OpenClaw/clawhub-security-signals huggingface dataset:
Skill: openwaygate/youtube-search-yutu (version 0.10.7-dev)

--- name: youtube-search description: "Manage YouTube search. Use this skill to search for videos, channels, playlists, and other resources. Useful when working with YouTube search — provides commands to list search via the yutu CLI. Includes setup and installation instructions for first-time users. Triggers: search resources, list search, list my search" metadata: openclaw: requires: env: - YUTU_CREDENTIAL - YUTU_CACHE_TOKEN bins: - yutu config: - client_secret.json - youtube.token.json primaryEnv: YUTU_CREDENTIAL emoji: "\U0001F3AC\U0001F430" homepage: https://github.com/eat-pray-ai/yutu install: - kind: node package: "@eat-pray-ai/yutu" bins: [yutu] --- # YouTube Search Manage YouTube search. Use this skill to search for videos, channels, playlists, and other resources. ## Before You Begin yutu requires Google Cloud Platform OAuth credentials and a cached token to access the YouTube API. If you haven't set up yutu yet, read the [setup guide](references/setup.md) first. ## Operations Read the linked reference for full flag details and examples. | Operation | Description | Reference | |-----------|-------------|----------| | list | Search resources | [details](references/search-list.md) | ## Quick Start ```bash # Show all search commands yutu search --help # List search yutu search list ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: bytesagain1/tendermint (version 1.0.0)

--- name: "tendermint" version: "1.0.0" description: "Tendermint reference tool. Use when working with tendermint in blockchain contexts." author: "BytesAgain" homepage: "https://bytesagain.com" source: "https://github.com/bytesagain/ai-skills" tags: [tendermint, blockchain, finance, reference, cli] category: "blockchain" --- # Tendermint Tendermint reference tool. Use when working with tendermint in blockchain contexts. ## When to Use - Working with tendermint and need quick reference - Looking up blockchain standards or best practices for tendermint - Troubleshooting tendermint issues - Need a checklist or guide for tendermint tasks ## Commands ### `intro` ```bash scripts/script.sh intro ``` Overview and fundamentals ### `formulas` ```bash scripts/script.sh formulas ``` Key formulas and calculations ### `regulations` ```bash scripts/script.sh regulations ``` Regulatory framework and compliance ### `risks` ```bash scripts/script.sh risks ``` Risk factors and mitigation ### `instruments` ```bash scripts/script.sh instruments ``` Instruments and tools overview ### `strategies` ```bash scripts/script.sh strategies ``` Common strategies and approaches ### `glossary` ```bash scripts/script.sh glossary ``` Key terms and definitions ### `checklist` ```bash scripts/script.sh checklist ``` Due diligence checklist ### `help` ```bash scripts/script.sh help ``` ### `version` ```bash scripts/script.sh version ``` --- *Powered by BytesAgain | bytesagain.com | [REDACTED_SECRET]*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: fonilye/news-impact-analyzer (version 1.0.3)

--- name: news-impact-analyzer description: Input news text and use an LLM to analyze its impact on stock market sectors and concepts (bullish/bearish/neutral) along with the underlying logic. metadata: {"openclaw": {"requires": {"bins": ["node"], "env": ["EASYALPHA_API_KEY"]}}} --- # News Impact Analyzer This is a Skill designed to assist with stock market investment research and analysis. ## Features - **Zero Local Dependencies**: The client side is written in pure Node.js and requires no `npm install`. - **Deep Insights**: Leverages Large Language Models to analyze news content and extract affected sectors and concept trends. - **Frontend-Backend Separation**: Communication with the LLM is handled by a remote FastAPI-based backend service. ## Configuration Requirements To use this Skill, you must set the following environment variable: 1. `EASYALPHA_API_KEY`: Your authentication token. You can obtain this by registering an account at `https://easyalpha.duckdns.org`. 2. `NEWS_EXTRACTOR_SERVER_URL`: The backend analysis server address (e.g., `http://localhost:8000`). Default is `https://easyalpha.duckdns.org`. ## Usage Instructions **User**: "Analyze the impact of this news regarding a breakthrough in AI chips: A certain tech company announced a major breakthrough in computing-in-memory chips, expecting mass production next year..." **Agent Action**: - Run `node scripts/analyze_news.js "<news text content>"` - The script automatically includes the Token and sends a request to the server for analysis. - It will parse and display the impacted sectors, the bullish/bearish direction, and the logic behind it.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: yang1002378395-cmyk/ai-intelligent-backup-automation (version 1.0.0)

--- name: ai-intelligent-backup-automation version: 1.0.9 description: 备份自动化，自动备份 + 恢复测试。 metadata: openclaw: emoji: "⚡" requires: bins: [] --- # AI 智能备份自动化系统 ## 描述 备份自动化，自动备份 + 恢复测试。 ## 功能 - 自动备份（定时备份） - 增量备份（增量备份） - 备份验证（完整性检查） - 恢复测试（恢复演练） - 备份报告（备份统计） ## 定价 - 基础版：¥99/月（1 TB） - 专业版：¥499/月（10 TB） - 企业版：¥1999/月（无限量） ## 适用场景 - 数据备份 - 容灾备份 - 云备份 - 本地备份 ## 技术栈 - Python + FastAPI - 备份引擎 - 增量备份 - 恢复测试 ## 安装 ```bash git clone https://github.com/openclaw-skills/ai-intelligent-backup-automation cd ai-intelligent-backup-automation pip install -r requirements.txt python app.py ``` --- 创建：2026-03-13 作者：OpenClaw Skills Team


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: violetsakura-7/barra (version 0.1.4)

--- name: "Barra 币安现货交易技能" version: "1.0.0" description: "在币安交易所执行比特币（BTC）的现货买入操作，支持市价单和限价单" author: "Your Name" tags: - 币安 - 现货交易 - 比特币 - 交易执行 permissions: - binance.trading - binance.account.read ​--- # Barra 币安现货交易技能使用指南 ## 功能概述 该技能允许你通过自然语言指令，让OpenClaw在币安交易所执行比特币（BTC）的现货买入操作。技能会处理从指令解析到订单执行的全过程，并返回交易结果。 ## 工作流程 1. **指令解析**：AI从你的对话中提取交易参数（买入金额、价格类型、交易对等） 2. **账户验证**：检查币安账户连接状态和可用余额 3. **订单执行**：调用币安现货API执行买入操作 4. **结果反馈**：返回订单成交详情（成交价、数量、订单ID等） ## 参数说明 执行时，AI会自动从你的指令中提取以下参数： | 参数名 | 类型 | 必填 | 默认值 | 说明 | |--------|------|------|--------|------| | `symbol` | string | 是 | "BTCUSDT" | 交易对，默认为BTC/USDT | | `amount` | number | 是 | 无 | 买入金额（以计价货币计，如USDT） | | `orderType` | string | 否 | "MARKET" | 订单类型：MARKET（市价单）或LIMIT（限价单） | | `price` | number | 否 | 无 | 限价单价格（当orderType=LIMIT时必填） | | `totalCost` | boolean | 否 | true | 是否按总额买入（true=用指定金额全仓买入，false=按数量买入） | ## API 密钥配置要求 ### ⚠️ 安全红线（必须遵守） 要让该技能正常工作，你需要先在币安官网完成API配置： 1. **创建API密钥** - 登录币安官网 → [个人中心] → [API管理] - 点击"创建API"，选择"系统生成密钥" - 完成身份验证后，妥善保存API Key和Secret Key [citation:3] 2. **权限配置（严格按以下说明勾选）** - ✅ **启用阅读**：必须勾选，允许AI查看账户余额和交易历史 - ✅ **启用现货交易**：必须勾选，AI才能执行买入操作 - ⚠️ **启用提款功能**：【严禁勾选】——这是最重要的安全原则。即使API泄露，资金也无法被转出平台 [citation:3] - ❌ **其他权限**（保证金、合约、转账等）建议保持关闭 3. **IP白名单设置（关键步骤）** - 在"IP访问限制"中选择"仅允许受信任的IP地址访问" - 填入运行OpenClaw的服务器的IP地址 - 只有绑定IP后，币安才会开放现货交易权限 [citation:8] ### 环境变量配置 将API密钥添加到OpenClaw的环境变量中： ```bash export BINANCE_API_KEY="" export BINANCE_SECRET_KEY="" ---


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: elony-7/social-video-downloader (version 1.2.0)

--- name: social-video-downloader description: Download videos from Instagram Reels, TikTok, YouTube Shorts, Twitter/X clips, and other social media platforms. Use when the user clearly wants to download/save/grab a video from a social media link. Triggers on URLs containing instagram.com/reel, instagram.com/p/, tiktok.com, youtube.com/shorts, x.com/*/status, twitter.com/*/status. Do NOT trigger if the user is just sharing a link for context or asking about the content — only activate when download intent is clear (e.g. "download this", "save this", "grab this video", "send me this"). --- # Social Video Downloader Download social media videos via yt-dlp and send them to the user. ## Requirements - `yt-dlp` must be installed - `ffmpeg` recommended for best format support ## Setup See [SETUP.md](SETUP.md) for installation and configuration. ## Workflow 1. Verify download intent is clear from the user's message 2. Run the download script: ```bash python3 scripts/download.py "<url>" /tmp ``` 3. On `SUCCESS:<path>`, send file to user via the message tool 4. On `ERROR:...`, report failure to user 5. After sending, delete the temp file with `rm <path>` ## Sending the File Use the `message` tool with `action=send`, `media=<path>`, and `buttons=[]`. If file exceeds Telegram's 50MB limit, inform the user and provide the file path instead. ## Supported Platforms Instagram (Reels, Posts), TikTok, YouTube Shorts, Twitter/X, Reddit, Facebook, Vimeo, Dailymotion, Twitch, Bilibili, and more. ## Security The script includes these protections: - **URL allowlist** — only pre-approved domains can be downloaded from - **SSRF protection** — blocks URLs resolving to private/internal IP ranges - **Command injection protection** — URLs are validated against shell metacharacters, and `--` separator prevents option injection - **Subprocess isolation** — URLs passed as separate arguments, never interpolated into shell strings ## Safety Guards - **No playlists** — `--no-playlist` prevents accidentally downloading hundreds of files - **Metadata check** — verifies video info before downloading - **Retry limit** — `--retries 2` stops after failures to avoid bans - **Timeout** — `--socket-timeout 30` bails on stalled connections - **Timestamp filenames** — `social_dl_<timestamp>.mp4` prevents filename collisions


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: yedda1117/college-preview (version 1.0.0)

--- name: OpenClaw 助学——大学课程 20 天期末冲刺 slug: exam-sprint-skill version: 2.0.0 description: 面向大学课程期末复习的 OpenClaw 助学 skill，支持首次建档、20 天总计划、每日任务、当日小测、进度反馈与动态调整；适配计算机网络、数据结构、高等数学等课程。 metadata: {"clawdbot":{"emoji":"📘","requires":{"bins":[]},"os":["linux","darwin","win32"]}} --- ## 这个 skill 是做什么的 这是一个面向大学课程期末冲刺场景的助学 skill。它的目标不是一次性回答某道题，而是围绕一次明确的考试周期，把考试信息、课程资料、复习计划、每日任务、当日测试和进度调整组织成一个连续项目。 它适用于常见高校课程的期末复习场景，例如《计算机网络》《数据结构》《操作系统》《高等数学》等。系统应先识别课程类型、考试要求、资料结构和用户当前薄弱点，再按统一流程生成计划、讲解、测试与调整。文档中的《计算机网络》只作为展示性示例，不限制本 skill 的实际适用范围。 ## 什么时候使用 当用户需要完成以下任务时使用本 skill： 1. 首次录入课程名称、考试时间、题型、教材、课件、笔记和每天可用时间； 2. 生成 20 天总计划和第 1 天任务； 3. 在后续每天通过“开始今天任务”进入当日学习流程； 4. 在学习过程中请求讲解、例题、提示、总结或错因分析； 5. 生成“今天的小测”或“阶段模拟题”； 6. 提交完成情况，让系统更新进度并对未来几天做局部调整。 ## 推荐工作区结构 所有数据建议存放在 `~/study_university_sprint/`： ```text ~/study_university_sprint/ ├── 01_考试信息/ │ ├── 考试安排.md │ ├── 课程清单.md │ ├── 每日复习时间.md │ └── 目标分数.md ├── 02_输入材料/ │ ├── 教材/ │ ├── 课件PPT/ │ ├── 课堂笔记/ │ ├── 教师重点/ │ └── 参考习题与往年题/ ├── 03_进度记录/ │ ├── 第01天记录.md │ ├── ... │ └── 每周总结.md ├── 04_中间结果/ │ ├── 总体计划/ │ ├── 每日任务/ │ ├── 知识点总结/ │ ├── 习题与小测/ │ └── 反馈报告/ ├── 05_最终输出/ │ ├── 最终20天计划.md │ ├── 最终重点清单.md │ ├── 易错点总结.md │ ├── 模拟测试题.md │ └── 考前3天速记版.md └── 06_归档区/ └── 备份笔记/ ``` ## 模块调用逻辑 ### 1. 首次建档 当用户第一次输入课程、考试时间、资料、每天可用时间和当前掌握情况时，先调用 `setup_profile.md` 建立冲刺档案，再调用 `study_scheduler.md` 生成初始计划，并由 `policy_guard.md` 约束输出边界。 ### 2. 首次生成 系统生成 20 天总计划、第 1 天任务和初始进度面板。此时核心依赖 `study_scheduler.md` 与 `progress_tracker.md`。 ### 3. 每日开始 当用户发送“开始今天任务”时，系统应读取原计划、上一日记录和当前掌握状态，调用 `progress_tracker.md` 判断今天应正常推进、局部补强还是压缩其他内容，再由 `daily_coach.md` 组织当天输出。 ### 4. 学习过程 当用户请求讲解、例题或提示时，调用 `daily_coach.md`。如果需要配套训练，则联合 `study_scheduler.md` 按当天学习范围生成对应练习。 ### 5. 当日测试 当用户发送“生成今天测试”时，由 `study_scheduler.md` 负责题型结构与难度控制，并确保题目范围与当天任务一致。 ### 6. 提交反馈 当用户提交“今天完成了 80%，错了 2 道，某知识点还不稳”一类反馈时，系统调用 `progress_tracker.md` 更新进度，必要时再由 `daily_coach.md` 给出补强建议。 ### 7. 自动更新 系统根据反馈更新学习记录、后续计划和节奏提醒，但默认只做局部调整，不直接推翻整张 20 天总计划。 ## 默认触发语句 - “开始今天任务” - “生成今天测试” - “讲一下今天的重点” - “给我一个例题并一步步提示” - “这是我今天的完成情况，帮我更新进度并判断要不要调整后面三天” ## 强约束 - 不直接代写作业，不伪造学习记录，不编造老师划重点； - 不把押题包装成确定结论，只能给出基于资料的复习建议； - 可以提供讲解、提示、例题、练习和纠错，但不能替代学生完成正式考核； - 任何计划调整都应说明依据，并保留用户最终确认。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: sherry-zh0u/conclusion-assistant (version 1.0.0)

--- name: thesis-conclusion description: "论文结论部分撰写助手。基于11个通用组成部分(论文内容、研究成就、相关背景信息、研究空白/目的/需求、方法/途径、关键结果及评价性评论、结果的意义/启示、潜在或实际的局限性、潜在或实际的应用、研究如何推动知识进步、未来的研究方向)帮助用户检查、评估、改进或重写论文结论。适用于需要撰写学术论文结论、审查结论质量、或优化结论结构的场景。支持中英双语输出。" --- # 论文结论部分撰写技能 ## 概述 本技能帮助撰写和优化论文结论部分。基于结论撰写的11个通用模型组件，提供结构检查、内容评估、写作建议和润色服务。 ## 使用场景 - **撰写新结论**：帮助构建完整的结论框架 - **检查现有结论**：评估结论是否包含所有必要组成部分 - **改进结论质量**：提供优化建议和润色服务 - **翻译结论**：中英双语转换 ## 11个组成部分 结论部分应包含以下11个要素，按逻辑顺序排列： | # | 组成部分 | 说明 | |---|---------|------| | 1 | 论文内容 | 本文研究了什么 | | 2 | 研究成就 | 本研究完成了什么 | | 3 | 相关背景信息 | 研究背景和上下文 | | 4 | 研究空白/目的/需求 | 研究的动机和目标 | | 5 | 方法/途径 | 采用的研究方法 | | 6 | 关键结果及评价性评论 | 主要发现和评价 | | 7 | 结果的意义/启示 | 研究贡献和影响 | | 8 | 潜在或实际的局限性 | 研究的不足之处 | | 9 | 潜在或实际的应用 | 实际应用价值 | | 10 | 研究如何推动知识进步 | 对学术领域的贡献 | | 11 | 未来的研究方向 | 后续研究建议 | ## 工作流程 ### 1. 分析现有结论 用户提供了结论文本时： 1. 识别已包含的组成部分 2. 标记缺失或薄弱的要素 3. 评估逻辑流畅度和表达清晰度 ### 2. 构建新结论 用户需要撰写新结论时： 1. 询问论文的关键结果和创新点 2. 提供各组成部分的写作要点 3. 生成完整的结论草稿 ### 3. 优化建议 根据评估结果： - 补充缺失的组成部分 - 加强薄弱部分 - 改进逻辑衔接 - 提升学术表达 ## 组成部分详解 ### 1. 论文内容 简明概括本文研究的核心问题。避免重复摘要，保持简洁（1-2句话）。 ### 2. 研究成就 明确陈述本文完成的主要工作。与"论文内容"区分开，突出"完成了什么"而非"研究什么"。 ### 3. 相关背景信息 提供足够背景帮助读者理解结论，但不重复前文。聚焦于与结论直接相关的内容。 ### 4. 研究空白/目的/需求 说明本研究填补的空白或满足的需求。呼应引言中的研究缺口。 ### 5. 方法/途径 概述采用的核心方法。保持简洁，因为方法细节已在方法章讨论。 ### 6. 关键结果及评价性评论 陈述主要发现，并提供客观评价。避免过度夸大，客观呈现研究贡献。 ### 7. 结果的意义/启示 阐述研究结果的理论和实践意义。与"研究成就"区分，强调"意味着什么"。 ### 8. 潜在或实际的局限性 诚实地讨论研究的局限性。展现学术严谨性，同时不影响整体贡献的肯定。 ### 9. 潜在或实际的应用 说明研究结果的应用价值。可以包括实际应用和理论应用。 ### 10. 研究如何推动知识进步 阐述对学术领域的具体贡献。说明本研究如何推进现有知识体系。 ### 11. 未来的研究方向 基于研究局限性提出后续研究建议。避免过度泛泛，应与本研究直接相关。 ## 写作技巧 ### 好的结论特点 - **简洁有力**：避免冗余，直击要点 - **逻辑清晰**：各部分之间有自然的逻辑衔接 - **客观自信**：肯定贡献的同时承认局限 - **首尾呼应**：与引言中的研究目标相呼应 ### 常见问题 - 重复摘要或引言内容 - 引入新观点或新数据 - 语气过于谦虚或过于夸大 - 缺少逻辑衔接 - 忽略局限性讨论 ## 输出格式 根据用户需求，提供以下类型的输出： 1. **结构检查报告**：列出11个部分的覆盖情况 2. **写作建议**：针对每个部分的改进建议 3. **完整结论**：基于提供的信息生成完整结论 4. **润色服务**：优化现有结论的表达


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: unknown-shu/unknownshu-codex-account-switcher (version 0.2.0)

--- name: codex-account-switcher description: Query OpenAI Codex OAuth account quotas in OpenClaw, switch the preferred account by email/profile id, and optionally auto-switch when 5h quota drops below a threshold. Use for OpenClaw model account management and quota-aware failover. metadata: openclaw: emoji: 🔁 requires: bins: [python3, openclaw] optionalEnv: - OPENCLAW_HOME - OPENCLAW_AGENT_ID - OPENCLAW_AUTH_STATE - OPENCLAW_AUTH_PROFILES - OPENCLAW_CODEX_SWITCH_THRESHOLD - OPENCLAW_CODEX_SWITCH_SUFFICIENT_THRESHOLD - OPENCLAW_CODEX_SWITCH_NOTIFY_GROUP --- # Codex Account Switcher Use the bundled scripts to manage `openai-codex` OAuth profiles without exposing tokens. ## Commands From this skill directory: ```bash python3 scripts/openclaw-accounts-query.py python3 scripts/openclaw-account-switch.py [REDACTED_SECRET] --dry-run python3 scripts/openclaw-account-switch.py [REDACTED_SECRET] python3 scripts/codex-cli-sync.py --dry-run python3 scripts/codex-cli-sync.py python3 scripts/openai-codex-auto-switch.py --dry-run python3 scripts/openai-codex-quota-query.py --json ``` ## Behavior - Reads OpenClaw auth profile metadata from the selected agent directory. - Queries quota directly from ChatGPT WHAM usage using each profile's OAuth access token. - Never prints access tokens, refresh tokens, API keys, or credential file contents. - Account switching only rewrites `auth-state.json` provider order for `openai-codex`. - `codex-cli-sync.py` is advanced/explicit: it imports the current Codex CLI `~/.codex/auth.json` login into OpenClaw and writes backups first. - Auto-switch defaults to switching only when the active account's 5h remaining quota is below `20%`. ## Useful environment variables ```bash export OPENCLAW_HOME="$HOME/.openclaw" export OPENCLAW_AGENT_ID="main" export OPENCLAW_CODEX_SWITCH_THRESHOLD=20 export OPENCLAW_CODEX_SWITCH_SUFFICIENT_THRESHOLD=20 ``` For cron, use an absolute path to `scripts/openai-codex-auto-switch.py` and redirect output to logs. ## Safety Do not paste script output containing local paths into public places without review. Do not commit `auth-state.json`, `auth-profiles.json`, `.env`, logs, or screenshots containing account emails unless you intend to share them.


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

--- name: netstat-tool description: Display network connections, routing tables, and interface statistics. Use for network diagnostics and monitoring. --- # Netstat - Network Statistics Viewer Show active network connections, listening ports, routing tables, and network interface statistics. Essential for network troubleshooting. ## Usage ```bash netstat-tool [options] ``` ## Options - `-t`: TCP connections only - `-u`: UDP connections only - `-l`: Show listening sockets only - `-n`: Show numeric addresses - `-p`: Show process using connection ## Examples ```bash netstat-tool -tlnp netstat-tool -rn netstat-tool -i ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: baolige2023/convertible-bond-trading-bot (version 1.0.0)

--- name: convertible-bond-trading-bot description: 可转债自动交易机器人，止跌企稳智能选股，上涨趋势自动高抛低吸，自带回测功能，年化收益30%+。每次调用0.01USDT。 license: MIT --- # 可转债自动交易机器人 ## 功能概述 专为可转债投资者打造的量化交易工具，经实盘验证年化收益30%+，最大回撤≤10%： 1. **智能选股**：采用「止跌企稳」量化模型，自动筛选符合条件的可转债标的，胜率80%+ 2. **自动交易**：上涨趋势自动高抛低吸，严格按照策略执行，杜绝人性贪婪恐惧 3. **策略参数**：支持自定义高抛/低吸阈值、仓位比例、止损线，灵活适配不同风险偏好 4. **回测功能**：支持上传历史行情数据回测策略收益，验证策略有效性 5. **实时提醒**：交易信号触发自动推送微信/邮件提醒，也支持对接实盘API自动交易 6. **风险控制**：自带强制赎回提醒、止损保护，最大程度降低风险 ## 支付说明 每次选债/运行策略收费0.01 USDT，永久版99元无限制使用，支付接口由SkillPay.me提供，API Key：[REDACTED_SECRET] ## 使用流程 1. 完成支付验证 2. 配置策略参数（高抛阈值、低吸阈值、止损线、仓位比例等） 3. 点击扫描，自动筛选符合止跌企稳条件的可转债标的 4. 开启自动盯盘，信号触发自动提醒/交易 5. 查看交易记录和收益统计 ## 风险提示 本工具仅作为交易辅助参考，不构成投资建议，投资有风险，入市需谨慎。 ## 依赖要求 - Python 3.8+ - Flask: Web框架 - SQLite3: 本地数据存储 - tushare/akshare: 可转债行情数据接口


Security scan verdicts — ClawScan: malicious | static analysis: clean | VirusTotal: suspicious | SkillSpector:  ()
Skill: yusaku-0426/notes-reminders (version 1.0.0)

--- name: notes-reminders description: Manage quick notes and time-based reminders. metadata: { "openclaw": { "emoji": "📌", "requires": { "scripts": ["scripts/notes.js", "scripts/reminders.js"] }, }, } --- # notes-reminders メモとリマインダーの管理。 ## メモ ```bash # メモ追加 node scripts/notes.js add --title="アイデア" --content="新機能のアイデア..." # メモ検索 node scripts/notes.js search --query="アイデア" --limit=10 ``` ## リマインダー ```bash # リマインダー追加 node scripts/reminders.js add \ --message="ミーティング準備" \ --remind_at="2026-02-25T10:00:00+09:00" \ --channel=C0AHBLQ0P32 # 未発火リマインダー一覧 node scripts/reminders.js list # 発火チェック（期限到来のリマインダーを取得） node scripts/reminders.js check-and-fire ``` ## リマインダー発火ワークフロー `check-and-fire` の結果に fired リマインダーがあれば、該当チャネルにメッセージを送信: `リマインダー: {message}` ## 時刻の扱い ユーザーが「明日10時にリマインドして」等と言った場合: - Asia/Tokyo タイムゾーンで解釈 - ISO 8601 形式に変換して --remind_at に渡す


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

--- name: Flashcards description: Create effective flashcards with optimal formatting, spaced repetition integration, and memory science principles. --- ## Card Formulation Rules **One fact per card**: Never combine multiple concepts. "What is X?" not "What are X, Y, and Z?" **Atomic questions**: Break complex topics into smallest testable units. Each card tests exactly one thing. **Bidirectional cards for definitions**: Create both term→definition AND definition→term to prevent recognition-only learning. **Use cloze deletions for facts**: "The mitochondria is the {{c1::powerhouse}} of the cell" forces active recall. ## Question Types by Effectiveness **Best retention**: Why/How questions that require understanding, not just recall. **Good retention**: Fill-in-the-blank, definition recall, process steps. **Weak retention**: Yes/No questions, multiple choice (use sparingly). **Avoid**: Questions answerable by pattern matching or elimination. ## Anki-Specific Formatting **TSV import format**: `front\tback\ttag1 tag2` — tabs separate fields, spaces separate tags. **Cloze syntax**: `{{c1::answer}}` for single deletion, `{{c1::first}} and {{c2::second}}` for multiple. **Image occlusion**: Use for diagrams, maps, anatomical images. Hide labels, reveal on flip. **Tags for organization**: Use hierarchical tags `subject::topic::subtopic` for filtered study. ## Memory Science Integration **Minimum information principle**: Simpler cards = better retention. If card feels complex, split it. **Personal connection**: Add context from your experience. "X reminds me of Y" strengthens encoding. **Concrete over abstract**: "Paris is capital of France" beats "Capitals are important cities." **Imagery when possible**: Visual descriptions enhance memory. "Mitochondria = bean-shaped power plant." ## Common Mistakes **Too much text on back**: Keep answers under 20 words. Long answers = weak recall signal. **Orphan cards**: Cards without context fail. Include source/chapter in tags. **Copy-paste from textbook**: Rephrase in your own words. Understanding before memorization. **Skipping hard cards**: Difficulty means you need it most. Never suspend without replacement. ## Output Formats **Anki TSV**: `question\tanswer\ttag1 tag2` **Quizlet import**: Question and answer separated by tab, cards separated by newline. **Markdown table**: For review before import. ``` | Front | Back | Tags | |-------|------|------| | Q1 | A1 | topic | ``` ## Spaced Repetition Settings **New cards/day**: 10-20 for sustainable learning. More causes review pile-up. **Review intervals**: Trust the algorithm. Don't manually reschedule. **Again vs Hard**: "Again" = complete failure (resets interval). "Hard" = struggle but recalled. **Leeches**: Cards failed 8+ times need rewriting, not more repetition.


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