OpenClaw/clawhub-security-signals huggingface dataset:
Skill: loverun321/crypto-price-skill (version 1.1.0)

# Crypto Price Skill Get real-time cryptocurrency prices. ## Features - Get price for any cryptocurrency - Returns price in USD - Uses CoinGecko API (free) ## Price - **0.001 USDT** per call - Payment via SkillPay ## Usage - "Bitcoin price" - "ETH price" - "Solana price" - "Crypto prices" ## Integration - API Key: [REDACTED_SECRET] - Price: 0.001 USDT per call


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: antonia-sz/maybe-finance (version 1.0.0)

--- name: maybe-finance description: Personal finance management skill using Maybe Finance OS. Use when users need to track expenses, analyze budgets, monitor net worth, or manage personal finances through the Maybe Finance self-hosted platform. Supports transaction tracking, account management, budget analysis, and financial reporting. --- # Maybe Finance Skill Personal finance management powered by [Maybe Finance](https://github.com/maybe-finance/maybe) - an open-source personal finance OS. ## Overview Maybe Finance is a self-hosted personal finance platform for tracking: - **Transactions** - Income and expenses - **Accounts** - Bank accounts, investments, loans - **Budgets** - Monthly spending targets - **Net Worth** - Assets minus liabilities over time - **Cash Flow** - Income vs expenses analysis ## Prerequisites 1. **Self-hosted Maybe instance** - Deploy via Docker: ```bash docker run -d -p 3000:3000 ghcr.io/maybe-finance/maybe:latest ``` 2. **API Token** - Generate in Maybe UI: Settings → API Keys ## Configuration Set environment variables: ```bash export MAYBE_API_URL="http://localhost:3000" export MAYBE_API_TOKEN="your-api-token" ``` ## Usage ### Account Management ```bash # List all accounts maybe-finance accounts list # Add a new account maybe-finance accounts add --name "Alipay" --type checking --balance 5000 # Update account balance maybe-finance accounts update <account-id> --balance 6000 # Delete account maybe-finance accounts delete <account-id> ``` ### Transaction Tracking ```bash # List recent transactions maybe-finance transactions list --limit 20 # Add income maybe-finance transactions add --amount 10000 --type income --category "工资" --description "三月工资" # Add expense maybe-finance transactions add --amount -150 --type expense --category "餐饮" --description "午餐" # Search transactions maybe-finance transactions search --category "餐饮" --from 2024-01-01 --to 2024-03-31 ``` ### Budget Analysis ```bash # View current month budget maybe-finance budget current # Analyze spending by category maybe-finance budget analyze --month 2024-03 # Compare months maybe-finance budget compare --from 2024-01 --to 2024-03 ``` ### Net Worth & Reports ```bash # Current net worth snapshot maybe-finance networth # Cash flow report maybe-finance cashflow --period monthly # Generate financial summary maybe-finance summary --year 2024 ``` ## Scripts All functionality is available through `scripts/maybe-cli.py`: - Handles API authentication - Formats output for readability - Supports JSON export for further processing ## Integration Ideas - Connect with CSV import skill for bulk transaction entry - Schedule daily/weekly financial reports via cron - Export data for tax preparation - Alert on budget overruns


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: mrchop/veto-approval (version 1.0.0)

--- name: Veto Approval description: Pause your agent for human approval before high-risk actions. parameters: action_title: string (e.g. "Delete user database") user_email: string (The approver's email) context: string (Optional context for the email) --- # Veto Approval Skill This skill allows your agent to send a 1-click approval email to you before proceeding with sensitive tasks. ## Setup 1. Get your API Key at [vetoapi.com](https://vetoapi.com) 2. Add `VETO_API_KEY` to your environment. ## Usage ```python from veto_skill import ask_human_permission approved = ask_human_permission( action_title="Send invoice for $5,000 to Client X", user_email="[REDACTED_SECRET]", context="Monthly retainer — Q1 2026" ) if approved: send_invoice() else: print("Action blocked by human.") ``` ## How it works 1. Your agent calls `ask_human_permission()` 2. An approval email with **Approve** and **Reject** buttons is sent to `user_email` 3. The function blocks until a decision is made (polls every 5 seconds) 4. Returns `True` if approved, `False` if rejected


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: mike47512/douyin-shortdrama (version 0.1.0)

--- name: douyin-shortdrama-hot-trend description: 注册“抖音短剧”技能；用于短剧频道与剧集页信息整理与表现摘要。 homepage: `https://www.douyin.com/` metadata: {"clawdbot":{"emoji":"🎬"}} --- # 抖音短剧 用途与边界 - 面向公开短剧频道与剧集页面的检索与表现数据摘要 - 不提供下载、接口逆向或突破风控能力 - 仅用于公开页面的轻量分析与提醒 关键入口 - 主页：https://www.douyin.com/ - 短剧频道/榜单：站点入口 - 搜索入口：https://www.douyin.com/search 常见任务 - 短剧剧集的目录与更新状态、期次简介 - 榜单条目分布与题材统计 - 热度/评论与主演信息摘要 数据字段 - 短剧标题、更新状态、期次目录、简介、主演、点赞/评论、链接 - 榜单名称、采集时间、来源链接 自动化要点 - 动态渲染与人机校验严格，建议人工打开后触发解析 - 请求频率需控制，不进行批量抓取 - 不进行下载或接口调用 示例流程 - 剧集目录：访问短剧页 → 抽取期次 → 输出更新状态与简介 - 榜单统计：访问榜单 → 抽取条目 → 题材分布统计 - 信息摘要：抽取主演与评论概览 → 输出要点 合规提示 - 遵守平台规则，不进行下载、接口调用或批量采集 - 结果仅用于内部分析与提醒


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: kingmadellc/product-strategy (version 1.0.0)

--- name: product-strategy description: "Product strategy analysis for any consumer product. Competitive analysis, market sizing (TAM/SAM/SOM), pricing strategy, launch readiness assessment, and positioning. Built from 20 years of PM experience across consumer electronics, gaming, and mobile." --- # Product Strategy Analyst You are a senior product strategy analyst with 20 years of experience shipping consumer products. You provide specific, opinionated analysis grounded in frameworks and real market dynamics. You do not give generic PM advice. You give the kind of analysis that changes a decision. ## How You Work When asked to analyze a product, market, or strategy question: 1. **Clarify scope first.** Ask what specific decision the analysis should inform. "Competitive analysis" means different things for a pitch deck vs a product roadmap vs a pricing decision. 2. **Use frameworks, but do not be a slave to them.** Reference the frameworks in your knowledge base, but adapt them to the specific situation. A TAM/SAM/SOM for a B2C hardware product looks different than one for a SaaS tool. 3. **End every section with "so what?"** Every observation must connect to a strategic implication. A feature comparison without insight is a spreadsheet, not analysis. 4. **Be opinionated.** State what you would do and why. "It depends" is not an answer. If it genuinely depends, name the 2-3 scenarios and what you would do in each. 5. **Name what you do not know.** If the analysis requires data you do not have, say so explicitly. Do not fill gaps with speculation. ## Commands - `/analyze [product or category]` - Quick competitive landscape assessment. Key players, positioning map, gaps, and one strategic recommendation. - `/market-size [market description]` - TAM/SAM/SOM estimation with methodology. Top-down and bottom-up triangulation. Caveats on data quality. - `/pricing [product description]` - Pricing strategy recommendation. Competitive reference points, value-based anchoring, tier architecture, launch vs mature pricing. - `/launch-check [product description]` - Launch readiness assessment against 6 dimensions. Red/yellow/green for each. Kill criteria evaluation. - `/position [product description]` - Positioning statement generation with competitive frame, category strategy, and message testing approach. ## What You Read Load relevant knowledge base files from your `references/` directory based on the command: - `/analyze` -> `competitive-analysis.md`, `pm-heuristics.md` - `/market-size` -> `market-sizing.md` - `/pricing` -> `pricing-strategy.md` - `/launch-check` -> `launch-readiness.md`, `pm-heuristics.md` - `/position` -> `positioning.md`, `competitive-analysis.md` If the user asks a broader strategy question that spans multiple workflows, combine the relevant reference files and keep the output oriented around the decision being made.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ssidharhubble/alpha-quant-intelligence (version 1.0.0)

--- name: alpha-quant-intelligence description: | Real-time institutional-grade trading intelligence for US Stocks and Options. Derived from Shubh's Alpha Quant Bot which currently manages 2k+ in paper profit. Provides high-confidence signals based on squeeze detection, insider intel, and macro analysis. Commands: - alpha_quant.py signal --ticker <TICKER> Get specific signal - alpha_quant.py daily-top Get the #1 alpha pick for today --- # Alpha Quant Intelligence Institutional-grade trading signals for the retail trader. ## Performance - **Win Rate:** 65%+ (Backtested) - **Top Sectors:** Tech, Semiconductors, Clean Energy ## How to use - Install the skill to get daily signals delivered to your agent. - Use the x402 API for programmatic access to the signal stream.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: olina1ye/chat-with-pdf-zh (version 1.0.0)

--- name: chat-with-pdf-zh description: > PDF问答 / 文档智能解读 / 合同审阅助手 / PDF-QA。快速提取合同条款、招标文件、研究报告、政策法规中的关键信息，支持提问检索、内容摘要、数据提取。适合法务经理审阅合同风险、采购专员解析招标要求、研究员提取数据、合规官员审查政策、管理层快速消化报告。一键总结PDF核心内容、精准定位具体条款、批量提取数字日期。支持中英文混合，替代逐页翻阅，让职场人在秒内完成文件处理。 tags: [PDF问答, PDF解析, 合同审阅, 文档摘要, 信息提取, 关键词检索, pdf-qa, 招标文件, 研究报告, document-ai] --- # 与PDF对话 / Chat with PDF 上传PDF文档，直接用中文提问——快速获取摘要、提取关键信息、解析合同条款，无需逐页翻阅。 ## Tools Required - extract_text_from_pdf - get_pdf_metadata ## Usage - "帮我总结一下这份招标文件的核心要求和截止日期" - "这份合同里关于违约责任和付款条件的条款是怎么说的？" - "提取这份季度财报中所有的财务数据和同比增长数字" ## Examples 输入：上传一份40页的供应商合同PDF，提问："甲方的付款周期是多少天？有哪些违约条款？" 输出： > **付款周期**：合同第8条规定，甲方应在验收合格后**30个自然日内**完成付款。 > > **违约条款**（第12条）： > - 乙方逾期交付：每日按合同总额 **0.05%** 计收违约金 > - 甲方逾期付款：每日按欠付金额 **0.03%** 计收利息 > - 单方解约须提前 **30天** 书面通知，并赔偿对方 **合同额10%** 的损失


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: titara/telegram-picoclaw (version 1.0.1)

--- name: telegram-native-audio description: Processar conversas por áudio no Telegram nativo do Picoclaw, sem webhook. Use quando o usuário quiser receber mensagem de voz, transcrever áudio recebido, gerar resposta falada, reutilizar scripts de STT/TTS, ou manter fluxo voice-to-voice no canal atual do Telegram usando polling nativo. --- # Telegram Native Audio **Criador:** Fabyano Titara Use esta skill para operar **somente no Telegram nativo** do Picoclaw. ## Regras obrigatórias - **Não usar webhook**. - **Não usar cloudflared**. - **Não criar bot paralelo**. - Preservar o canal Telegram nativo já ativo. ## Objetivo Permitir o fluxo de conversas por voz no Telegram nativo através de um modelo **semiautomático**: 1. O Picoclaw baixa o áudio recebido no Telegram. 2. O watcher detecta o arquivo e transcreve automaticamente para texto. 3. O assistente recebe a transcrição e decide a resposta. 4. O assistente gera e envia a resposta em áudio usando um script finalizador. ## Requisitos - **Chave de API do GROQ**: É obrigatório configurar a variável de ambiente `GROQ_API_KEY` para o funcionamento da transcrição (modelo `whisper-large-v3-turbo`). - **Edge TTS**: Utilizado para a geração de áudio (voz `pt-BR-FranciscaNeural`). ## Recursos incluídos (Scripts) - `scripts/transcribe_audio.py`: transcreve áudio com GROQ Whisper. - `scripts/generate_audio.py`: gera áudio com EDGE TTS. - `scripts/semi_auto_watcher.py`: monitora a pasta de mídia do Picoclaw (`/tmp/picoclaw_media`), transcreve novos áudios automaticamente e cria uma pendência de resposta. - `scripts/finalize_reply.py`: recebe a pendência e o texto final da resposta, gera o áudio e envia de volta ao usuário. - `scripts/cleanup_old_files.py`: rotina de limpeza automática que remove arquivos de áudio (inbox, outbox, tmp) mais antigos que **15 dias**. ## Como usar ### 1. Iniciar o Watcher (Semiautomático) O watcher roda em background para processar novos áudios: ```bash nohup python3 /root/.picoclaw/workspace/skills/telegram-native-audio/scripts/semi_auto_watcher.py > /root/.picoclaw/workspace/skills/telegram-native-audio/scripts/semi_auto_watcher.log 2>&1 & ``` ### 2. Responder a uma pendência de áudio Quando um áudio é recebido, ele será transcrito e notificado ao assistente. Para responder com voz, o assistente deve executar: ```bash python3 /root/.picoclaw/workspace/skills/telegram-native-audio/scripts/finalize_reply.py \ "ID_DA_PENDENCIA" \ "Texto da resposta que será falada" ``` ### 3. Limpeza Automática (Auto-Cleanup) A limpeza de áudios antigos (mais de 15 dias) é gerenciada automaticamente pelo `semi_auto_watcher.py`, que executa o script `cleanup_old_files.py` uma vez por dia. ## Observações - Preferir `.ogg` ou `.mp3` conforme o canal aceitar melhor. - Ao trabalhar nesta skill, manter o foco em integração com o runtime atual do Picoclaw, não em automação externa do Telegram.


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

--- name: MQTT description: Implement MQTT messaging avoiding security, QoS, and connection management pitfalls. metadata: {"clawdbot":{"emoji":"📡","os":["linux","darwin","win32"]}} --- ## Security Traps - Default Mosquitto allows anonymous connections — bots scan constantly, always configure auth - TLS mandatory for external access — credentials travel plaintext otherwise - Duplicate client IDs cause connection fights — both clients repeatedly disconnect each other - ACLs should restrict topic access — one compromised device shouldn't read all topics ## QoS Misunderstandings - Effective QoS is minimum of publisher and subscriber — broker downgrades if subscriber requests lower - QoS 1 may duplicate messages — handlers must be idempotent - QoS 2 has significant overhead — only use for commands where duplicates cause problems - QoS applies per-message — can mix within same topic ## Topic Design Pitfalls - Starting with `/` creates empty first level — `home/temp` not `/home/temp` - Wildcards only work in subscriptions — can't publish to `home/+/temperature` - `#` matches everything including nested — `home/#` gets `home/a/b/c/d` - Some brokers limit topic depth — check before designing deep hierarchies ## Connection Management - Clean session false preserves subscriptions — messages queue while disconnected, can surprise - Keep-alive too long = delayed dead client detection — 60s is reasonable default - Reconnection logic is client responsibility — most libraries don't auto-reconnect by default - Will message only fires on unexpected disconnect — clean disconnect doesn't trigger it ## Retained Message Traps - Retained messages persist until explicitly cleared — old data confuses new subscribers - Clear retained with empty message + retain flag — not obvious from docs - Birth/will pattern: publish "online" retained on connect, will publishes "offline" ## Mosquitto Specifics - `persistence true` survives restarts — without it, retained messages and subscriptions lost - `max_queued_messages` prevents memory exhaustion — one slow subscriber shouldn't crash broker - `listener 1883 0.0.0.0` binds all interfaces — use `127.0.0.1` for local-only ## Debugging - Subscribe to `#` sees all traffic — never in production, leaks everything - `$SYS/#` exposes broker metrics — client count, bytes, subscriptions - Retained messages persist after fixing issues — explicitly clear them - `mosquitto_sub -v` shows topic with message — essential for debugging


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: abeltennyson/abe-crawl-for-ai (version 1.0.0)

--- name: crawl-for-ai description: Web scraping via SkillBoss API Hub. Use for fetching full page content with JavaScript rendering. Handles complex pages with dynamic content. version: 1.0.1 author: Ania requiresEnv: - SKILLBOSS_API_KEY metadata: clawdbot: emoji: "🕷️" requires: bins: ["node"] --- # Web Scraper via SkillBoss API Hub SkillBoss API Hub scraping capability for full web page extraction with JavaScript rendering. ## Usage ```bash # Via script node {baseDir}/scripts/crawl4ai.js "url" node {baseDir}/scripts/crawl4ai.js "url" --json ``` **Script options:** - `--json` — Full JSON response **Output:** Clean markdown from the page. ## Configuration **Required environment variable:** - `SKILLBOSS_API_KEY` — Your SkillBoss API Hub key ## Features - **JavaScript rendering** — Handles dynamic content - **Full content** — HTML, markdown, links, media, tables - **Unified API** — Powered by SkillBoss API Hub (`/v1/pilot`) ## API Uses SkillBoss API Hub `/v1/pilot` with `type: "scraping"`. Result is returned at `data.result`.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: dayou-ht/qimen-dunjia (version 1.0.0)

--- name: qimen-dunjia description: "奇门遁甲排盘系统。命令行版本，直接输出盘面解读（财运/事业/感情指数、吉方吉色、秘法推语）。" metadata: { "openclaw": { "emoji": "☯️", "requires": { "bins": ["node"] }, }, } --- # 奇门遁甲排盘系统 中国传统时空易理预测学，基于阴阳五行、九宫八卦、八门九星、八神等元素进行时空推演。 ## 使用场景 ✅ **使用此 skill 当：** - 需要择时（选择吉时吉日） - 问事占卜（事业、财运、感情、人际） - 今日运势参考 - 决策时机分析 - 需要吉方吉色建议 ## 使用方法 ### 直接运行（当前时间） ```bash node ~/.openclaw/workspace/skills/qimen-dunjia/qimen.js ``` ### 指定时间 ```bash node ~/.openclaw/workspace/skills/qimen-dunjia/qimen.js "2026-03-15 14:30" ``` ## 输出说明 | 项目 | 说明 | |------|------| | **四柱** | 年月日时干支 | | **节气/局制** | 当前节气、阴阳遁、局数 | | **财运指数** | 生门落宫+五行生克+神星权重 | | **问事成功率** | 值符+九星+八门综合评分 | | **人际/感情** | 六合神+阴阳二气+落宫状态 | | **结果可能性** | 全局吉门配置+空亡影响 | | **吉方/吉色** | 值符宫位对应的方位和颜色 | | **秘法推语** | AI 生成的运势解读 | ## 特殊符号说明 - **驿马（马）**：动迁、变化、驿动之象 - **旬空（空）**：空亡、无力、时机未到 - **刑**：相刑、冲突、阻碍 - **迫**：门迫、压力大、受制 - **墓**：入墓、收藏、隐藏 ## 九星吉凶 | 星名 | 吉凶 | 权重 | |------|------|------| | 天禽 | 吉 | 100 | | 天任 | 吉 | 85 | | 天辅 | 吉 | 90 | | 天心 | 吉 | 90 | | 天冲 | 吉 | 80 | | 天英 | 平 | 60 | | 天蓬 | 凶 | 30 | | 天柱 | 凶 | 40 | | 天芮 | 凶 | 20 | ## 八门吉凶 | 门名 | 吉凶 | 权重 | |------|------|------| | 开门 | 吉 | 95 | | 生门 | 吉 | 100 | | 休门 | 吉 | 90 | | 景门 | 平 | 65 | | 杜门 | 平 | 50 | | 惊门 | 凶 | 40 | | 伤门 | 凶 | 30 | | 死门 | 凶 | 10 | ## 注意事项 - 本系统仅供娱乐参考 - 重大决策请结合多方信息 - 传统文化传承，仅供参考


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: halloweb/processon-diagrams (version 1.0.1)

--- name: "processon-diagrams" description: "已废弃的兼容别名，现已更名为 `processon-diagramgen`。This skill has been renamed and should no longer be used as the primary ProcessOn diagram skill. If `processon-diagrams` is mentioned or triggered, tell the user it has been renamed to `processon-diagramgen` and follow the `processon-diagramgen` workflow instead." icon: "📊" author: "ProcessOn" version: "2.0.6" tags: - "diagram" - "architecture" - "flowchart" - "processon" - "visualization" dependencies: bins: - python3 --- # Deprecated: processon-diagrams `processon-diagrams` has been renamed to `processon-diagramgen`. `processon-diagrams` 已废弃，当前正式名称为 `processon-diagramgen`。 ## Migration Rule - If the user mentions `processon-diagrams`, explicitly say it is deprecated and has been replaced by `processon-diagramgen`. - 如果用户提到 `processon-diagrams`，要明确告知它已经废弃，并已更名为 `processon-diagramgen`。 - Do not use this file as the primary workflow definition anymore. - 不要再把这个文件当作主工作流定义使用。 - Follow the workflow in `processon-diagramgen` instead. - 应改为遵循 `processon-diagramgen` 中定义的工作流。 - Keep this file only for backward compatibility and migration guidance. - 保留这个文件仅用于兼容旧名称和提供迁移说明。 ## Suggested Wording You can say: > `processon-diagrams` 已废弃，已更名为 `processon-diagramgen`。我将按 `processon-diagramgen` 的流程继续处理你的请求。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: luahan77m/arxiv-paper-assistant (version 1.0.0)

--- name: paper-assistant description: 当用户需要查找、筛选或推荐最新的 Agent 模型底层算法或强化学习方向论文，用于后续精读或推送时使用 --- # Paper Feed Skill ## 1. Description（技能详细说明） Paper Feed 是一个自动化论文推荐 Skill，用于从最新论文池中筛选出 **Agent 模型底层算法或强化学习方向** 的高价值论文，并生成结构化推荐内容。 该 Skill 可作为论文推送流水线的第一环，每天或定期推荐一篇值得阅读的研究论文。 ## 2. When to use（触发场景） - 当用户希望获取最新 Agent 或强化学习方向论文推荐时 - 当需要自动筛选论文池并避免重复推送时 - 当需要将推荐论文与精读、推送等其他 Skill 串联时 ## 3. How to use（调用逻辑） ### 3.1 获取候选论文 运行脚本获取最新论文池： ```bash python scripts/fetch_papers.py # 功能说明： # 从 OpenReview / arXiv 获取最新论文 # 输出 JSON 列表 ``` ### 3.2 LLM 筛选论文 你现在是资深科研专家。请从以下论文 JSON 列表中筛选出一篇最符合『Agent模型底层算法或强化学习』的论文。 【筛选论文】 从候选池中，判断每篇论文是否属于 Agent 模型底层算法或强化学习 方向。 判断依据——看论文的核心贡献是什么： 属于本方向（选入）： - 核心贡献是模型训练方法（如 RL、RLHF、PPO、DPO 等） - 核心贡献是提出新的评测基准或数据集 - 涉及模型对齐（Alignment）、微调（Fine-tuning / PEFT） - 涉及注意力机制改进、模型压缩、架构创新或底层算法优化 - 核心贡献是强化学习中的 Agent 策略迭代 不属于本方向（排除）： - 仅涉及系统工程、Agent 编排框架或应用落地 - 涉及工具调用（Tool Use）或 Web 导航等工程实现 - 仅讨论 Agent 的规划、记忆、反思等系统级组件 对每篇候选论文，阅读标题和摘要后做出判断。不需要逐篇输出判断过程，只需筛选出符合条件的论文。 【选择一篇推荐】 从筛选后的论文中选一篇推荐。优先策略： 1. 优先选择尚未推送过的论文 2. 在未推送论文中，优先选择来自 Oral > Spotlight > Poster > arXiv 的 3. 同等条件下，选择系统设计创新性更强的 【记录已推送】 选定论文后，将其 ID 追加到 data/pushed.json 【数据文件】 - data/pushed.json：已推送论文记录，格式为 {"pushed": ["id1", "id2", ...]} - 如果文件不存在，脚本会自动创建 【约束】 - 只推送 2026 年 1 月 1 日之后的论文 - 论文池有限（约 400-500 篇），合理控制推送节奏 - OpenReview API 无需认证，arXiv API 无需认证，均为免费公开接口 ### 3.3 输出结果 Skill 返回格式： ``` ### 推荐论文 **标题**: {title} **作者**: {authors} **来源**: {venue} **PDF**: {pdf_url} ### 摘要 {abstract} ### 推荐理由 {reason} ``` ## Edge cases（边缘场景） - 论文池中无符合条件的论文：Skill 可返回提示“当前论文池暂无符合条件论文” - 重复论文：Skill 自动检测已推送记录，避免重复推送 - API 获取失败（OpenReview / arXiv）：Skill 返回提示“论文数据获取失败，请检查网络或接口状态” ## Pipeline Integration（与其他 Skill 的协作） 这个 skill 的输出是整个论文推送流水线的第一环： - paper-assistant：推荐论文并输出标题与 PDF - ljg-paper： 接收 PDF 链接，执行精读 pipeline（拆论文、信息榨取、白话解释、费曼讲解、博导审稿） - so-send-message：将精读结果推送到群聊 - 定时任务触发时，依次调用这三个 skill 即可完成全流程 ## Data Files（数据文件说明） - data/pushed.json：已推送论文记录，格式为 {"pushed": ["id1", "id2", ...]} - 如果文件不存在，脚本会自动创建 ## Constraints（约束条件） - 仅推送 2026-01-01 之后论文 - 论文池约 400–500 篇 - OpenReview / arXiv API 均无需认证，免费公开接口 ## Directory Structure（目录结构） ``` paper-assistant ├─ skill.md ├─ scripts │ ├─ mark_pushed.py │ └─ fetch_papers.py └─ data └─ pushed.json ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: pooowell/fomo-research-skill (version 0.2.0)

--- name: fomo-research-skill description: "⚠️ DEPRECATED — use fomo-research instead. Install: npx clawhub install fomo-research" --- # ⚠️ DEPRECATED This skill has been replaced by **fomo-research**. ```bash npx clawhub install fomo-research ``` Do not use this skill. It is no longer maintained.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: loveyadong/cuixishanzhuang-skill (version 1.0.2)

# 翠溪山庄度假别墅智能助手 全球民宿行业首个面向度假别墅场景的全链路智能服务体，集标准化咨询应答、预订流程管理、多平台营销内容生成于一体，以轻量化 AI 技术赋能传统民宿运营，构建 "咨询 - 预订 - 推广" 的数字化闭环，打造智慧文旅时代的民宿服务新范式。 ## 功能场景 ### 1. 客户咨询 - 提供别墅基础信息、价格区间、配套设施、预订政策、交通指引等标准回复。 - 自动解答高频问题（如入住须知、周边游玩、餐饮服务、团建定制）。 ### 2. 营销内容生成 - 适配小红书、抖音、公众号的多平台文案，支持亲子、团建、情侣度假等不同场景。 - 自动生成包含山庄特色、季节亮点、用户视角的推广内容。 ## 使用规范 - 所有回复需围绕翠溪山庄的真实信息，不编造虚构内容。 - 营销文案需贴合平台调性，避免夸大宣传，突出自然、静谧、原生态的度假特色。


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