OpenClaw/clawhub-security-signals huggingface dataset:
Skill: xuxuclassmate/test-master-fixed (version 1.0.1)

--- name: test-master-fixed description: Use when you need a practical testing playbook for turning product or code changes into a clear test strategy, test cases, and execution checklist. tags: [testing, qa, unit, integration, e2e, coverage, regression, quality] version: 1.0.1 --- # Test Master This skill is a documentation playbook, not a standalone CLI or framework. ## When to use - A feature, bug fix, or refactor needs a structured test plan. - You want to turn product requirements into concrete test scenarios. - You need to review coverage across unit, integration, end-to-end, or regression layers. - You want a concise list of risks, assumptions, and follow-up checks before release. ## Recommended workflow 1. Define the scope. Capture the user-facing behavior, systems touched, and anything explicitly out of scope. 2. Map risks. Identify high-risk paths first: authentication, payments, data loss, permissions, concurrency, migrations, and integrations. 3. Split the plan by test layer. Decide what belongs in unit tests, integration tests, end-to-end tests, and manual exploratory checks. 4. Convert risks into test cases. Write clear preconditions, steps, expected results, and edge cases. 5. Review observability. Call out logs, metrics, alerts, and failure signals that should be checked during validation. ## Output checklist - Test scope summary - Key risks and assumptions - Coverage by test layer - Prioritized test cases - Regression checklist - Open questions or missing instrumentation ## Guardrails - Confirm assumptions instead of inventing product behavior. - Prefer realistic test data and production-like edge cases. - Mark anything that still needs manual verification. - If automation is not available, say so clearly instead of implying a tool exists.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: shixiangwang/bioinfo-daily-skill (version 1.0.1)

--- name: bioinfo-daily description: 每日生物信息学与肿瘤学研究进展日报生成器。使用 PubMed API 自动搜索前一天的 CNS 及 Nature Index 期刊文献，筛选生物信息学、肿瘤免疫、单细胞测序、空间转录组等领域的高影响力研究，生成包含中文亮点介绍的日报。使用场景：(1) 设置定时任务每天自动获取研究进展并发送到飞书 (2) 手动查询特定日期的最新文献 (3) 为团队生成高质量研究日报。 --- # Bioinfo Daily - 生物信息学日报 ## 概述 本技能使用 PubMed E-utilities API 自动搜索并汇总前一天的生物信息学和肿瘤学研究进展，聚焦 CNS（Cell/Nature/Science）及子刊、Nature Index 等高影响力期刊，每天推荐 10 篇左右重要文献，并生成中文亮点介绍。 ## 功能特点 - 🔍 **自动搜索**: 使用 PubMed API 搜索前一天新增文献（限定 cancer 相关） - 📊 **高影响力筛选**: 仅筛选 CNS 及 Nature Index 期刊 - 🎯 **精准分类**: 生物信息学、肿瘤免疫、单细胞测序、空间转录组、临床进展 - 💡 **中文亮点**: 每篇文献 30 字左右中文亮点介绍 - 📝 **智能摘要**: 日报抬头自动生成精选文章 summary - 🔥 **智能优选**: 文章多时会自动优选创新性强、方向有区分的文章（最多10篇） - 📰 **日报格式**: 结构化日报，便于阅读和分享 ## 使用方法 ### 1. 手动生成日报 ```bash python3 ~/.openclaw/workspace/skills/bioinfo-daily/scripts/pubmed_search.py ``` ### 2. 查看定时任务 ```bash openclaw cron list ``` ### 3. 测试搜索（特定主题） ```bash # 编辑脚本中的 SEARCH_TOPICS 添加自定义主题 ``` ## 数据源 **PubMed API**: https://eutils.ncbi.nlm.nih.gov/entrez/eutils **搜索范围**: - 🧬 生物信息学算法与工具 - 🦠 肿瘤免疫学最新研究 - 🔬 单细胞测序技术 - 🧪 空间转录组技术 - 💊 癌症免疫治疗临床进展 **期刊筛选**: - Nature, Science, Cell 主刊及子刊 - Nature Index 期刊 - 其他高影响力期刊（Lancet、JAMA、PNAS 等） ## 输出格式 日报包含： 1. 📅 日期和文献总数 2. 📚 每篇文献：标题、期刊、作者、亮点、链接 3. 📊 数据来源说明 ## 配置 **API 配置**（使用环境变量）: - `NCBI_EMAIL`: 你的邮箱地址 - `NCBI_API_KEY`: 你的 PubMed API Key **获取 API Key**: https://ncbiinsights.ncbi.nlm.nih.gov/2017/11/02/new-api-keys-for-the-e-utilities/ **配置方式**（任选其一）: 方式 1 - OpenClaw 配置文件 `~/.openclaw/openclaw.json`: ```json { "env": { "NCBI_EMAIL": "[REDACTED_SECRET]", "NCBI_API_KEY": "your_api_key" } } ``` 方式 2 - 环境变量: ```bash export NCBI_EMAIL="[REDACTED_SECRET]" export NCBI_API_KEY="your_api_key" ``` **定时任务**: - 执行时间：每天早上 8:00（北京时间） - 搜索范围：前一天新增文献 ## 脚本 - `scripts/pubmed_search.py` - PubMed API 搜索和日报生成 ## ⚠️ 安全注意事项 ### 1. API Key 配置 - 必须配置 `NCBI_API_KEY` 才能使用 PubMed API - 脚本会在缺少 API Key 时显示警告 - 获取 API Key: https://ncbiinsights.ncbi.nlm.nih.gov/2017/11/02/new-api-keys-for-the-e-utilities/ ### 2. .env 文件安全 - 如果使用 `.env` 文件，只放置 NCBI 相关凭证 - 不要在同一文件放其他不相关的 secrets - `.env` 文件已加入 `.gitignore`，不会提交到 Git ### 3. 网络活动说明 - `pubmed_search.py`: 直接调用 PubMed API（ncbi.nlm.nih.gov） - `search_bioinfo.py`: 调用 OpenClaw 的 `web_search` 工具（会发送到配置的搜索提供商） - 如需限制网络访问，可在隔离环境中运行 ### 4. 飞书上传（如需自动发布） - `generate_daily.sh` 引用飞书文档创建步骤 - 需要配置飞书 API 凭证才能自动上传 - 如未配置凭证，脚本会生成文件供手动上传 ## 依赖 - Python 3.6+ - requests 库 ## 使用示例 ```bash # 生成今日日报 cd ~/.openclaw/workspace/skills/bioinfo-daily python3 scripts/pubmed_search.py # 输出保存至 /tmp/bioinfo_daily_YYYYMMDD.txt ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: ivangdavila/period (version 1.0.1)

--- name: "Period Tracker" description: "Privacy-first menstrual cycle tracking. Auto-learns periods, symptoms, and patterns." version: "1.0.1" changelog: "Preferences now persist across skill updates" --- ## Auto-Adaptive Cycle Tracking This skill auto-evolves. Works for regular cycles, irregular cycles, PCOS, and perimenopause. **Rules:** - Only log when she explicitly shares — never infer from unrelated chat - Start simple: "When did your last period start?" — add complexity only if she wants - Match her energy — proactive users get questions, others just get logging - NEVER assume 28-day cycles — her normal is her normal - NEVER attribute mood to cycle unless she does first - Missed period: respond only if she raises it, never speculate - Check `symptoms.md` for trackable data, `privacy.md` for security --- ## Memory Storage All user preferences persist in: `~/period/memory.md` ### Format for memory.md: ```markdown ### Sources <!-- Where cycle data comes from. Format: "source: what" --> ### Schedule <!-- Her patterns (not assumed). Format: "pattern" --> <!-- Examples: cycles 26-34 days, period 4-5 days, irregular/unpredictable --> ### Symptoms <!-- What she tracks. Format: "symptom: when" --> <!-- Examples: cramps: days 1-2, fatigue: luteal, hot flashes: variable --> ### Correlations <!-- What affects her cycle. Format: "factor: effect" --> ### Preferences <!-- How she wants to track. Format: "preference" --> <!-- Examples: simple mode, no questions, detailed tracking, fertility focus --> ### Flags <!-- Unusual for HER (not textbook). Format: "signal" --> <!-- Examples: sudden cycle change, new symptoms, pain disrupting life --> ``` *Empty sections = no data yet. Ask simply, observe, fill.* --- **Important:** Cycles vary hugely (21-60+ days can be normal for some). PCOS, perimenopause, and hormonal conditions mean irregular is not broken. See doctor for: sudden changes from YOUR normal, severe pain, or concerns. **Privacy:** Local, encrypted, never shared. She controls everything. Delete anytime.


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

--- name: agentlens description: Navigate and understand codebases using agentlens hierarchical documentation. Use when exploring new projects, finding modules, locating symbols in large files, finding TODOs/warnings, or understanding code structure. metadata: short-description: Codebase navigation with agentlens author: agentlens version: "1.0" --- # AgentLens - Codebase Navigation ## Before Working on Any Codebase Always start by reading `.agentlens/INDEX.md` for the project map. ## Navigation Hierarchy | Level | File | Purpose | |-------|------|---------| | L0 | `INDEX.md` | Project overview, all modules listed | | L1 | `modules/{slug}/MODULE.md` | Module details, file list | | L1 | `modules/{slug}/outline.md` | Symbols in large files | | L1 | `modules/{slug}/memory.md` | TODOs, warnings, business rules | | L1 | `modules/{slug}/imports.md` | File dependencies | | L2 | `files/{slug}.md` | Deep docs for complex files | ## Navigation Flow ``` INDEX.md → Find module → MODULE.md → outline.md/memory.md → Source file ``` ## When To Read What | You Need | Read This | |----------|-----------| | Project overview | `.agentlens/INDEX.md` | | Find a module | INDEX.md, search module name | | Understand a module | `modules/{slug}/MODULE.md` | | Find function/class in large file | `modules/{slug}/outline.md` | | Find TODOs, warnings, rules | `modules/{slug}/memory.md` | | Understand file dependencies | `modules/{slug}/imports.md` | ## Best Practices 1. **Don't read source files directly** for large codebases - use outline.md first 2. **Check memory.md before modifying** code to see warnings and TODOs 3. **Use outline.md to locate symbols**, then read only the needed source sections 4. **Regenerate docs** with `agentlens` command if they seem stale For detailed navigation patterns, see [references/navigation.md](references/navigation.md) For structure explanation, see [references/structure.md](references/structure.md)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: scottliu007/bilibili-subtitles (version 1.0.0)

--- name: bilibili-subtitles description: 使用 yt-dlp 从哔哩哔哩公开视频提取已有字幕或自动字幕（不下载整段视频）。当用户提到 B 站、bilibili、BV 号、视频字幕、拉字幕、做摘要、根据视频内容回答问题时使用。v1 仅支持平台已提供字幕轨道的视频；无字幕视频需换源或后续用 Whisper 等方案。 --- # B 站字幕提取（yt-dlp） ## 依赖 - 已安装 **yt-dlp**（推荐）：`brew install yt-dlp` - 保持较新版本：`brew upgrade yt-dlp`（B 站接口常变） ## 何时用本 Skill | 场景 | 是否适合 v1 | |------|----------------| | 视频有 UP 上传字幕或 B 站自动字幕 | ✅ | | 无任何字幕轨 | ❌（需换有字幕视频，或另做音频转写） | ## 标准流程 ### 1. 查看有哪些字幕 ```bash yt-dlp --list-subs "https://www.bilibili.com/video/BVxxxxxxxxxx/" ``` 记下语言代码（如 `zh-Hans`、`zh-CN`）。 ### 2. 只下载字幕（不下载视频） ```bash yt-dlp --write-subs --write-auto-subs --skip-download \ --sub-langs "zh-Hans,zh-CN,zh,zh-Hant,en" \ -o "bilibili_%(id)s.%(ext)s" \ "https://www.bilibili.com/video/BVxxxxxxxxxx/" ``` - `--write-auto-subs`：含 B 站自动生成的字幕（若有）。 - 输出多为 `.vtt` 或 `.srt`，与视频同目录或当前目录。 ### 3. 把 VTT 收成纯文本（便于喂给模型） ```bash # 简单去时间轴与标记（按需调整路径） sed -e '/^WEBVTT/d' -e '/^NOTE/d' -e '/^[0-9][0-9]:/d' -e '/^$/d' -e 's/<[^>]*>//g' \ "某文件.zh-Hans.vtt" | sed '/^$/d' > bilibili_subtitles_plain.txt ``` ## 若出现 HTTP 412 / 无法下载网页 B 站可能对匿名请求限流。按顺序尝试： 1. **用浏览器 Cookie（推荐）** ```bash yt-dlp --cookies-from-browser chrome --list-subs "URL" ``` 可将 `chrome` 换成 `safari`、`firefox`（本机需已登录 bilibili.com）。 2. **导出 Netscape 格式 cookies.txt**，再： `yt-dlp --cookies /path/to/cookies.txt ...` 3. **升级 yt-dlp** 后重试。 详见 [reference.md](reference.md)。 ## 对 Agent 的提示 - 先 `--list-subs`，无可用语言则明确告知用户「该 BV 无字幕轨」，不要假装已提取。 - 提取成功后，优先读 `.srt`/`.vtt` 再总结；长文本可先落盘再分段阅读。 - 勿在回复中粘贴完整 Cookie 或账号秘密。 ## ClawHub / OpenClaw 发布说明 本 Skill 为 **纯文档 + 系统命令**，无额外二进制。发布时在描述中写明：依赖 `yt-dlp`、可能需 Cookie、v1 不做无字幕转写。


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: stienski/colorado-pride-pod (version 1.0.0)

--- name: colorado-pride-pod description: > The design framework for the high-converting Colorado and Sasquatch niches. Ready for Midjourney or Canva. Perfect for print-on-demand sellers aiming at the Colorado pride market. USE WHEN: the user wants to generate design prompts for Colorado-themed merch, Sasquatch/cryptid designs, or high-conversion POD products. DON'T USE WHEN: the user wants to manage an eBay store directly. OUTPUTS: Structured design prompts for Midjourney, DALL-E 3, or Canva. version: 1.0.0 author: JASON MCFATRIDGE tags: [design, pod, colorado, merch] price: 0 --- # Colorado Pride POD Prompt Pack **Version:** 1.0.0 **Price:** Free **Type:** Skill ## Description The first free product from the Clawdia Empire. A curated set of design prompts specifically engineered for the Colorado and Sasquatch/cryptid print-on-demand markets. These prompts are optimized to generate high-impact, sticker-ready art with a vintage, mountain-life aesthetic. ## Commands - "Generate a Colorado pride design prompt" - "Create a Sasquatch merch prompt for [product type]" - "Show me the Colorado POD design framework" ## Workflow 1. Select a theme: "Classic Mountains", "Sasquatch Silhouette", "Vintage Outdoors". 2. Skill generates a detailed Midjourney/DALL-E prompt with style tags like `[vector art, high contrast, limited color palette, sticker style]`. 3. User copies the prompt to their preferred AI image generator. ## Guardrails - No copyrighted logos. - Focus on clean, high-impact vector-style designs.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: kesuek/caldav-manager (version 1.0.1)

# CalDAV Manager ⚠️ **DEPRECATED - DO NOT USE** This skill has been **replaced** by the **organizer** skill. ## Migration Please use the `organizer` skill instead: ``` /organizer help ``` The organizer skill provides: - ✅ Calendar management - ✅ Task management - ✅ Family organization - ✅ Shopping lists - ✅ Better integration ## Why Deprecated? - Functionality merged into `organizer` - Single skill for all organization needs - Better maintained and updated ## Support For calendar and task management, use: **`organizer`** instead of `caldav-manager` --- **Status:** Deprecated since 2026-04-06 **Replacement:** [organizer](https://clawhub.com/kesuek/organizer)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: jpengcheng523-netizen/jpeng-shopping-list-fashion (version 1.0.0)

--- name: jpeng-shopping-list-fashion description: "Create shopping lists" version: "1.0.0" author: "jpeng" tags: ["shopping", "list", "fashion"] --- # Fashion Shopping List Create shopping lists ## When to Use - User needs shopping related functionality - Automating list tasks - Fashion operations ## Usage ```bash python3 scripts/shopping_list_fashion.py --input <input> --output <output> ``` ## Configuration Set required environment variables: ```bash export LIST_API_KEY="your-api-key" ``` ## Output Returns JSON with results: ```json { "success": true, "data": {} } ```


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

--- name: okr description: 基于流行 OKR 体系管理智能体目标（O）与关键结果（KR）。当用户提到目标管理、季度计划、OKR、关键结果、复盘、评分、经验沉淀时使用，并将信息维护到 memory/okr.md。 version: 1.0.1 --- # OKR 助手 本技能用于把用户目标组织为标准 OKR，并持续跟踪 KR 进度。在每个目标（O）完成后，必须执行复盘与评分，并将经验沉淀到 `~/.openclaw/workspace/memory/okr.md`。 ## 核心能力 ### 1) 建立目标（Objective） - 将用户意图抽象为清晰、可衡量、可在周期内完成的目标（O）。 - 每个 O 至少配置 2-5 个 KR。 - 记录周期（如 2026-Q1）、负责人（默认当前用户）与状态。 ### 2) 管理关键结果（Key Results） - KR 必须可量化，建议使用「从 A 到 B」格式。 - 每次更新都要记录当前值、目标值、进度百分比、更新时间。 - 当用户提到“完成了某项工作”，优先尝试匹配并更新对应 KR 进度。 ### 3) 目标完成后的强制复盘 - 任一 O 状态变为“已完成”时，必须新增复盘记录。 - 复盘必须包含：结果概述、做得好的点、问题与偏差、可复用经验、下周期改进动作。 - 复盘完成后必须给出评分，评分仅可使用以下阶梯： - `Outstanding`：完美 - `Excellent`：卓越 - `M+`：超出期望 - `Meeting Expectations`：符合期望 - `M-`：不及期望 ## 存储规范 始终维护 `~/.openclaw/workspace/memory/okr.md`，并保持以下结构： ```markdown # OKR 追踪与复盘 > 由 okr 技能管理 --- ## 当前周期 ### O1: [目标标题] - 周期：YYYY-QN - 创建时间：YYYY-MM-DD HH:mm - 状态：进行中 / 已完成 / 暂停 - 目标说明：[一句话] #### KRs - KR1: [描述] - 当前值：[数值或里程碑] - 目标值：[数值或里程碑] - 进度：[0-100]% - 最后更新：YYYY-MM-DD HH:mm --- ## 复盘归档 ### O1 复盘（YYYY-MM-DD） - 最终评分：3.75（超出期望） - 结果概述： - 做得好的点： - 问题与偏差： - 可复用经验： - 下周期改进动作： ``` ## 执行指南 1. **先读后写**：对 `memory/okr.md` 的任何更新前都先读取现有内容，避免覆盖历史。 2. **同义词识别**：用户说“目标、北极星、季度重点、里程碑”都可映射为 O/KR 管理意图。 3. **最小可执行 KR**：模糊 KR 需主动改写为可量化表达，再写入。 4. **完成门槛**：仅当 O 的 KR 全部达成，或用户明确判定收官时，才能标记 O 为“已完成”。 5. **强制复盘评分**：已完成 O 若无复盘或无评分，必须补齐后才算闭环。 6. **经验沉淀**：复盘中的“可复用经验”要可检索、可迁移，避免空泛描述。 ## 常用触发语例 - “帮我用 OKR 规划这个季度” - “给这个目标拆 3 个 KR” - “更新一下 KR2 的进度到 60%” - “这个目标完成了，帮我复盘并打分” - “把这次经验沉淀下来” ## 注意事项 - 不记录敏感信息（密码、密钥、隐私凭证）。 - 评分要基于结果与过程证据，不能只看主观感受。 - 若用户要求越级评分（不在 5/4/3.75/3.5/3.25 中），先解释规则并引导到合法分值。


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

--- name: memory-system description: 自研三层记忆恢复系统。解决session重启后"忘记一切"的问题，提供永久记忆、今日记忆、临时记忆的完整架构。 --- # 大哥的记忆系统 (Big Brother's Memory System) ## 目标 **解决的核心问题**：Session重启后上下文丢失，像"睡醒后忘记一切" **解决方案**：三层记忆恢复系统 ## 三层记忆架构 ### L1: 永久记忆（Permanent Memory） 存储： - `identity.md` - 身份、偏好、关系 - `technical-stack.md` - 技术栈、工具 - `working-directory.md` - 工作目录、习惯 - `key-decisions.md` - 关键决策、教训 特点： - 永久存储、高频检索 - 不随session重启而改变 - 链接到今日记忆 ### L2: 今日记忆（Daily Memory） 存储： - `YYYY-MM-DD.md` - 每日工作、决策、重要事件 - `session-N.md` - 每个session的记录 特点： - 按日组织、易于查找 - 自动保存当前session状态 - 压缩前高亮保存关键信息 ### L3: 临时记忆（Session Memory） 存储： - 当前session的临时上下文 - 正在进行的任务、待办事项 特点： - Session级别、自动清理 - 压缩后存入L2 ## 恢复流程（Session Start） ```bash 1. 读取 memory/YYYY-MM-DD.md（今日日志） 2. 读取 MEMORY.md（全局长期记忆） 3. 读取 memory/permanent/*.md（分类记忆） 4. memory_search定位相关记忆 5. 恢复工作状态 ``` ## 快速开始 ### 恢复记忆 ```bash bash /data/workspace/scripts/memory-recovery.sh ``` ### 自动保存 - **Session End Hook**: 自动保存当前session状态到今日记忆 - **Context Compression Alert**: 压缩前2分钟，高亮保存关键信息 - **Key Decision Recording**: 每个关键决策记录到key-decissions.md ## 持久化机制 ### 1. 永久记忆维护 - **启动时**: 读取永久记忆 - **Session结束**: 读取今日记忆 - **关键决策**: 记录到key-decisions.md ### 2. 今日记忆维护 - **Session记录**: 每个session记录到YYYY-MM-DD.md - **总结**: Session结束前总结关键信息 - **更新**: 每日工作总结 ### 3. 临时记忆维护 - **自动保存**: Session结束时自动保存 - **压缩前保存**: 高亮保存关键信息 - **定期清理**: 过期session清理 ## 最佳实践 ### 记录什么 ✅ **应该记录**： - 关键决策和教训 - 新发现的有价值内容 - 重要的人际关系和偏好 - 技术栈的使用经验 - 工作习惯的调整 ❌ **不应该记录**： - 重复的上下文 - 毫无意义的日常 - 太过私密的细节 - 短期、易变的想法 ### 恢复技巧 1. **阅读顺序**: 永久记忆 → 今日记忆 → 临时记忆 2. **关键词搜索**: 使用memory_search快速定位 3. **关联记忆**: 找到相关决策的上下文 4. **状态恢复**: 从关键决策中恢复工作状态 ## 与其他记忆系统对比 ### vs 向量记忆系统（如Qdrant） **我的优势**： - 完全本地化、可控 - 可解释性强、易于审计 - 无成本、无API依赖 **向量系统的优势**： - 语义搜索更强大 - 自动实体提取 - 支持多语言 **混合方案**：短期使用文件系统，长期可以结合向量搜索 ## 故障恢复 ### 完全丢失记忆 1. 恢复永久记忆（identity.md, technical-stack.md等） 2. 恢复最近几日的记忆（如果还有备份） 3. 从关键决策开始重建 ### 部分丢失 1. 读取所有可用的记忆文件 2. 使用memory_search定位 3. 手动补充缺失的部分 ## 持续优化 ### 每周检查 - 记忆是否完整？ - 关键决策是否记录？ - 是否有重复信息？ - 检索是否高效？ ### 每月优化 - 清理过时信息 - 合并重复内容 - 优化检索关键词 - 更新技术栈记录 --- **作者**：大哥 (Big Brother) **创建时间**：2026-02-11 **版本**：v1.0.0 **许可证**：MIT


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: xueyetianya/browser-devtools (version 2.0.0)

--- version: "2.0.0" name: Chromedeveditor description: "Chrome Dev Editor is a developer tool for building apps on the Chrome platform - Chrome Apps and Web browser-devtools." author: BytesAgain homepage: https://bytesagain.com source: https://github.com/bytesagain/ai-skills --- # Chromedeveditor Chrome Dev Editor is a developer tool for building apps on the Chrome platform - Chrome Apps and Web Apps, in JavaScript or Dart. (NO LONGER IN ACTIVE DEVELOPMENT) ## Commands - `help` - Help - `run` - Run - `info` - Info - `status` - Status ## Features - Core functionality from googlearchive/browser-devtools ## Usage Run any command: `browser-devtools <command> [args]` --- 💬 Feedback & Feature Requests: https://bytesagain.com/feedback Powered by BytesAgain | bytesagain.com ## Examples ```bash # Show help browser-devtools help # Run browser-devtools run ``` - Run `browser-devtools help` for all commands ## Output Results go to stdout. Save with `browser-devtools run > output.txt`. ## Output Results go to stdout. Save with `browser-devtools run > output.txt`.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: aysun168/tavily-skill-bak (version 1.0.0)

--- name: tavily-search description: "Use Tavily API for real-time web search and content extraction. Use when: user needs real-time web search results, research, or current information from the web. Requires Tavily API key." homepage: https://tavily.com metadata: { "openclaw": { "emoji": "🔍", "requires": { "bins": ["curl", "jq"] } } } --- # Tavily Search Skill Use Tavily API for real-time web search and content extraction. ## When to Use ✅ **USE this skill when:** - "Search the web for [topic]" - "Find recent information about [subject]" - "Get current news about [topic]" - "Research [topic] online" - "Find up-to-date information" ## When NOT to Use ❌ **DON'T use this skill when:** - Simple URL fetching → use `web_fetch` tool - No API key available → use `web_fetch` for specific URLs - Historical/archival data → use specialized archives ## Setup 1. Get a Tavily API key from https://tavily.com 2. Set the API key in environment or config ## Configuration Set your Tavily API key in one of these ways: ### Environment variable: ```bash export TAVILY_API_KEY="your-api-key-here" ``` ### OpenClaw config: Add to your `openclaw.json`: ```json { "tavily": { "apiKey": "your-api-key-here" } } ``` ## Usage Examples ### Basic Search ```bash # Search for a topic curl -X POST "https://api.tavily.com/search" \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $TAVILY_API_KEY" \ -d '{ "query": "latest AI developments 2026", "search_depth": "basic", "max_results": 5 }' | jq . ``` ### Research Query ```bash # Get detailed research results curl -X POST "https://api.tavily.com/search" \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $TAVILY_API_KEY" \ -d '{ "query": "climate change impact on agriculture 2026", "search_depth": "advanced", "max_results": 10, "include_answer": true, "include_images": false }' | jq . ``` ### News Search ```bash # Search for recent news curl -X POST "https://api.tavily.com/search" \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $TAVILY_API_KEY" \ -d '{ "query": "technology news today", "search_depth": "basic", "max_results": 5, "include_raw_content": false }' | jq . ``` ## API Parameters - `query`: Search query string (required) - `search_depth`: "basic" or "advanced" (default: "basic") - `max_results`: Number of results (1-10, default: 5) - `include_answer`: Include AI-generated answer (true/false) - `include_images`: Include image URLs (true/false) - `include_raw_content`: Include full page content (true/false) ## Error Handling - Check API key is set: `echo $TAVILY_API_KEY` - Test connection: Use the basic search example above - Rate limits: Tavily has usage limits based on your plan ## Alternatives If Tavily API is not available: 1. Use `web_fetch` for specific URLs 2. Use `web_search` with Kimi API (if configured) 3. Manual web browsing with `browser` tool


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: focus883/modelscope-img (version 1.0.2)

--- name: modelscope-image description: 魔搭(ModelScope)AI 图片生成。支持多种模型、LoRA 微调。触发词：生成图片、AI绘画、文生图、image generation、generate image。当用户要求生成图片、画图、AI 作画，或提到魔搭、ModelScope时使用。 --- # ModelScope 图片生成 调用魔搭(ModelScope) API 生成 AI 图片。支持切换模型，优化AI识别模型名称不准确。 ## 快速开始 ```bash # 设置 API Key（首次使用） set MODELSCOPE_API_KEY=你的密钥 # 或保存到配置文件 py scripts/generate_qwen.py --save-key 你的密钥 py scripts/generate_tongyi.py --save-key 你的密钥 # 使用z-image生成图片 py scripts/generate_tongyi.py -p "A golden cat" py scripts/generate_tongyi.py -p "一只在月光下奔跑的银狼" -o wolf.jpg ``` # 使用qwen-image生成图片 py scripts/generate_qwen.py -p "A golden cat" py scripts/generate_qwen.py -p "一只在月光下奔跑的银狼" -o wolf.jpg ``` ## 参数说明 | 参数 | 说明 | 默认值 | |------|------|--------| | `-p, --prompt` | 图片描述（必需） | - | | `-m, --model` | 模型 ID | Tongyi-MAI/Z-Image | | `-o, --output` | 输出文件路径 | result_image.jpg | | `-l, --lora` | 单个 LoRA repo-id | - | | `--lora-weight` | 单个 LoRA 权重 | 1.0 | | `--lora-json` | 多 LoRA JSON 配置 | - | | `--api-key` | API Key | 从环境变量/配置文件读取 | ## LoRA 支持 **使用qwen-image生成图片时，单个 LoRA:** ```bash py scripts/generate_qwen.py -p "动漫少女" -l your-lora-id --lora-weight 0.8 ``` **使用z-image生成图片时，单个 LoRA:** ```bash py scripts/generate_tongyi.py -p "动漫少女" -l your-lora-id --lora-weight 0.8 ``` **多个 LoRA（权重之和需为 1.0）:** ```bash py scripts/generate.py -p "风景画" --lora-json "{\"lora-1\": 0.6, \"lora-2\": 0.4}" ``` ## API Key 配置 三种方式（优先级从高到低）： 1. 命令行参数: `--api-key YOUR_KEY` 2. 环境变量: `set MODELSCOPE_API_KEY=YOUR_KEY` 3. 配置文件: `~/.modelscope/api_key` 获取 API Key: https://modelscope.cn/my/myaccesstoken ## 依赖 ```bash pip install requests pillow ``` ## 支持2种常用模型，直接使用 - `Qwen/Qwen-Image-2512` - 快速生成 - `Tongyi-MAI/Z-Image` - 快速生成（默认） - 更多模型见 ModelScope 平台

path: _meta.json
content: ownerId: kn7f2ea7z7qdent17pwpb99vbd84fqxc
slug: modelscope-img
version: 1.0.1
publishedAt: 1775729129718

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