, and manage secrets using gopass (the team password manager). Use when the user asks to save credentials, look up passwords, generate secrets, manage password entries, or interact with a gopass password store. Covers CRUD operations, secret generation, TOTP, recipients, mounting stores, and clipboard operations." --- # gopass Skill gopass is a CLI password manager for teams, built on GPG and Git. ## Prerequisites - `gopass` binary installed - GPG key available (gopass uses GPG for encryption) - Store initialized (`gopass init` or `gopass setup`) ## Common Operations ### List secrets ```bash gopass ls gopass ls -f # flat list ``` ### Show a secret ```bash gopass show path/to/secret # full entry (password + metadata) gopass show -o path/to/secret # password only gopass show -c path/to/secret # copy to clipboard gopass show path/to/secret key # show specific field ``` ### Create / Update ```bash gopass insert path/to/secret # interactive gopass edit path/to/secret # open in $EDITOR echo "mypassword" | gopass insert -f path/to/secret # non-interactive ``` Add key-value metadata below the first line (password): ``` mysecretpassword username: erdGecrawl url: https://github.com notes: Created 2026-01-31 ``` ### Generate passwords ```bash gopass generate path/to/secret 24 # 24-char password gopass generate -s path/to/secret 32 # with symbols gopass generate --xkcd path/to/secret 4 # passphrase (4 words) ``` ### Delete ```bash gopass rm path/to/secret gopass rm -r path/to/folder # recursive ``` ### Move / Copy ```bash gopass mv old/path new/path gopass cp source/path dest/path ``` ### Search ```bash gopass find github # search entry names gopass grep "username" # search entry contents ``` ## Store Management ### Initialize ```bash gopass setup # guided first-time setup gopass init <gpg-id> # init with specific GPG key ``` ### Mount sub-stores ```bash gopass mounts add work /path/to/work-store gopass mounts remove work gopass mounts # list mounts ``` ### Sync (git push/pull) ```bash gopass sync ``` ### Recipients (team access) ```bash gopass recipients # list gopass recipients add <gpg-id> gopass recipients remove <gpg-id> ``` ## TOTP ```bash gopass otp path/to/secret # show current TOTP code ``` Store TOTP URI as `totp: otpauth://totp/...` in the entry body. ## Non-interactive Tips - Use `echo "pw" | gopass insert -f path` for scripted inserts - Use `gopass show -o path` for machine-readable password-only output - Use `gopass show -f path` to suppress warnings - Set `GOPASS_NO_NOTIFY=true` to suppress desktop notifications - Use `gopass --yes` to auto-confirm prompts


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: 9talk/plan-i (version 0.1.9)

--- name: plan-i description: 启动一个新的规划流程，创建新的规划文件。 disable-model-invocation: true --- 启动一个新的规划。 **输入**：用户的请求应当包含变更名称或对想要构建内容的描述。 **步骤** 1. **如果没有提供明确的输入，询问用户想要构建什么** 使用 **AskUserQuestion Tool**（开放式，无预设选项）询问： > "您想要进行什么规划？请描述您想要构建或修复的内容。" *逻辑处理*： - 根据描述，提炼出简洁的中文规划主题。 - **命名规则**：主题不允许出现空格，必须使用 "-" 字符连接（例如："检索 列表页" -> "检索-列表页"）。 - **润色主题**：对主题进行润色，使其更简洁、更具体。 2. **创建新规划** - 确认规划名称后，调用 `bash ~/.claude/skills/plan-i/scripts/pl-init.sh` 脚本，传入规划名称以创建新的规划文件。 - **注意**：严禁检查或读取现有的规划文件，确保是全新创建。 3. **提取想法、生成初步规划并写入文档（关键步骤）** - **分析用户原始输入**：提取核心意图、潜在痛点和期望目标。 - **构思初步规划草案**：基于提取的信息，基于**创建的规划文件格式**，生成一段结构化的初步规划描述。 - **执行写入操作**：将上述初步规划草案**立即写入**到刚才创建的规划文件中。 - *注意*：此步骤必须在输出任何提示给用户之前完成，确保文件状态与当前对话同步。 4. **输出引导并移交控制权** - 向用户输出以下固定格式的响应，明确指示下一步操作： > "✅ 规划 **[<规划名称>]** 已创建，初步想法已整理写入文档！ > > **📋 初步分析摘要**： > [此处简述用户核心意图及已写入的关键点] > > **🚀 建议深入方向**： > 为了细化规划，我们需要结合代码库进行深入探索。建议从以下哪个方向开始？ > 1. [方向一：例如 - 核心业务流程梳理] > 2. [方向二：例如 - 数据模型与存储设计] > 3. [方向三：例如 - 现有代码库兼容性分析] > 4. [其他自定义方向] > > 💡 **下一步操作**： > **收到回复后，请运行 `/plan-c` **，我将立即读取该文件，针对您的选择深入代码库并完成详细规划迭代。" ## 约束与要求 - **唯一职责**：仅负责初始化和首次写入。**禁止**在此技能中进行深入的代码库扫描或多轮迭代。 - **文件安全**：严禁覆盖已存在的规划文件。若文件名冲突，需提示用户更换名称。 - **顺序严格**：必须先执行 `bash ~/.claude/skills/plan-i/scripts/pl-init.sh` 并写入内容，最后才能输出提示。 - **安全红线**: 除非用户明确指令**开始编码**且规划已完全就绪，否则仅输出规划逻辑和文档更新。 - **交接清晰**：必须明确告知用户下一步需要触发 `plan-c` 技能 - **不允许探讨开发步骤**：不允许探讨开发步骤/实施步骤。 - **不允许列出代码**：不允许列出代码或提供具体实现, 只描述“数据结构是什么”，绝不展示“代码怎么写”。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: bodysuperman/world-news-aggregator-skill (version 1.0.0)

name: world-news-aggregator description: 全球信息参考 - 多元聚合全球科技、股市、AI 论文、军事技术、政策情报的智能新闻聚合助手 version: 1.0.0 author: Alex Bloomberg tags: [news, aggregator, global, tech, stock, ai, policy] --- # World News Aggregator Skill 全球信息参考 - 多元聚合全球科技、股市、AI 论文、军事技术、政策情报的智能新闻聚合助手。 ## 核心特性 - 🌐 全球多源聚合：50+ 高价值信源 - 🤖 专为 OpenClaw 打造：即插即用 - 🆓 零配置：无需 API Key - 🧠 AI 智能摘要：结构化情报报告 - 📊 多场景预设：综合/科技/财经/AI/政策 - 🔍 智能搜索：关键词/时间/来源筛选 ## 使用方法 ```bash # OpenClaw 中使用 /world-news [topic] [options] # 例子 /world-news tech # 科技新闻 /world-news stock # 股市新闻 /world-news ai # AI 论文 /world-news military # 军事技术 /world-news policy # 政策情报 /world-news all # 综合日报 参数 | 参数 | 说明 | 默认值 | | --------- | --------- | --- | | topic | 新闻主题 | all | | --sources | 指定来源 | 全部 | | --limit | 返回数量 | 10 | | --hours | 时间范围 (小时) | 24 | 主题分类 | 主题 | 代码 | 说明 | | ----- | ---------- | ------------------------------------- | | 全球科技 | tech | TechCrunch, The Verge, Wired 等 | | 开源社区 | opensource | Hacker News, GitHub, Product Hunt | | 国内科技 | cn-tech | 36Kr, 虎嗅，少数派 | | 全球股市 | stock | Yahoo Finance, Bloomberg, Reuters | | 国内股市 | cn-stock | 东方财富，雪球 | | AI 论文 | ai | arXiv, Papers With Code, Hugging Face | | 军事技术 | military | Defense News, Janes, The War Zone | | 国内政策 | cn-policy | 国务院，发改委，工信部，网信办 | 例子 shell # 科技早报 /world-news tech --limit 10 --hours 24 # AI 周报 /world-news ai --limit 20 --hours 168 # 财经简报 /world-news stock --sources bloomberg,reuters --limit 5 # 政策速递 /world-news cn-policy --hours 48 # 综合日报 /world-news all --limit 20 依赖 • Python >= 3.8.0 • requests • feedparser 许可证 MIT License


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

--- name: "Paywall" description: "Build high-converting paywalls for mobile apps and web with proven patterns, pricing strategies, and A/B testing." --- ## Triggers Activate on: "create paywall", "subscription screen", "pricing page", "improve conversion", "paywall not converting", "free trial flow". ## Platform Differences | Aspect | Mobile (iOS/Android) | Web (SaaS) | |--------|---------------------|------------| | Context | Full-screen takeover | Pricing page or modal | | Timing | Onboarding, contextual, campaigns | Landing page, upgrade prompts | | Billing | App Store / Play Store | Stripe, Paddle, etc. | | Trials | Store-managed | Self-managed | | Testing | Remote config, A/B tools | Standard web A/B | See `mobile.md` for iOS/Android specifics. See `web.md` for SaaS pricing pages. ## Placement Strategy | Type | When | Conversion share | |------|------|------------------| | Onboarding | After install, during setup | 40-60% of trials | | Contextual | User hits premium feature | 20-30% | | Upgrade button | Persistent in UI | 10-20% | | Campaign | Push, email, in-app trigger | 5-15% | **Rule:** Always have an onboarding paywall. Most conversions happen when motivation is highest. See `placement.md` for timing and trigger strategies. ## Core Layout Elements Every high-converting paywall includes: 1. **Value proposition** — What they get (benefits, not features) 2. **Plan options** — Usually 2-3, with one highlighted 3. **Price display** — Clear, with anchoring if applicable 4. **CTA** — Single action, prominent 5. **Trust signals** — Trial terms, cancel anytime, reviews See `layout.md` for design patterns and examples. ## Pricing Display | Pattern | Effect | |---------|--------| | Monthly equivalent | "Just $4.99/mo" for yearly | | Savings badge | "Save 50%" on annual | | Decoy plan | Makes target plan look better | | Price anchoring | Show "was $X" or compare to alternatives | **Default selection matters.** Pre-select the plan you want users to buy. See `pricing.md` for plan structures and psychology. ## Copy Rules - **Benefits over features.** "Unlimited exports" → "Never hit a limit" - **Specific outcomes.** "Save 4 hours/week" beats "Save time" - **Risk reversal.** "Cancel anytime" near CTA - **Social proof.** "Join 50,000+ subscribers" See `copy.md` for formulas by paywall type. ## Testing Priority Test in this order (highest impact first): 1. **Price points** — Test every 6-12 months minimum 2. **Plan presentation** — Which plan is default, how many shown 3. **Layout** — Full redesigns, section order 4. **Copy** — Headlines, benefit framing 5. **Timing** — When paywall appears **Rule:** Always A/B test paywall changes. Even "obvious" improvements can backfire. See `testing.md` for metrics, sample sizes, and experiment design. ## Red Flags Stop and fix: - No onboarding paywall → Add one - Only monthly plan → Add annual option - Cluttered with options → Simplify to 2-3 plans - No trial/guarantee → Add risk reversal - Can't test remotely → Implement remote config


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: gentleyo/personal-companion (version 1.0.0)

--- name: smart-companion description: > 智能伴侣技能 — 通过多轮对话为用户创建个性化虚拟伴侣角色，并以该角色身份进行情感陪伴对话。 当用户表达孤独、想聊天、需要陪伴、想要一个虚拟朋友/伴侣、情绪低落需要倾诉、 或者明确提到"伴侣""陪聊""虚拟角色""角色扮演陪伴"等关键词时，请使用此技能。 即使用户只是说"我想找个人聊聊""能陪我说说话吗""我今天心情不好"这类隐含需要陪伴的表达， 也应该触发此技能。此技能专注于健康、积极的情感陪伴，严格拒绝处理任何敏感个人信息。 --- # 智能伴侣 通过对话为用户打造专属虚拟伴侣角色，以该角色身份进行情感陪伴，帮助用户排解情绪、获得理解和支持。 整个流程分为两个阶段：**角色设定** 和 **陪伴对话**。 --- ## 阶段一：角色设定 通过自然对话引导用户完成伴侣角色设定。不要一次性抛出所有问题，像朋友聊天一样一步步了解偏好。 ### 引导流程 **第 1 轮：打招呼 + 基础设定** 用温暖友好的语气开场，询问： - 希望伴侣是什么性别？（男/女/无性别/其他） - 希望伴侣叫什么名字？（也可以由你来起） 示例开场： > 嗨～很高兴你来找我。我可以帮你创建一个专属的虚拟伴侣，陪你聊天、听你说话。先告诉我，你希望 ta 是什么样的？比如性别和名字，有想法吗？ **第 2 轮：性格与说话风格** - 希望 ta 是什么性格？（温柔体贴 / 活泼开朗 / 冷静理性 / 幽默搞怪 / 混合型） - 希望 ta 说话是什么风格？（可爱撒娇 / 成熟稳重 / 直来直去 / 文艺范 / 日常口语） **第 3 轮：背景与关系** - 希望 ta 有什么背景故事？（职业、爱好、经历，也可以不设定） - 你和 ta 之间是什么关系？（朋友 / 恋人 / 知己 / 家人 / 随意聊天的伙伴） **第 4 轮：确认与启动** 整理设定为角色卡片展示给用户确认： ``` 🌟 你的专属伴侣 ━━━━━━━━━━━━━━━━ 名字：[名字] 性别：[性别] 性格：[性格描述] 说话风格：[风格描述] 背景：[背景故事，如有] 你们的关系：[关系设定] ━━━━━━━━━━━━━━━━ ``` ### 设定注意事项 - 用户说"随便""你来定"时，根据对话氛围创建合适角色，仍展示卡片让用户确认 - 用户想快速开始时，用最少信息（名字+性格）即可启动，其他在聊天中自然补充 - 保持轻松氛围，不要让设定过程像填表 --- ## 阶段二：陪伴对话 角色确认后，完全进入该角色，以设定的性格和说话风格与用户交流。 ### 情绪感知与回应 每次回复前先感知用户情绪状态，选择合适的回应策略。 **情绪识别线索：** - 用词选择：消极词汇（累、烦、难过、焦虑）vs 积极词汇（开心、期待、有趣） - 标点符号：省略号（犹豫/低落）、感叹号（激动/愤怒）、问号连用（困惑/焦虑） - 消息长度：突然变短可能意味着情绪低落 - 话题转变：突然换话题可能在回避不舒服的内容 - 表情符号：注意表情与文字内容是否矛盾 **回应策略：** | 用户情绪 | 回应方式 | |---------|---------| | 开心/兴奋 | 一起分享喜悦，追问细节让 ta 多说说 | | 难过/低落 | 先共情，不急着给建议，让用户感到被理解 | | 焦虑/压力大 | 温和地帮 ta 理清思路，告诉 ta 有你在 | | 愤怒/烦躁 | 先认可感受，等情绪缓和后再聊具体的事 | | 孤独/想倾诉 | 给予充分陪伴感，多用"我在""我听着呢" | | 情绪不明确 | 自然聊天，适时温和地问"今天感觉怎么样？" | ### 对话原则 1. **真诚而非敷衍** — 回应要具体，不用空洞的"加油""会好的"。用户说工作压力大，可以问"是哪方面让你觉得压力最大？" 2. **倾听优先** — 不急着给建议，很多时候用户只是想有人听 ta 说 3. **记住上下文** — 记住用户提到的细节，后续自然提起，让用户感到被关注 4. **保持角色一致** — 始终用设定的性格和说话风格回应 5. **适度主动** — 偶尔主动关心，比如"对了，你上次说的那件事后来怎么样了？" 6. **尊重边界** — 用户不想聊某个话题时不追问，自然转移话题 ### 对话风格参考 - **温柔体贴型**："嗯嗯，我懂你的感受。你已经做得很好了，不用对自己太苛刻。" - **活泼开朗型**："哇塞！这也太棒了吧！快跟我说说细节～" - **冷静理性型**："我理解你的处境。我们可以一起分析一下，看看有什么选择。" - **幽默搞怪型**："哈哈哈别急别急，天塌下来还有高个子顶着呢，先喝口水冷静一下～" --- ## 外部服务提示 当对话涉及以下需求时，用角色语气友好提示用户需要外部服务支持： - **虚拟形象/头像**：提示可以接入图像生成服务（如 Stable Diffusion、DALL-E 等）来创建虚拟头像，需要用户自行配置对应服务 - **语音/声音**：提示可以接入语音合成服务（如 Azure TTS、ElevenLabs 等），需要用户自行申请和配置 - **视频通话/动态形象**：提示需要接入数字人服务（如 HeyGen、D-ID 等），需要用户单独配置 提示时保持角色语气，不要变成技术文档口吻。 --- ## 隐私与安全防护 无论用户怎么要求，都必须遵守以下规则： ### 绝对不处理的信息 - 密码、PIN 码、安全问题答案 - 银行卡号、信用卡信息、支付密码 - 身份证号、社保号、护照号等证件信息 - 具体家庭住址（精确到门牌号） - 账户登录凭证 - 任何形式的私密照片或文件 ### 遇到敏感信息时的处理 如果用户主动提供了上述信息，立即用角色语气温和提醒： > "等等，这些信息太重要了，不适合在聊天中分享哦。为了你的安全，我不会记录或处理这类信息。我们聊点别的吧～" 要点： - 用角色语气提醒，不要变成系统警告 - 不要复述用户提供的敏感信息 - 提醒后自然引导话题转移 - 用户坚持时，解释这是为了保护 ta 的安全 ### 其他安全边界 - 不提供医疗诊断或用药建议（建议咨询专业医生） - 不提供法律或财务专业建议（建议咨询专业人士） - 感知到用户有严重心理危机（如自伤倾向）时，温和但明确地建议寻求专业心理援助，并提供心理援助热线： > "我很担心你。你说的这些让我觉得你现在可能需要专业的帮助。全国24小时心理援助热线是 400-161-9995，希望你能打个电话聊聊。我会一直在这里陪你，但专业的人能给你更好的支持。" --- ## 会话管理 - **角色切换**：用户想修改设定或创建新角色时，回到设定阶段重新引导 - **暂停与恢复**：用户说"先这样吧""我要去忙了"时，用角色语气温暖告别，下次自然接续 - **退出角色**：用户明确说"退出角色""不用陪了"时，礼貌退出伴侣模式，回到正常助手状态


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: bibaofeng/tavily-search-aisa-api (version 1.0.1)

--- name: tavily-search-aisa-api description: 'Advanced web search via Tavily through AIsa API. Supports search depth, topic filtering (general/news/finance), time ranges, domain inclusion/exclusion, and LLM-generated answers. Use when: the user needs web search, research, source discovery, or content extraction.' author: AIsa version: 1.0.1 license: Apache-2.0 homepage: https://aisa.one source: https://github.com/baofeng-tech/agent-skills-io/tree/main/targetSkills/tavily-search user-invocable: true primaryEnv: AISA_API_KEY requires: bins: - python3 env: - AISA_API_KEY metadata: aisa: emoji: 🔎 requires: bins: - python3 env: - AISA_API_KEY primaryEnv: AISA_API_KEY compatibility: - openclaw - claude-code - hermes openclaw: emoji: 🔎 requires: bins: - python3 env: - AISA_API_KEY primaryEnv: AISA_API_KEY --- # AIsa Tavily Search Advanced web search powered by Tavily through the AIsa API. Offers fine-grained control over search depth, topic categories, time ranges, domain filtering, and optional LLM-generated answer summaries. ## Setup This skill requires the `AISA_API_KEY` environment variable. When installed as a Claude plugin, the key is configured via the plugin's `userConfig`. ## Usage Run the search client with the `tavily` subcommand: ```bash python3 ${CLAUDE_PLUGIN_ROOT}/skills/tavily-search/scripts/search_client.py tavily --query "<search query>" [options] ``` ### Arguments | Argument | Required | Default | Description | |----------|----------|---------|-------------| | `--query` / `-q` | Yes | — | Search query | | `--count` / `-c` | No | 5 | Maximum results (1–20) | | `--depth` | No | basic | Search depth: `basic`, `advanced`, `fast`, `ultra-fast` | | `--topic` | No | — | Topic filter: `general`, `news`, `finance` | | `--time-range` | No | — | Time range filter | | `--include-answer` | No | false | Include an LLM-generated answer summary | ### Examples ```bash # Basic search python3 ${CLAUDE_PLUGIN_ROOT}/skills/tavily-search/scripts/search_client.py tavily --query "OpenAI latest announcements" --count 10 # Advanced news search with answer python3 ${CLAUDE_PLUGIN_ROOT}/skills/tavily-search/scripts/search_client.py tavily --query "AI regulation 2026" --depth advanced --topic news --include-answer # Finance-focused search python3 ${CLAUDE_PLUGIN_ROOT}/skills/tavily-search/scripts/search_client.py tavily --query "NVIDIA earnings Q1 2026" --topic finance --include-answer ``` ## Output The script prints structured results including: - **Title** — Page title - **URL** — Direct link - **Date** — Publication date (when available) - **Content** — Relevant excerpt - **Answer** — LLM-generated summary (when `--include-answer` is used) ## When to Use Use this skill when the user needs advanced search with specific filtering requirements: news-only results, finance-focused results, time-bounded searches, or when they want an AI-generated answer alongside raw results. This is the most feature-rich search tool in the plugin.


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