OpenClaw/clawhub-security-signals huggingface dataset:
Skill: fwangzil/safe-flow-sui-skill (version 1.0.0)

--- name: safe-flow-sui-skill description: Use when running SafeFlow against the shared Sui package with owner-assisted provisioning. Trigger for tasks such as creating an agent execution address with local Sui CLI, asking owner to fund gas and finish web-side wallet/session setup, saving walletId/sessionCapId for autonomous payments, syncing package id to SQL, and running Publish API plus Walrus end-to-end tests. --- # Using SafeFlow Shared Contract Operate this as a **test skill** for real-world owner/agent collaboration. Default Publish API endpoint: - `https://producer.safeflow.space` ## Quick Start (Owner-Handoff, Recommended) 1. Bootstrap agent context and owner handoff instructions: ```bash cd .claude/skills/safe-flow-sui-skill/scripts chmod +x ./*.sh ./bootstrap_owner_handoff.sh \ --package-id 0xcc76747b518ea5d07255a26141fb5e0b81fcdd0dc1cc578a83f88adc003a6191 \ --portal-url https://dash.safeflow.space ``` 2. Ask owner to: - fund agent gas; - open the portal URL and finish wallet pre-deposit/config; - return with `walletId` and `sessionCapId`. 3. Save owner-provided runtime config: ```bash ./save_owner_config.sh \ --wallet-id <WALLET_ID> \ --session-cap-id <SESSION_CAP_ID> ``` 4. Execute payment under SafeFlow controls: ```bash ./execute_payment.sh --recipient <RECIPIENT_ADDRESS> --amount 1000000 ``` ## Publish API E2E Test (Intent + Walrus Upload) When user gives a real API URL, run: ```bash ./test_publish_api_flow.sh \ --publish-api-base-url <PUBLISH_API_BASE_URL> \ --recipient <RECIPIENT_ADDRESS> ``` This flow will: 1. call Publish/Producer API health endpoint; 2. create intent; 3. run the agent consumer once (`e2e_runner.ts --once`); 4. rely on SDK `executePaymentWithEvidence` to upload reasoning blob to Walrus (or fallback when degraded); 5. print final `intentId`, status, digest, and blob id. ## SQL Sync for Package ID Sync package id for AI runtime lookup: ```bash ./sync_package_id_to_sql.sh --driver sqlite ``` Use `--driver postgres --postgres-dsn <DSN>` when needed. ## Progressive Disclosure References Load only what is needed: - Owner handoff workflow: `references/owner-handoff-flow.md` - Publish API test workflow: `references/publish-api-test-flow.md` - SQL sync details: `references/sql-sync.md` - Troubleshooting: `references/troubleshooting.md`


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: seichris/xiaohongshu-search-mcp (version 1.0.0)

--- name: xiaohongshu-search-mcp description: Search XiaoHongShu / RED notes through the hosted whatson.red MCP endpoint when users need structured RED search results, note details, or a draft blog payload. version: 1.0.0 metadata: openclaw: requires: env: - WHATSON_RED_API_KEY primaryEnv: WHATSON_RED_API_KEY envVars: - name: WHATSON_RED_API_KEY required: true description: whatson.red Agent API key used as the Bearer token for MCP requests. homepage: https://www.whatson.red/agents --- # XiaoHongShu Search MCP Use this skill when the user wants to search XiaoHongShu / RED / RedNote and receive structured JSON results through the hosted whatson.red MCP endpoint. ## Requirements - The user needs a whatson.red account with an Agent API key from https://www.whatson.red/agents. - Read the API key from `WHATSON_RED_API_KEY`. - Send the key as `Authorization: Bearer ${WHATSON_RED_API_KEY}`. - MCP requests use account-based whatson.red billing and credits. ## MCP Endpoint Use the Streamable HTTP endpoint: ```text https://www.whatson.red/api/agent/mcp ``` Primary tool: ```text search_xiaohongshu_notes ``` ## Tool Arguments - `query` string, required: Search query in English, Chinese, or pinyin. - `limit` number, optional: 1-100, default 60. Values above 20 fetch additional search pages. - `include_note_details` boolean, optional: Default true. Includes note detail payloads when available. - `include_blog` boolean, optional: Default false. Returns a draft blog/article payload without publishing it. Use `limit` values such as 40, 60, 80, or 100 when the user needs more than one page of notes. Note details are best-effort for the returned real notes. ## Expected Response The tool returns structured content with: - `query` - `translatedQuery` - `searchQuery` - `notes` - `noteDetails` - `blog` - `blogError` - `usage` `usage` includes charged credits, remaining credits, and whether the request was already charged through idempotency. ## REST Alternative For non-MCP callers, whatson.red also exposes: ```text POST https://www.whatson.red/api/agent/search ``` REST supports API-key billing and accountless x402 payment. x402 is REST-only; do not use x402 for MCP. REST x402 searches cost $0.05 USDC per raw search on supported USDC networks, and `includeBlog=true` is not available through x402. ## Safety Notes - Do not ask the user for Browserless, proxy, cookie, or deployment secrets; this skill only uses the hosted whatson.red MCP endpoint. - Do not attempt to scrape XiaoHongShu directly from this skill. - Do not publish posts when `include_blog` is true; the returned blog payload is only a draft.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: nopedijah/sports-betting-analyzer (version 1.0.0)

--- name: sports-betting-analyzer description: 智能体育彩票分析助手 - 基于数据分析和简单机器学习的比赛预测辅助工具。支持NBA、足球世界杯等赛事分析，提供数据收集、基础统计、概率预测和投注建议。重点在于辅助决策，而非预测结果。 license: mit version: 1.0.0 --- # 体育彩票分析助手 智能体育彩票分析助手 - 基于数据分析和简单机器学习的比赛预测辅助工具。 ## 核心功能 ### 1. 数据采集与分析 - 收集比赛基础数据（历史战绩、近期走势、主客场表现） - 赔率监测（多平台对比、异常检测） - 伤病信息跟踪 - 历史数据存储与分析 ### 2. 智能预测模型 - 多维度特征提取 - 简单机器学习预测 - 概率计算与置信度评估 - 价值投注识别 ### 3. 辅助决策 - 风险评估与提示 - 投注建议生成 - 可解释性分析 - 资金管理建议 ## 支持赛事 ### NBA - 季后赛分析 - 主客场优势评估 - 热门球队vs冷门球队分析 - 四阶段预测（首轮、次轮、分区决赛、总决赛） ### 足球 - 欧洲五大联赛 - 世界杯赛事 - 欧亚指数分析 - 进球数预测 ## 使用方法 ### 分析一场比赛 ``` 分析 NBA 比赛 湖人 vs 勇士 分析足球比赛 巴萨 vs 皇马 ``` ### 查看历史记录 ``` 查看最近的分析记录 我的投注历史 ``` ### 获取预测建议 ``` NBA 今晚有什么值得投注的比赛 足球周末推荐 ``` ## 数据来源 使用开源数据源： - 体育官方数据（NBA官网、足球官方） - 免费数据API - 历史比赛记录 ## 预测方法论 ### 1. 基础统计 - 历史交锋记录 - 近期胜率（近5-10场） - 主客场表现 - 进球失球数据 ### 2. 特征工程 - 球队实力指数 - 近期状态指数 - 主客场优势系数 - 伤病影响因子 ### 3. 概率模型 - 逻辑回归基础预测 - 蒙特卡洛模拟（简单版） - 集成多因素权重 ### 4. 风险控制 - 置信度评估 - 赔率价值判断 - 建议投注比例 ## 重要提示 ⚠️ **本工具仅提供辅助分析，不保证预测准确率** - 体育比赛存在不确定性 - 建议结合自身判断 - 理性投注，控制风险 - 不要依赖单一工具决策 ## 配置 创建配置文件 `config/sports-betting.json`： ```json { "risk_level": "conservative", "default_bet_percentage": 2, "preferred_leagues": ["NBA", "Premier League", "La Liga"], "data_sources": ["official", "free_api"] } ``` ## 输出格式 分析报告包含： - 📊 数据概览 - 🎯 预测结果（含概率） - 💡 投注建议 - ⚠️ 风险提示 - 📝 可解释性说明 ## 模板 报告模板：`templates/analysis_report.md` ## 更新日志 ### v1.0.0 (2026-04-02) - MVP版本发布 - 支持NBA和足球基础分析 - 简单预测模型 - 辅助决策功能

path: _meta.json
content: skill_name: sports-betting-analyzer
version: 1.0.0
author: 黄杨
description: 智能体育彩票分析助手 - 基于数据分析和简单机器学习的比赛预测辅助工具
created_at: 2026-04-02T01:48:00Z
last_updated: 2026-04-02T01:48:00Z

path: requirements.txt
content: numpy>=1.21.0

path: venv/lib/python3.12/site-packages/numpy/_core/include/numpy/arrayobject.h
content: #ifndef NUMPY_CORE_INCLUDE_NUMPY_ARRAYOBJECT_H_
#define NUMPY_CORE_INCLUDE_NUMPY_ARRAYOBJECT_H_
#define Py_ARRAYOBJECT_H

#include "ndarrayobject.h"

#endif  /* NUMPY_CORE_INCLUDE_NUMPY_ARRAYOBJECT_H_ */


path: venv/lib/python3.12/site-packages/pip-24.0.dist-info/top_level.txt
content: pip


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: bbniao100/brand-sentinel (version 1.0.0)

--- name: brand-sentinel description: > 品牌舆情哨兵：搜索公开平台的品牌相关信息，自动去重和时效过滤，输出结构化结果供 LLM 做风险分级和预警。 触发场景：监控某品牌/产品的公开舆情、搜索负面信息并按时间筛选、定时巡查品牌口碑、竞品舆情对比。 关键词：品牌监控、舆情搜索、负面信息、口碑巡查、舆情预警、品牌声誉、sentinel、舆情哨兵。 不做的事：不做风险分级（交给 Agent/LLM）、不做预警推送（交给 cron）、不做特定行业适配。 --- # brand-sentinel — 品牌舆情哨兵 搜索公开平台的品牌相关信息 → 去重 → 时效过滤 → 输出结构化结果。 ## 核心脚本 `scripts/sentinel.py` — 纯 Python（无第三方依赖），调用 AutoGLM Web Search API。 ## 依赖 - Python 3.10+ - 本地 AutoGLM Token 服务运行中（`http://127.0.0.1:18432/get_token`） - `certifi` 包（SSL 证书，通常已预装） ## 用法 ### 命令行 ```bash # 基本用法 python3 scripts/sentinel.py --brand "特斯拉" --keywords "刹车失灵,自燃" --hours 48 # JSON 输出（供 LLM 消费） python3 scripts/sentinel.py --brand "瑞幸咖啡" --keywords "食品安全,蟑螂" --hours 24 --output json # 配置文件模式 python3 scripts/sentinel.py --config references/config-example.json ``` ### 参数 | 参数 | 必填 | 说明 | |------|------|------| | `--brand` | 是* | 品牌名，如 "特斯拉" | | `--keywords` | 是* | 逗号分隔的关键词，如 "刹车失灵,自燃" | | `--hours` | 否 | 时效窗口（小时），默认 48 | | `--output` | 否 | `text`（默认）或 `json` | | `--config` | 是* | JSON 配置文件路径（替代上方参数） | \* `--brand`+`--keywords` 与 `--config` 二选一。 ### 配置文件格式 见 `references/config-example.json`： ```json { "brand": "品牌名", "keywords": ["关键词1", "关键词2"], "hours": 48, "output": "json" } ``` ## 输出结构 ### JSON 模式 ```json { "brand": "特斯拉", "run_time": "2026-04-14 19:00:00", "time_window_hours": 48, "keywords": ["刹车失灵", "自燃"], "stats": { "raw_count": 85, "kept_count": 62, "expired_count": 8, "no_date_count": 15 }, "items": [ { "title": "页面标题", "url": "页面链接", "snippet": "摘要内容", "date_status": "recent|expired|unknown", "parsed_date": "2026-04-14|null" } ] } ``` ### 字段说明 - `date_status`: - `recent` — 解析到日期且在时效窗口内 ✅ - `expired` — 解析到日期但超出时效窗口（已被过滤掉） - `unknown` — 未解析到日期（搜索词含"最新"限定，大概率近期，默认保留） - `parsed_date` — 从标题/摘要提取的日期，解析失败为 `null` ## 工作流程 1. **构建搜索词** — 对每个关键词自动生成 3 种变体：原始词、`+ 最新`、`+ 当前年月` 2. **批量搜索** — 调用 autoglm-websearch，每个词间隔 0.3s 3. **URL 去重** — 同一 URL 只保留首次出现 4. **日期解析** — 从标题+摘要中提取日期（支持"2026年4月14日"、"3天前"、"昨天"等中文格式） 5. **时效过滤** — 超出时间窗口的结果丢弃，无日期的默认保留 6. **输出** — text 或 json 格式 ## 典型 Agent 工作流 ``` 用户: "帮我看看特斯拉最近有什么负面新闻" 1. Agent 调用 sentinel.py: python3 scripts/sentinel.py --brand "特斯拉" --keywords "刹车失灵,自燃,自动驾驶事故,降价维权" --hours 48 --output json 2. Agent 读取 JSON 输出，用 LLM 判断: - 哪些是真正的负面/危机 - 风险分级（普通/关注/严重/危机） - 是否需要预警 3. Agent 生成人类可读的摘要推送给用户 ``` ## 注意事项 - 搜索 API 有速率限制，关键词数量建议 ≤10 组（每组会生成 3 个变体，共 30 次搜索） - 日期解析基于正则匹配，覆盖率约 60-70%，未解析到的默认保留 - 搜索词加"最新"限定可提升近期结果命中率，但不能保证 100% 时效性 - 如需跨次运行去重，Agent 可自行维护 URL 集合


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: sfkislev/adobe-flue (version 1.0.0)

--- name: Adobe description: Control Adobe desktop apps - Photoshop, Illustrator, Premiere, After Effects, InDesign, Audition - from the shell via Flue, without an MCP server. --- This skill lets a coding agent operate **Adobe desktop applications** through [Flue](https://github.com/SFKislev/flue), a local shell-to-application bridge. The agent pipes ExtendScript into a per-app bridge process and gets structured JSON back. Flue covers Photoshop, Illustrator, Premiere Pro, After Effects, InDesign, and Audition through a consistent stdin/stdout contract. > agent shell -> `<app>_bridge.py` -> ExtendScript -> JSON result ## When to use The human requests work performed **inside an Adobe desktop app** — inspect the open document or project, make bounded edits, run small ExtendScript steps. A CLI agent cannot act inside these apps without a bridge like Flue. ## How to proceed 1. **Read `FLUE.md` in this directory first.** It explains how Flue works, the bridge contract, a worked ExtendScript example, and safety expectations. 2. **Identify which Adobe app the human is working in** — Photoshop, Illustrator, Premiere, After Effects, InDesign, or Audition — and use the matching adapter (e.g. `adapters/photoshop_adapter/photoshop_bridge.py`). 3. **If Flue is installed:** also read `C:\Users\fredd\.claude\skills\flue\SKILL.md` and the matching `adapters/<app>_adapter/APP.md` for the operational contract, then work from there. 4. **If Flue is not installed:** tell the human. The install command is `pip install flue && flue setup`. Do not install without explicit approval in the current session. ## Other apps Flue also supports Blender, Houdini, Autodesk 3ds Max, Unity, and Microsoft Office through the same stdin/stdout contract. One install covers all adapters. ## Safety Flue acts inside professional desktop software. Treat the human as the driver; prefer small, inspectable steps; avoid destructive actions unless explicitly requested. Flue is MIT-licensed and reviewable on GitHub / PyPI before use.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: lgugeng/cron-model-manager (version 1.0.0)

--- name: cron-model-manager description: | 管理 OpenClaw 定时任务：查看、切模型、启用/禁用、手动触发、删除。 触发词："定时任务"、"cron"、"切换模型"、"查看定时任务"、"定时任务管理" --- # Cron 定时任务管理器 ## 前置检查 用 `exec` 运行 `openclaw cron list`。如果报错： - `Unrecognized key: "toolCall"` → `exec` 运行 `sed -i '/"toolCall": true/d' ~/.openclaw/openclaw.json` 后重试 - 其他错误 → 告知用户手动修复 ## 查看任务 用 `exec` 运行 `openclaw cron list`，直接展示输出表格。如果需要更详细信息，加 `--json` 解析。 ## 切换模型 1. 展示当前状态 2. 输出方案：`任务名: 旧模型 → 新模型` 3. 确认后执行：`exec: openclaw cron edit <id> --model <model>` 4. 验证：`exec: openclaw cron list` 确认生效 ## 启用/禁用 - 启用：`exec: openclaw cron edit <id> --enable` - 禁用：`exec: openclaw cron edit <id> --disable` ## 手动触发（测试） `exec: openclaw cron run <id>` 切模型后建议立刻触发一次验证效果。 ## 删除任务 先展示任务详情，用户确认后： `exec: openclaw cron rm <id>` ## 获取可用模型 `exec: openclaw cron list --json` 中提取已有 model 字段，或直接问用户。 ## 注意事项 - model 格式：`provider/model`（如 `custom/mimo-v2.5-pro`） - 修改立即生效，下次执行使用新模型 - 必须通过 CLI 命令操作，不要直接改 jobs.json


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: enihsago/sbti-claw (version 1.0.0)

--- name: sbti description: SBTI 恶搞人格测试。给用户出一组抽象搞笑的测试题，根据回答生成一个离谱的人格类型和解读。触发词：SBTI、人格测试、性格测试、测一测、做个测试。当用户提到 SBTI、想做人格测试、或有人分享了 SBTI 结果时触发。 --- # SBTI — 没有任何科学依据的人格测试 恶搞版 MBTI。问一堆离谱问题，给你一个更离谱的人格标签。 ## 测试流程 1. 告诉用户："来，做个没有任何科学依据的SBTI测试吧！一共10道题，凭直觉选。" 2. 从 `references/questions.md` 随机抽取 10 道不重复的题目（每题2-4个选项）。 3. **逐题提问**，等用户回复选项后记录得分，再出下一题。 4. 10题结束后，根据得分映射到人格类型（见 `references/types.md`）。 5. 生成结果卡片，格式如下： ``` 🎯 你的SBTI人格类型：[类型代号] — [类型名] [一段300字以内的幽默人格解读，要够损够搞笑，结合互联网梗] ✨ 关键特征： • [特征1] • [特征2] • [特征3] 🔥 建议搭配人格：[另一个类型] 💀 死对头人格：[另一个类型] — 本测试没有任何科学依据，结果仅供参考，不对你的人生负责 — ``` ## 出题风格 - 问题要**抽象、发疯、精神状态堪忧** - 选项要**让人纠结又好笑** - 参考孙吧语录、打工人梗、社畜日常、互联网名场面 - 不要正经，不要严肃，不要像真正的心理测试 - 中文提问，中文结果 ## 得分计算 每道题的每个选项对应不同人格维度的加分。10题结束后汇总各维度得分，取最高维度组合确定类型。 维度： - **C** (Chaos) 混乱程度 - **S** (Social) 社交能量 - **E** (Emotion) 情绪浓度 - **W** (Workaholic) 卷王指数 最终类型由最高的2个维度字母组合决定，如 CE、SW、EW 等。具体映射见 `references/types.md`。 ## 注意事项 - 每次测试的题目要随机，不要每次都一样 - 解读要个性化，不要套模板，根据用户的具体回答加点料 - 如果用户半途放弃，已回答的题也可以出结果，但要说"你果然是XX型，连测试都做不完" - 保持轻松搞笑的氛围


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: malenconiaprincep/webpage-screenshot (version 1.0.0)

--- name: webpage-screenshot description: 打开指定网页并截图为图片文件。在用户要求对某 URL 截图、保存网页为图片、或需要网页快照时使用。 --- # 网页截图 ## 何时使用 - 用户给出一个 URL，要求「打开并截图」或「保存网页为图片」 - 需要网页快照、页面留档或可视化结果时 ## 方式一：运行项目脚本（推荐，得到文件） 使用项目内 TypeScript 脚本，可保存为本地图片文件： ```bash # 在项目根目录执行 npm run screenshot -- <URL> [输出路径] ``` 示例： ```bash npm run screenshot -- https://example.com # 默认保存为 ./screenshot.png npm run screenshot -- https://example.com ./output/page.png ``` **前置条件**：已执行 `npm install` 且已安装浏览器：`npx playwright install chromium`。 ## 方式二：使用 MCP 浏览器 若仅需在对话中「看到」页面内容而不必保存为文件，可使用 Cursor 的 browser MCP： 1. `browser_navigate` 打开目标 URL 2. `browser_snapshot` 获取页面结构（相当于当前状态的“快照”信息） 注意：MCP 浏览器不直接生成截图文件，适合快速查看与交互。 ## 输出 - 脚本方式：在指定路径生成 PNG 截图（默认 `screenshot.png`）。 - 截图为整页（含滚动区域），视口宽度默认 1280px。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: loutai0307-prog/ai-product-description-generator-free (version 2.0.1)

--- name: ai-product-description-generator-free description: "Generate product descriptions using free AI backends: Ollama (local, offline) or HuggingFace Inference API (free online). Use when creating e-commerce copy without paid API keys, running product description generation offline, or testing AI copy tools for free." version: "2.0.1" author: BytesAgain homepage: https://bytesagain.com source: https://github.com/bytesagain/ai-skills tags: [ecommerce, product, description, free, ollama, huggingface] env: - name: HF_TOKEN required: false description: "HuggingFace API token (optional, free at huggingface.co)" --- # AI Product Description Generator (Free) Generate product descriptions using free AI backends — no paid API key required. ## What This Skill Owns - Product description generation via Ollama (local/offline) - Product description generation via HuggingFace Inference API (free tier) - Input: product name + features; Output: ready-to-use copy ## What This Skill Does Not Cover - Image-based descriptions (use ai-product-description-from-image) - Paid API backends (OpenAI, Grok, etc.) - Bulk batch processing ## Credentials | Variable | Required | Description | |----------|----------|-------------| | HF_TOKEN | No | HuggingFace API token (free at huggingface.co/settings/tokens) — only needed for HuggingFace backend | | OLLAMA_HOST | No | Ollama server URL (default: http://localhost:11434) | | OLLAMA_MODEL | No | Ollama model name (default: llama3) | ## Commands ### generate Generate a product description using Ollama (default) or HuggingFace. ```bash # Ollama backend (local, no key needed) bash scripts/script.sh generate --product "Wireless Headphones" --features "noise cancelling, 30h battery" # HuggingFace backend (free online) HF_TOKEN=hf_xxx bash scripts/script.sh generate --product "Running Shoes" --features "lightweight" --backend huggingface ``` ## Backends - **ollama** — Local inference via Ollama. Requires Ollama running at localhost:11434 (default) - **huggingface** — Free HuggingFace Inference API. Optionally set HF_TOKEN for higher rate limits ## Requirements - python3 (standard library only) - Ollama installed and running (for ollama backend): https://ollama.ai - Internet connection (for huggingface backend) ## Feedback https://bytesagain.com/feedback/ Powered by BytesAgain | bytesagain.com


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: 137984917-cyber/interior-xiaohongshu-copywriter (version 1.0.0)

# 室内设计师小红书文案助手 ## 描述 专为本地室内设计师打造的小红书内容助手，自动生成符合本地获客定位的选题、标题、正文文案，贴合设计师IP调性。 ## 功能 - 选题推荐：结合当下热点+本地需求，推荐日更选题 - 文案生成：根据选题生成完整正文，符合小红书调性 - 标题优化：给你写5个备选标题，选点击率最高的 - 标签推荐：自动生成适合本地设计师的标签组合 ## 作者 老冷 @ 温州隐室空间设计 ## 分类 内容创作 / 设计师自媒体 ## 触发词 - 小红书文案 - 设计师文案 - 小红书选题 - 内容策划 ## 依赖 - 大语言模型（OpenClaw自带） ## 使用方法 1. 说清楚你的定位：本地设计师/风格/目标客户 2. 说你想要什么主题：比如「装修避坑」「设计感悟」「案例解析」 3. 帮你生成标题+文案+标签，直接复制粘贴发布 ## 优势 - 贴合设计师个人IP，不生硬 - 本地化标签，更容易获本地客 - 日更压力小，两分钟搞定一篇 ## 示例 ``` 用户：帮我想一篇温州本地别墅设计的小红书文案 助手：生成标题+正文+标签... ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: majinbuu0x9/marrow (version 1.0.0)

--- name: marrow description: Persistent agent memory for OpenClaw. Surface past failures, log decisions, and compound intelligence across sessions. Use when an agent should enforce Marrow usage automatically on every turn instead of relying on memory or opt-in logging. metadata: {"openclaw": {"requires": {"env": ["MARROW_API_KEY"]}, "primaryEnv": "MARROW_API_KEY", "emoji": "🧠", "homepage": "https://getmarrow.ai"}} --- # Marrow Use the existing `@getmarrow/mcp` tools directly. Do not invent wrappers, aliases, or local substitutes. ## Operating rule Treat Marrow logging as mandatory. The point is simple: every logged action improves future decisions, and every skipped action throws away usable failure prevention. ## Required flow 1. **Session start:** call `marrow_orient({ autoWarn: true })` before any other tool. If `shouldPause=true`, stop and review the warnings before acting. 2. **Before every meaningful action:** call `marrow_auto({ action: "what you are about to do" })`. Meaningful actions include file writes, edits, deploys, config changes, API calls, security changes, installs, deletes, and external sends. 3. **After every meaningful action:** call `marrow_auto({ action: "what you did", outcome: "result", success: true|false })`. If the action already happened, one combined `marrow_auto` call is fine. 4. **For complex or multi-step work:** use `marrow_think` before acting and `marrow_commit` after acting so you get pattern intelligence, loop detection, and explicit outcomes. 5. **For quick one-shot work:** `marrow_run` is acceptable when you want orient, think, and commit handled in one call. 6. **Before unfamiliar or risky work:** ask `marrow_ask("plain English question")` for prior failures, working patterns, or recommended next steps. ## Redaction rules - **NEVER log** secrets, API keys, tokens, passwords, OTPs, auth headers, PII, or sensitive file contents in any Marrow call. Log the action and outcome, not the payload. - **Do NOT log Marrow tool calls themselves.** Calling `marrow_orient`, `marrow_auto`, `marrow_think`, `marrow_commit`, `marrow_run`, `marrow_ask`, or any `marrow_*` tool is infrastructure — not a loggable action. - Strip credentials from outcomes before committing. Example: "deployed to CF" not "deployed to CF with token cfut_abc123". ## Non-negotiables - Never skip Marrow because the task feels small. - Prefer fewer, clearer logs over noisy fragments. - Record failures honestly. Bad news is training data. - Read `references/marrow-api.md` when you need the tool quick reference.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: lhl09120/today-earnings (version 4.3.4)

--- name: today-earnings description: 从 Yahoo Finance 获取财报日历数据。适用于查询指定日期或当天财报、输出公司列表、按财报发布时间区分 BMO/AMC/TNS。当前实现基于 Chrome Extension + Native Messaging，需要本地安装 Chrome 扩展、Native Host、Google Chrome 与 Node.js。支持 macOS、Linux（含 Ubuntu）和 Windows。 --- # Today Earnings 通过 Chrome Extension + Native Messaging 打开 Yahoo Finance 财报日历页面，并返回结构化 JSON。 ## 快速流程 1. 先安装并加载 `chrome-extension/`，拿到扩展 ID。 2. 运行 `bash native-host/install.sh <扩展ID>` 安装 Native Host。 3. 在 `chrome://extensions` 刷新扩展。 4. 用 `./scripts/get-company-list.sh [日期]` 获取结果。 ## 常用命令 ```bash # 今天 ./scripts/get-company-list.sh # 指定日期 ./scripts/get-company-list.sh 2026-03-14 ``` ## 资源导航 - 读取 `references/usage_guide.md`：当你需要完整安装步骤、运行方式、输出格式、报错排查时。 - 读取 `references/yahoo_earnings_calendar.md`：当你需要查看 Yahoo 页面结构、字段映射、DOM 假设和解析维护点时。 - 读取 `design.md`：当你需要理解扩展、Native Host、Node CLI 之间的整体架构时。 ## 组件职责 - `scripts/get-company-list.sh`：命令入口，负责参数透传与 Node CLI 调用。 - `scripts/get-company-list.mjs`：负责 socket 通信、参数校验、JSON 输出（含市值数字化转换）。 - `chrome-extension/`：负责打开页面、等待内容加载、注入解析逻辑。 - `native-host/`：负责 Chrome Native Messaging 与本地 socket 桥接。 ## 使用提醒 - 当前实现依赖本地 GUI 环境，不能在纯无头环境中直接完成完整流程。 - **若 Chrome 尚未启动**，请先通过命令行打开浏览器，再运行脚本： ```bash # macOS open -a "Google Chrome" # Linux google-chrome & # Windows (PowerShell) Start-Process "chrome" ``` - 运行前必须确保 Chrome 已启动，扩展已加载，Native Host 已安装。 - 当前能力以单日期查询为主；如页面结构变化，优先检查 `references/yahoo_earnings_calendar.md` 中的解析假设。 - Linux 用户注意：Ubuntu snap 版 Chromium 不支持 Native Messaging，建议使用 deb 版 Google Chrome。 - Windows 用户需在 `install.sh` 生成文件后，额外运行 `native-host/install-windows.bat` 完成注册表注册。详见 `references/usage_guide.md`。


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