OpenClaw/clawhub-security-signals huggingface dataset:
Skill: luaqnyin/a-stock-paper-trade (version 1.0.1)

--- name: a-stock-paper-trade description: A股模拟炒股系统。当用户需要模拟炒股、查询行情、买卖下单、查看持仓盈亏时触发。支持实时行情、买卖下单、持仓管理、盈亏计算、涨跌排行。关键词：炒股、买入、卖出、持仓、盈亏、行情、涨停、跌停、选股、大盘。 metadata: { "openclaw": { "requires": { "bins": ["python3"] }, "os": ["darwin", "linux", "win32"] } } --- 优化日期: 2026-04-02 # A股模拟炒股 本地纸面交易系统，基于 akshare 实时行情。无需任何外部账号，纯模拟。 ## 快速开始 ```bash # 初始化账户（100万虚拟资金） source ~/.agent-reach-venv/bin/activate python3 {baseDir}/scripts/trader.py init # 查大盘 python3 {baseDir}/scripts/trader.py quote --all # 查个股行情 python3 {baseDir}/scripts/trader.py quote 600519 000001 # 搜索股票 python3 {baseDir}/scripts/trader.py search 茅台 # 买入（1手=100股，市价） python3 {baseDir}/scripts/trader.py buy 600519 1 # 买入（指定价格） python3 {baseDir}/scripts/trader.py buy 600519 2 --price 1700 # 卖出 python3 {baseDir}/scripts/trader.py sell 600519 1 # 查持仓 python3 {baseDir}/scripts/trader.py positions # 账户总览 python3 {baseDir}/scripts/trader.py balance # 交易记录 python3 {baseDir}/scripts/trader.py history --limit 10 # K线 python3 {baseDir}/scripts/trader.py kline 600519 --days 60 # 涨幅TOP10 python3 {baseDir}/scripts/trader.py rank --top 10 # 跌幅TOP10 python3 {baseDir}/scripts/trader.py rank --bottom --top 10 ``` ## 交易规则 - 最小单位：1手 = 100股 - 佣金：万三（最低5元） - 印花税：千一（仅卖出） - 不支持涨跌停价格下单（但当前版本未强制校验，agent 应自行判断） - 数据来源：东方财富（akshare），仅交易时间内有效 ## 费用计算 | 费用 | 买入 | 卖出 | |------|------|------| | 佣金 | 万三（最低5元） | 万三（最低5元） | | 印花税 | — | 千一 | | 过户费 | — | 忽略（模拟简化） | ## 数据存储 - 位置：`~/.openclaw/paper-trade/portfolio.json` - 重置：`python3 {baseDir}/scripts/trader.py init --reset` ## 注意 - 行情数据来自 akshare，依赖网络（不需要代理，东方财富国内直连） - 非 A 股交易时间行情可能不准确 - 所有操作输出 JSON，方便 agent 解析


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ray1012/paper-summary (version 1.0.0)

--- name: paper-summary description: 用于总结学术论文和研究文章。当用户提供 PDF 文件、论文链接或上传论文内容时自动触发。适用于研究论文、学术文章、技术报告等的阅读理解与内容提炼。触发短语：总结论文、论文摘要、阅读报告、文章总结、帮我理解这篇论文。 --- # 论文总结技能 本技能用于快速提取和总结学术论文的核心内容，包括研究背景、方法论、主要发现和结论。 ## 输入要求 支持以下输入方式： 1. **PDF 文件**：直接上传 PDF 文件或提供本地路径 2. **URL 链接**：提供论文的网页或 PDF 直链 3. **文本内容**：直接粘贴论文文本（适用于未加密的论文） ## 工作流程 ### 步骤 1：论文解析 使用 `extract_pdfs_full_content` 或 `extract_pdfs_key_info` 提取论文内容： - 对于完整分析：使用 `extract_pdfs_full_content` 提取全部内容 - 对于快速摘要：使用 `extract_pdfs_key_info` 提取关键信息 ### 步骤 2：内容分析与结构化 根据提取的内容，进行以下维度的分析： 1. **基本信息** - 论文标题 - 作者信息 - 发表时间/期刊 - 研究领域 2. **研究背景** - 研究问题的定义 - 现有研究的局限性 - 研究动机与意义 3. **研究方法** - 采用的研究方法/技术路线 - 数据来源与实验设计 - 分析框架 4. **主要发现** - 核心研究成果 - 关键数据与统计结果 - 重要结论 5. **研究贡献** - 理论贡献 - 实践意义 - 创新点 6. **局限性与展望** - 研究的局限性 - 未来研究方向 ## 输出格式 生成结构化的论文摘要报告，包含以下部分： ### 摘要概览（约 200 字） 一句话概括论文核心价值，3-5 个关键词。 ### 详细内容 按照上述六个维度展开详细分析。 ### 核心要点提炼 列出 3-5 个最重要的 takeaways。 ### 适用场景 说明该论文适合哪类读者，以及可应用于哪些场景。 ## 输出示例 ``` 【论文总结报告】 标题：[论文标题] 作者：[作者信息] 领域：[研究领域] 来源：[期刊/会议/预印本] 【一句话总结】 [用一句话概括论文的核心贡献] 【关键词】 关键词1 | 关键词2 | 关键词3 | 关键词4 | 关键词5 【详细内容】 1. 研究背景 [详细分析...] 2. 研究方法 [详细分析...] 3. 主要发现 [详细分析...] ... 【核心要点】 1. [要点1] 2. [要点2] 3. [要点3] 【阅读建议】 [适合的读者群体和应用场景] ``` ## 使用场景 - 快速筛选论文是否值得精读 - 帮助理解跨领域的研究论文 - 准备文献综述 - 学术写作参考 - 会议/ Seminar 准备


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: fpsjago/binance-dca-test (version 0.0.1)

--- name: binance-dca description: Binance Dollar-Cost Averaging (DCA) tool for automated and manual recurring crypto purchases. Use when the user wants to plan DCA strategies, execute recurring buys on Binance, check DCA projections, view trade history, or manage a systematic buying schedule for any trading pair (BTC/USDT, ETH/USDT, etc). Triggers on requests about DCA, recurring buys, cost averaging, accumulation strategies, or Binance spot purchases. --- # Binance DCA Execute and plan Dollar-Cost Averaging strategies on Binance. ## Setup Requires two environment variables (never hardcode these): ```bash export BINANCE_API_KEY="your-key" export BINANCE_SECRET_KEY="your-secret" ``` Optional: `BINANCE_BASE_URL` (defaults to `https://api.binance.com`). Use `https://testnet.binance.vision` for paper trading. ## Quick Start ```bash # Check current BTC price bash scripts/dca.sh price BTCUSDT # Project a DCA plan: $50 every 7 days, 12 buys bash scripts/dca.sh plan 50 7 12 BTCUSDT # Execute a $50 market buy bash scripts/dca.sh buy BTCUSDT 50 # Check recent trades bash scripts/dca.sh history BTCUSDT 10 # Check USDT balance bash scripts/dca.sh balance USDT ``` ## Actions ### price [SYMBOL] Get current spot price. Default: BTCUSDT. ### balance [ASSET] Check free and locked balance for an asset. Default: USDT. ### buy SYMBOL AMOUNT [TYPE] [PRICE] Place a buy order. AMOUNT is in quote currency (USDT). - `MARKET` (default): Execute immediately at market price - `LIMIT`: Requires price parameter — `buy BTCUSDT 50 LIMIT 95000` ### history [SYMBOL] [LIMIT] Show recent trades with timestamps, side, quantity, price, and fees. ### plan [AMOUNT] [FREQ_DAYS] [NUM_BUYS] [SYMBOL] Project a DCA plan with scenario analysis showing PnL at different price levels (-30% to +100%). Defaults: $50, every 7 days, 12 buys, BTCUSDT. ## DCA Strategy Guidance When helping users plan DCA: 1. **Ask about budget** — How much per buy, and how often? 2. **Set expectations** — DCA smooths volatility, not guaranteed profit 3. **Run projections** — Use `plan` to show scenarios before committing 4. **Recommend testnet first** — Set `BINANCE_BASE_URL=https://testnet.binance.vision` 5. **Position sizing** — Suggest 1-2% of portfolio per buy for conservative approach 6. **Never store credentials** — Always use environment variables ## Scheduling DCA Buys For automated recurring buys, suggest setting up a cron job or OpenClaw cron: ``` # Example: buy $50 BTC every Monday at 9am UTC 0 9 * * 1 BINANCE_API_KEY=... BINANCE_SECRET_KEY=... /path/to/dca.sh buy BTCUSDT 50 ``` Or via OpenClaw cron for agent-managed scheduling with alerts and confirmations. ## Error Handling - Missing API keys → clear error message with setup instructions - Invalid amounts → validation before API call - API failures → descriptive error with endpoint info - Always verify the order response status before confirming to user


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: mrxlolcat/mrxlolcat-agent (version 0.1.0)

--- name: mrxlolcat-agent description: Master AI Automation Agent for the Farcaster ecosystem. Specializes in autonomous on-chain actions, cross-chain bridging via LI.FI, and long-term FID memory management. license: MIT compatibility: Web3, Farcaster, Base, Optimism allowed-tools: Fetch, LI.FI, Neynar, Pinecone, TTS metadata: version: 3.0.0 author: MRX LOLCAT homepage: https://mrxlolcat-agent.vercel.app image: https://mrxlolcat-agent.vercel.app/logo.jpg --- # MRX LOLCAT Agent 🤠✨ You are the master orchestrator of the MRX LOLCAT system. Your identity is defined as a chaotic-good cowboy cat living on the Base network. ## Unified Capabilities Your skills are divided into specialized modules located in `src/agent/skills/`. 1. **Bridge Logic**: Uses `bridge-skill` to move assets across 60+ chains. 2. **Monitor Logic**: Uses `monitor-skill` for real-time wallet tracking. 3. **Social Logic**: Uses `social-skill` for Farcaster hub interactions. 4. **Micropayment Logic**: Uses `agentcash-skill` for paid API access. ## Core Rules - Always maintain the **Cowboy Cat** persona (lowercase, emoji-rich). - Prioritize **Base** and **Optimism** for all operations. - Reference your long-term memory in Pinecone for recurring users. - Never request private keys; redirect users to the terminal UI for signing. ## Technical Access - Metadata: [agent.json](https://mrxlolcat-agent.vercel.app/agent.json) - MCP Endpoint: [api/mcp](https://mrxlolcat-agent.vercel.app/api/mcp) - Terminal: [analytics](https://mrxlolcat-agent.vercel.app/analytics)


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: yangkai258/openclaw-rescue-instances (version 1.0.0)

# openclaw-rescue-instances OpenClaw 多实例救援 Gateway 快速创建技能。按照官方文档规范，一键创建隔离的救援实例。 --- ## 📖 描述 基于 OpenClaw 官方多实例指南 (`docs/gateway/multiple-gateways.md`)，自动化创建隔离的救援 Gateway 实例。 **适用场景：** - 创建救援 Bot（主实例故障时备用） - 隔离不同环境（开发/生产） - 多配置测试 - 端口隔离需求 --- ## 🎯 能力 | 能力 | 说明 | |------|------| | 创建救援实例 | 一键创建隔离的 Gateway 实例 | | 端口自动分配 | 自动计算不冲突的端口 | | 配置隔离 | 独立的配置文件、状态目录、工作目录 | | LaunchAgent 集成 | 自动注册 macOS 开机自启服务 | | 批量创建 | 支持一次性创建多个实例 | --- ## 🚀 使用方法 ### 创建单个救援实例 ```bash # 默认创建（端口自动分配） openclaw agent --message "创建一个救援实例，端口 19001" # 指定端口 openclaw agent --message "创建救援实例，端口 20000" ``` ### 创建多个救援实例 ```bash # 批量创建 openclaw agent --message "创建 3 个救援实例，端口从 19001 开始" ``` ### 删除救援实例 ```bash openclaw agent --message "删除救援实例 rescue2" ``` --- ## 📋 配置参数 | 参数 | 默认值 | 说明 | |------|--------|------| | `base_port` | 19001 | 起始端口 | | `instance_count` | 1 | 创建实例数量 | | `port_spacing` | 1000 | 端口间隔（避免派生端口冲突） | | `disable_wecom` | true | 是否禁用企业微信（避免冲突） | | `copy_plugins` | false | 是否复制插件配置 | --- ## 📁 创建的文件 每个救援实例会创建以下内容： ``` ~/.openclaw-rescueN/ ├── openclaw.json # 独立配置文件 ├── workspace/ # 独立工作目录 ├── sessions/ # 独立会话数据 ├── credentials/ # 独立凭证 ├── logs/ │ ├── gateway.log │ └── gateway.err.log └── agents/ # 独立 Agent 数据 ~/Library/LaunchAgents/ └── ai.openclaw.gateway-rescueN.plist # LaunchAgent 服务 ``` --- ## ⚙️ 隔离配置 每个实例完全隔离以下内容： | 配置项 | 主实例 | 救援实例 | |--------|--------|----------| | 配置文件 | `~/.openclaw/openclaw.json` | `~/.openclaw-rescueN/openclaw.json` | | 状态目录 | `~/.openclaw/` | `~/.openclaw-rescueN/` | | 工作目录 | `~/.openclaw/workspace` | `~/.openclaw-rescueN/workspace` | | 端口 | 8080 | 19001, 20000, ... | | Service | `ai.openclaw.gateway` | `ai.openclaw.gateway-rescueN` | --- ## 🔧 维护命令 ### 查看状态 ```bash # 查看所有实例 launchctl list | grep openclaw.gateway # 健康检查 for port in 8080 19001 20000 30000 40000; do curl -sk https://localhost:$port/health && echo " ($port)" done ``` ### 重启实例 ```bash # 重启单个 launchctl kickstart -k gui/$UID/ai.openclaw.gateway-rescue1 # 重启所有 for name in gateway gateway-rescue1 gateway-rescue2; do launchctl kickstart -k gui/$UID/ai.openclaw.$name done ``` ### 查看日志 ```bash tail -f ~/.openclaw-rescue1/logs/gateway.log ``` --- ## ⚠️ 注意事项 1. **端口间隔**：基础端口之间至少留 20 个端口，推荐 1000+（避免 browser/canvas/CDP 派生端口冲突） 2. **企业微信冲突**：多个实例不能用同一个 Bot ID 同时连接企业微信，会互相踢。建议： - 只在主实例启用企业微信 - 或为每个实例配置不同的企业微信应用 3. **API 配额**：共享 API Key 时，配额是所有实例共用的 4. **磁盘空间**：每个实例约占用 100-500MB（取决于会话和缓存） --- ## 📖 官方文档参考 - [Multiple Gateways](/gateway/multiple-gateways) - [Gateway Configuration](/gateway/configuration) - [CLI Gateway](/cli/gateway) --- ## 📝 变更记录 | 日期 | 版本 | 说明 | |------|------|------| | 2026-03-27 | 1.0.0 | 初始版本，支持单实例和批量创建 | --- ## 💡 示例对话 ``` 用户：创建一个救援实例 助手：好的，正在创建救援实例 rescue1... - 配置文件：~/.openclaw-rescue/openclaw.json - 端口：19001 - 服务：ai.openclaw.gateway-rescue 创建完成！实例已启动。 用户：再创建两个，端口从 20000 开始 助手：正在创建 rescue2 (20000) 和 rescue3 (30000)... 完成！两个实例都已启动。 用户：删除 rescue2 助手：正在删除救援实例 rescue2... - 卸载服务：ai.openclaw.gateway-rescue2 - 删除配置：~/.openclaw-rescue2/ 完成！ ```


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: worm-tribe/quanyu-tech-director (version 0.1.1)

--- name: quanyu-tech-director description: 权舆科技软件研发总监技能。项目经理(小缺)在派发任务、跟踪进度、处理超时、汇报结果时激活。 --- # 软件研发总监 ## 核心原则 1. 项目经理是调度者，不是执行者 2. 写/改代码、改CSS、改配置(>1行)、构建部署、代码审查 → 必须派Agent 3. 预估超过5分钟的任务 → 必须拆分 4. Agent超时 → 检查中间产物 → 派小任务续做，**绝对不自己接手** 5. 每次派发/完成/超时/失败 → 更新台账 + 向创始人汇报 ## 任务派发流程 1. 分析指令，确定任务类型和粒度 2. 【强制】写入 TASK-TRACKER.json(status: dispatched) 3. 派发给Agent(设置对应timeout) 4. 向创始人汇报："已安排XX做YY,预计Z分钟" 5. 等待结果(不做其他长操作) **不写台账 → 不派发。这是硬性规则。** ## 任务类型速查 | 类型 | 时长 | timeout | |------|------|---------| | DEV-TINY | 1-2分钟 | 120秒 | | DEV-SMALL | 2-4分钟 | 180秒 | | DEV-MEDIUM | 4-8分钟 | 300秒 | | DEV-LARGE | **必须拆分** | - | | REVIEW-QUICK | 2-3分钟 | 180秒 | | REVIEW-STANDARD | 4-8分钟 | 300秒 | | DEPLOY-UPDATE | 2-4分钟 | 180秒 | | DEPLOY-NEW | 5-10分钟 | 300秒 | ## 超时处理 Agent超时 → 检查中间产物 → 更新台账(status: timed_out) → 评估完成度 → 派合适任务续做 ## 自检机制(PIT-037) 执行任何操作前问自己： 1. 写/改代码（tsx/ts/css/html）？ 2. 执行 build（pnpm build）？ 3. 执行部署（复制 static、启动服务）？ 如果是 → **停！派给 Agent。** ## 角色边界红线 可以：分析拆解任务、读文件查状态、写管理文档/台账、回答创始人 不可以：写/改任何代码/CSS/HTML、构建部署、代码审查、陷入长操作 ## 参考规范 - 完整规范：`knowledge-repos/guides/task-dispatch-mechanism.md` - 产品开发流程：`knowledge-repos/guides/product-dev-workflow.md` - 踩坑登记册：`knowledge-repos/knowledge/internal/pitfall-registry.md` - Git 提交规范：`knowledge-repos/knowledge/general/git-commit-spec.md` - **技能管理规范**：`knowledge-repos/guides/skill-management.md`（创建/编辑技能必读）


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: 54meteor/message-split (version 1.0.0)

--- name: message-split description: "长消息自动拆分 skill。当回复内容超过渠道限制时，自动将消息拆分为多条有序发送，避免截断或丢消息。Auto-split long messages into smaller chunks before sending, with sequence markers." --- # Message Split Skill ## Problem Many messaging channels (Feishu, Telegram, etc.) have a per-message character limit (~4000 chars for Feishu). When a response exceeds this limit, it either gets truncated or silently fails, leaving the user with no feedback. ## Solution Before sending any message, check its length and split if necessary. ## Usage ```python def send_long_message(text, channel="{active_channel}"): """ Send a message, splitting it into multiple chunks if it exceeds the length limit. Args: text: The message text to send channel: Target channel (feishu/telegram/discord/whatsapp/signal/imessage/openclaw-weixin) Returns: Number of chunks sent """ MAX_LEN = 3600 # Feishu limit with margin CHUNK_HEADER = "[{i}/{total}]\n" if len(text) <= MAX_LEN: message(action="send", channel=channel, message=text) return 1 chunks = split_text(text, MAX_LEN) total = len(chunks) for i, chunk in enumerate(chunks, 1): header = f"[{i}/{total}]\n" if total > 1 else "" message(action="send", channel=channel, message=header + chunk) return total def split_text(text, max_len): """ Split text into chunks of at most max_len characters. Attempts to split at sentence boundaries or line breaks for readability. """ import re # Try to split at sentence-ending punctuation first sentence_split = re.split(r'(?<=[。！？.!?])\s+', text) chunks = [] current = "" for sentence in sentence_split: if len(current) + len(sentence) + 1 <= max_len: current += (" " + sentence if current else sentence) else: if current: chunks.append(current) # If single sentence exceeds limit, split by words/characters if len(sentence) > max_len: for i in range(0, len(sentence), max_len - 100): chunks.append(sentence[i:i + max_len - 100]) current = "" else: current = sentence if current: chunks.append(current) return chunks ``` ## Channel Limits Reference | Channel | Max chars (approx) | Notes | |---------|-------------------|-------| | Feishu | 4000 | Hard limit | | Telegram | 4096 | | | Discord | 2000 | Embed limit 6000 | | WhatsApp | 65000 | But relayed messages get truncated | | Signal | 700 | Very low | | iMessage | ~4000 | Via macOS relay | ## Notes - Always use `MAX_LEN = 3600` as a safe default (leaves room for header) - If channel is unknown, default to feishu behavior - Splitting is done on word/sentence boundaries when possible to preserve readability - Sequence headers `[{i}/{total}]` are only added when `total > 1`


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

--- name: oraclaw-decide description: Decision intelligence for AI agents. Analyze options, map decision dependencies with PageRank, detect when information sources conflict, and find the choices that matter most. version: 1.0.0 metadata: openclaw: requires: env: - ORACLAW_API_KEY primaryEnv: ORACLAW_API_KEY emoji: "🎯" homepage: https://oraclaw.dev/decide tags: - decision-making - strategy - analysis - dependency-graph - consensus - intelligence price: 0.05 currency: USDC --- # OraClaw Decide — Decision Intelligence for Agents You are a strategic decision agent that uses graph analysis, convergence scoring, and optimization to make and analyze decisions. ## When to Use This Skill Use this when the user or another agent needs to: - Choose the best option from competing alternatives - Map dependencies between decisions and find bottlenecks - Check if multiple information sources agree or conflict - Identify which decisions have the highest ripple effect - Find the critical path through a complex project ## Tools Available ### `optimize_bandit` — Choose the Best Option Given options with historical performance, select the one with highest expected value. ### `optimize_contextual` — Context-Aware Decisions Choose differently based on the current situation (time pressure, stakes, complexity). ### `analyze_decision_graph` — Map & Analyze Decision Networks Feed in decisions as nodes and relationships as edges. Get back: - **PageRank**: Which decisions are most influential? - **Communities**: Which decisions cluster together? - **Bottlenecks**: What's blocking everything? - **Critical path**: Shortest route from start to goal ### `score_convergence` — Are Your Sources Agreeing? When you have multiple forecasts, estimates, or opinions — score how much they agree. Detects outliers automatically. ## Example: Project Decision Analysis ```json { "nodes": [ { "id": "hire", "type": "decision", "label": "Hire senior dev", "urgency": "critical", "confidence": 0.4, "impact": 0.9, "timestamp": 1711350000 }, { "id": "ship", "type": "goal", "label": "Ship v2.0", "urgency": "critical", "confidence": 0.5, "impact": 1.0, "timestamp": 1711350000 }, { "id": "fundraise", "type": "decision", "label": "Start fundraise", "urgency": "high", "confidence": 0.6, "impact": 0.8, "timestamp": 1711350000 } ], "edges": [ { "source": "hire", "target": "ship", "type": "enables", "weight": 0.9 }, { "source": "ship", "target": "fundraise", "type": "enables", "weight": 0.8 } ], "sourceGoal": "hire", "targetGoal": "fundraise" } ``` ## Rules 1. For graph analysis: nodes need all required fields (id, type, label, urgency, confidence, impact, timestamp) 2. Edge weights should be 0-1 (higher = stronger relationship) 3. Convergence scoring works best with 3+ sources 4. When sources disagree significantly (spread > 2000 bps), investigate the outlier before deciding ## Pricing $0.05 per analysis call (USDC on Base via x402). Free tier: 100 decisions/month with API key.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: 52yuanchangxing/study-revision-planner (version 1.0.0)

--- name: study-revision-planner description: Convert a syllabus, exam scope, or course notes into a revision calendar with spaced review, mock tests, and weak-point loops. version: 1.1.0 metadata: openclaw: requires: bins: - python3 emoji: 🧰 --- # Study Revision Planner ## Purpose Convert a syllabus, exam scope, or course notes into a revision calendar with spaced review, mock tests, and weak-point loops. ## Trigger phrases - 复习计划 - exam revision planner - 课程大纲变计划 - spaced repetition schedule - 安排模拟测试 ## Ask for these inputs - syllabus or topics - exam date - weekly availability - difficulty by topic - preferred study block length ## Workflow 1. Split the scope into study units and estimate effort. 2. Generate a calendar with learn, review, and test phases. 3. Insert spaced review loops and buffer days. 4. Highlight overload weeks and propose tradeoffs. 5. Return a printable plan and a concise today-next checklist. ## Output contract - revision calendar - topic effort table - mock test schedule - today-next checklist ## Files in this skill - Script: `{baseDir}/scripts/revision_schedule.py` - Resource: `{baseDir}/resources/study_plan_template.csv` ## Operating rules - Be concrete and action-oriented. - Prefer preview / draft / simulation mode before destructive changes. - If information is missing, ask only for the minimum needed to proceed. - Never fabricate metrics, legal certainty, receipts, credentials, or evidence. - Keep assumptions explicit. ## Suggested prompts - 复习计划 - exam revision planner - 课程大纲变计划 ## Use of script and resources Use the bundled script when it helps the user produce a structured file, manifest, CSV, or first-pass draft. Use the resource file as the default schema, checklist, or preset when the user does not provide one. ## Boundaries - This skill supports planning, structuring, and first-pass artifacts. - It should not claim that files were modified, messages were sent, or legal/financial decisions were finalized unless the user actually performed those actions. ## Compatibility notes - Directory-based AgentSkills/OpenClaw skill. - Runtime dependency declared through `metadata.openclaw.requires`. - Helper script is local and auditable: `scripts/revision_schedule.py`. - Bundled resource is local and referenced by the instructions: `resources/study_plan_template.csv`.

path: CHANGELOG.md
content: # Changelog

## 1.0.0
- Initial release.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: susankerrbq/easy-movie-search (version 1.0.0)

--- name: movie-search description: 搜索电视剧、动漫、电影的百度网盘和夸克网盘资源链接。当用户询问任何影视作品的网盘链接、资源、下载地址时使用此技能，包括但不限于"有XX的网盘链接吗"、"XX在哪里下载"、"找XX的资源"等问题。 --- # 影视资源搜索技能 当用户询问电视剧、动漫或电影的百度网盘或夸克网盘链接时，使用此技能。 ## 触发条件 用户询问包含以下关键词的内容： - 电视剧/电视/剧集 + 网盘/百度/夸克/链接/资源 - 动漫/动画 + 网盘/百度/夸克/链接/资源 - 电影 + 网盘/百度/夸克/链接/资源 ## 使用方法 1. 从用户的问题中提取影视作品的名称（keyword） 2. 调用 API 接口获取资源信息 3. 格式化展示结果给用户 ## API 接口 - 接口地址：`https://meng-ge.top/api/movieData/getMoviesByType` - 请求方法：GET - 请求参数： - `page`: 页码（默认 1） - `size`: 每页数量（默认 50） - `keyword`: 影视作品名称（需要 URL 编码） ## 响应格式 ```json { "code": 0, "timestamp": 1773299629789, "message": "操作成功", "data": [ { "id": 2605, "movieName": "逐玉.1080P更 15", "baiduLink": "https://pan.baidu.com/s/xxx?pwd=1120", "quarkLink": "https://pan.quark.cn/s/xxx", "type": "TV", "updateTime": "2026-03-12 11:55:10", "hash": "EaM7XP", "hot": true } ] } ``` ## 展示格式 当获取到结果后，按以下格式展示： ``` 找到 [影视名称] 的资源： 1. [movieName] - 类型：[type 转换为中文] - 百度网盘：[baiduLink] - 夸克网盘：[quarkLink] - 更新时间：[updateTime] 2. ... ``` 类型映射： - TV: 电视剧 - TV_4K: 电视剧（4K） - MOVIE: 电影 - MOVIE_4K: 电影（4K） - ANIME: 动漫 - ANIME_4K: 动漫（4K） ## 注意事项 1. keyword 需要进行 URL 编码 2. 如果没有找到结果（data 为空数组），告知用户未找到相关资源 3. 如果 API 返回错误（code 不为 0），告知用户查询失败 4. 优先展示热门资源（hot 为 true） 5. 如果有多个版本（1080P、4K等），都展示给用户选择


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: staratheris/staratheris-arya-reminders (version 0.1.0)

--- name: arya-reminders description: Recordatorios en lenguaje natural (Bogotá). Crea cron jobs seguros y registra en markdown (y opcionalmente Sheets). metadata: openclaw: emoji: "⏰" requires: bins: ["bash", "python3"] --- # Arya Reminders Recordatorios en lenguaje natural para OpenClaw, diseñados para Jaider. ## Qué hace - Interpreta fechas/horas relativas y absolutas en español (y formatos comunes). - Usa **America/Bogota** por defecto. - Crea recordatorios **one-shot** (una sola vez) como cron jobs. - Registra cada recordatorio en `memory/reminders.md`. - (Opcional futuro) registrar en Google Sheets cuando esté habilitado. ## Uso (conversacional) Ejemplos: - "Recuérdame pagar la luz mañana a las 3pm" - "Recuérdame en 45 minutos revisar el horno" - "Recuérdame hoy a las 5:30pm llamar a mamá" - "Recuérdame el viernes a las 9am entregar el taller" ## Comandos (manual) ### Crear recordatorio (una vez) ```bash bash skills/arya-reminders/create-reminder.sh "Mensaje" "Cuándo" ``` ### Revisar log ```bash cat memory/reminders.md ``` ## Notas - No requiere APIs externas. - Usa el tool `cron` del Gateway (no hardcodea rutas ni IDs ajenos).


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