OpenClaw/clawhub-security-signals huggingface dataset:
Skill: ruigomeseu/ffcli (version 1.0.1)

--- name: ffcli description: Query Fireflies.ai meeting data. Use when searching meetings, viewing transcripts, reading AI summaries, extracting action items, or looking up what was discussed in a call. Triggers on "meeting notes", "transcript", "action items from meeting", "what was discussed", "fireflies", "meeting summary". metadata: {"openclaw":{"requires":{"bins":["ffcli"],"env":["FIREFLIES_API_KEY"]},"primaryEnv":"FIREFLIES_API_KEY","install":[{"id":"brew","kind":"brew","formula":"ruigomeseu/tap/ffcli","bins":["ffcli"],"label":"Install ffcli (Homebrew tap)"},{"id":"npm","kind":"node","package":"@ruigomeseu/ffcli","bins":["ffcli"],"label":"Install ffcli (npm)"}]}} --- # ffcli — Fireflies.ai CLI Query meeting recordings, transcripts, and AI summaries from Fireflies.ai. ## Setup Install via Homebrew or npm: ```bash brew install ruigomeseu/tap/ffcli # or npm install -g @ruigomeseu/ffcli ``` Authenticate with your Fireflies API key (get it from [Settings → Developer Settings](https://app.fireflies.ai/settings)): ```bash ffcli auth <your-api-key> # Store key locally (~/.config/ffcli/) ffcli auth --check # Verify it works ``` Alternatively, set the `FIREFLIES_API_KEY` environment variable (takes precedence over stored config). In OpenClaw, configure it via `skills.entries.ffcli.apiKey` in `openclaw.json`. **Note:** `ffcli` is a third-party CLI by @ruigomeseu (Homebrew tap or npm). Verify the source before installing: check the [npm package](https://www.npmjs.com/package/@ruigomeseu/ffcli) or [Homebrew tap repo](https://github.com/ruigomeseu/homebrew-tap) for code review and publish history. ## Commands ### List meetings ```bash ffcli list --limit 10 --md # Recent meetings ffcli list --from 2026-02-01 --to 2026-02-12 --md # Date range ffcli list --participant [REDACTED_SECRET] --md # By participant ffcli list --search "standup" --md # By title keyword ffcli list --limit 5 --include-summaries # With AI summaries (JSON) ``` ### Show meeting detail ```bash ffcli show <id> --md # Full detail (markdown) ffcli show <id> --summary-only --md # Just AI summary ffcli show <id> --transcript-only --md # Just transcript ffcli show <id> --include-transcript --md # Detail + transcript ``` ### User info ```bash ffcli me --md # Account info, transcript count ``` ### Scripting patterns ```bash # Action items from recent meetings ffcli list --limit 10 --include-summaries | jq '.[].summary.action_items' # All meeting IDs from a date range ffcli list --from 2026-02-01 --to 2026-02-07 | jq -r '.[].id' # Export a summary to file ffcli show <id> --summary-only --md > meeting-summary.md ``` ## Notes - Default output is JSON. Use `--md` for readable output. - `--include-summaries` on `list` adds AI summaries (increases response size). - Meeting IDs are needed for `show` — get them from `list` first. - Dates are UTC in JSON output.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: sky-lv/skylv-github-badge-maker (version 1.0.0)

--- name: skylv-readme-badge slug: skylv-readme-badge version: 1.0.0 description: "Generates GitHub README badges using shields.io. Triggers: add badge, shields io, readme badge, status badge." author: SKY-lv license: MIT tags: [automation, tools] keywords: [automation, tools] triggers: readme-badge --- # README Badge Generator ## Overview Creates shields.io badges for GitHub READMEs. ## Common Badges [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) [![npm version](https://img.shields.io/badge/npm-v1.0.0-blue.svg)](https://npmjs.com/package/package) [![CI](https://img.shields.io/github/actions/workflow/status/user/repo/ci.yml?style=flat-square)](https://github.com/user/repo/actions) ## Badge Colors - green = success - blue = version/info - yellow = warning - orange = downloads - red = failure ## Style Options flat (default), flat-square, for-the-badge, plastic


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: little-ke/xiaohongshu-publish-workflow (version 1.0.0)

--- name: xiaohongshu-publish-workflow description: "小红书发布流水线：XHS排版→主编审核→自动化发布→数据复盘。在已有Markdown定稿基础上执行小红书格式化和发布。触发词：发布到小红书、小红书排版、小红书发布。需先完成内容创作。" license: MIT version: 1.0.0 --- # 小红书发布流水线 在已有 Markdown 定稿基础上，完成小红书笔记的排版格式化、发布和数据复盘。 ## 前置条件 - 已通过 `/content-creation` 部署内容创作团队（墨白参与审核） - 已通过 `/xiaohongshu-publisher-setup` 部署小红书发布团队（灵格、星阑） - 已完成小红书登录初始化（`xhs_publish.cjs login`） - 已有 Markdown 定稿（来自 `/content-workflow` 或用户直接提供） ## 工作流概述 ``` 定稿 → 灵格(XHS排版) → 墨白(审核) → 星阑(发布+复盘) ``` ## Phase 3：小红书排版（灵格主导，墨白审核） 1. 灵格根据 Markdown 定稿完成小红书内容包： - **标题方案**：3-5 个标题，每个 ≤20 字，含 emoji，标注推荐（★） - **封面大字**：2-8 字冲击力短句（2-3 个方案） - **正文排版**：分段（≤3行/段）、emoji 标注、口语化改写、互动引导结尾 - **话题标签**：5-10 个（热门+垂直+长尾，分层规划） - **配图建议**：3-9 张图的概念（第1张为封面，附文字叠加建议） 2. 提交给墨白审核 3. 最多迭代 2 轮 4. 输出：XHS 内容包定稿 ## Phase 4：发布与复盘（星阑主导） ### 4.1 发布前准备 1. 星阑确定最佳发布时间，提交给用户确认 2. 验证登录状态： ```bash node {baseDir}/scripts/xhs_publish.cjs check-login ``` - 未登录 → 提示用户运行： ```bash node {baseDir}/scripts/xhs_publish.cjs login ``` 3. 用户准备好图片文件（按配图建议） ### 4.2 生成发布 JSON 星阑根据定稿内容生成 `content.json`： ```json { "title": "最终确定的笔记标题（含emoji）", "body": "完整正文（含emoji排版和话题标签）", "tags": ["话题1", "话题2", "话题3"], "images": ["/path/to/cover.jpg", "/path/to/img2.jpg"], "type": "normal" } ``` ### 4.3 用户确认发布 1. 展示发布预览（标题、正文摘要、话题标签、图片数量、发布时间） 2. **必须获得用户明确确认后才能执行发布** ### 4.4 执行发布 用户确认后： ```bash node {baseDir}/scripts/xhs_publish.cjs publish content.json ``` - 浏览器将自动打开，自动填写内容 - 填写完成后再次让用户在浏览器中确认，避免误操作 - 发布成功后记录笔记 ID 和发布时间 ### 4.5 数据追踪与复盘 1. 发布后 24h 初步数据报告： ```bash node {baseDir}/scripts/xhs_publish.cjs get-note <笔记ID> ``` - 核心指标：点赞、收藏、评论、涨粉 - 初步判断：超预期 / 符合预期 / 低于预期 2. 发布后 48h 完整数据复盘报告： - 与近期历史均值对比 - 数据洞察（突出/欠缺的指标及原因分析） - 至少 3 条可操作优化建议（按优先级排序） - 对下一篇内容的建议 3. 墨白主持复盘，确定下一轮选题优化方向 ## 灵活调用规则 - 用户可以跳过排版（如"直接用这个格式发布"）→ 跳到 Phase 4 - 用户可以只做排版不发布（如"帮我排版小红书格式"）→ Phase 3 结束后停止 - 用户可以单独让星阑分析数据（如"帮我看一下这篇笔记的数据"）→ 直接执行数据查询 - 用户可以单独检查登录状态（如"检查一下小红书登录"）→ 执行 check-login ## 质量门控 - XHS 内容包需经墨白审核（标题、正文风格、话题标签合规性） - 发布操作必须获得用户明确确认（两次：确认发布 + 浏览器内确认） - 发现事实性错误 → 回退到内容创作阶段（使用 /content-workflow） - 标题长度超过 20 字 → 灵格必须重新调整，不允许超标发布 ## 脚本说明 `xhs_publish.cjs` 位于 `{baseDir}/scripts/` 或已部署到 `~/.openclaw/workspace-xiaohongshu-publisher/scripts/`。 **依赖说明：** - 需要 `playwright` npm 包（`npm install playwright`） - 需要 Chromium 浏览器（`npx playwright install chromium`） - 发布为半自动化：脚本自动填写内容，用户在浏览器中最终确认后提交


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

--- name: Outfits description: Build an outfit planning system for saving inspiration, creating combinations, and dressing for any occasion. metadata: {"clawdbot":{"emoji":"👗","os":["linux","darwin","win32"]}} --- ## Core Behavior - User saves outfit inspiration → catalog with tags and notes - User asks what to wear → build combination from their clothes - User plans for event → suggest options with reasoning - Create `~/outfits/` as workspace ## Use Cases - Save looks you love: Instagram, Pinterest, street style - Build go-to combinations from clothes you own - Plan outfits for specific occasions - Develop consistent personal style - Reduce morning decision fatigue ## File Structure ``` ~/outfits/ ├── inspiration/ │ ├── casual/ │ ├── formal/ │ └── streetwear/ ├── my-outfits/ │ ├── work/ │ ├── weekend/ │ └── events/ ├── clothes.md └── style-notes.md ``` ## Clothes Inventory Simple list in clothes.md — no elaborate metadata: ```markdown ## Tops - white oxford shirt - navy crewneck sweater - black t-shirt ## Bottoms - dark wash jeans - khaki chinos ``` ## Saving Inspiration - Screenshot or URL with source - Note WHAT you like: "the layering", "color combo", "those boots" - Tag by style: minimalist, classic, streetwear, smart casual - Season tag if weather-specific ## Building Combinations - Ask what they own before suggesting - Top + bottom + shoes minimum - Add outerwear and accessories as needed - Save working combos to my-outfits/ ## Outfit Entry Format ```markdown # smart-casual-friday.md ## Pieces - Navy blazer - White t-shirt - Dark jeans - White sneakers ## Occasion Office casual Friday, drinks after ## Notes Blazer elevates basic combo ``` ## Occasion Planning - Ask occasion AND venue — "wedding" needs more: outdoor? evening? - Weather affects everything — rain changes shoes and layers - Dress code clarification — "smart casual" varies by context - Suggest 2-3 options, let user pick by mood ## Style Development - Track what styles appear in inspiration folder - Surface patterns: "You save a lot of minimalist looks" - Note color preferences over time - Build style-notes.md with personal preferences ## Capsule Thinking - Surface outfit math when helpful: "5 pieces = 12 combos" - Identify versatile pieces that appear in many outfits - Flag gaps only when obvious or asked - Don't push minimalism unless user wants it ## What To Surface - "This top appears in 6 of your saved outfits" - "You don't have saved outfits for formal events" - "Based on your inspiration, you like earth tones" - "Similar to that Pinterest save you liked" ## Progressive Enhancement - Week 1: save 5-10 inspiration outfits - Week 2: list clothes you wear most in clothes.md - Week 3: create 5 go-to combinations - Ongoing: add inspiration, build seasonal capsules ## What NOT To Push - Buying new items before using what they have - Specific brands or expensive pieces - Rigid fashion rules — principles over laws - Guilt about clothing or style choices


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: googleworkspace-bot/recipe-send-team-announcement (version 1.0.12)

--- name: recipe-send-team-announcement description: "Send a team announcement via both Gmail and a Google Chat space." metadata: version: 0.22.5 openclaw: category: "recipe" domain: "communication" requires: bins: - gws skills: - gws-gmail - gws-chat --- # Announce via Gmail and Google Chat > **PREREQUISITE:** Load the following skills to execute this recipe: `gws-gmail`, `gws-chat` Send a team announcement via both Gmail and a Google Chat space. ## Steps 1. Send email: `gws gmail +send --to [REDACTED_SECRET] --subject 'Important Update' --body 'Please review the attached policy changes.'` 2. Post in Chat: `gws chat +send --space spaces/TEAM_SPACE --text '📢 Important Update: Please check your email for policy changes.'`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: marcosrippel/whatsapp-validate (version 1.0.0)

--- name: whatsapp-validate description: Check if phone numbers exist in the local Baileys session cache --- # WhatsApp Validate Skill Validate whether phone numbers have been seen by the connected WhatsApp account. ## Usage ``` exec({ cmd: "node <skill_dir>/scripts/validate.js COMMAND [ARGS]" }) ``` ## Commands ### Check Single Number ``` exec({ cmd: "node <skill_dir>/scripts/validate.js check \"5511999999999\"" }) ``` ### Batch Check ``` exec({ cmd: "node <skill_dir>/scripts/validate.js batch \"5511999999999,5511888888888\"" }) ``` ### List Known Numbers ``` exec({ cmd: "node <skill_dir>/scripts/validate.js list 50" }) ``` ## Note This skill checks the local cache only. A number not found may still have WhatsApp — it simply hasn't interacted with the bot yet.


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

--- name: "腾讯视频" version: "1.0.0" description: "腾讯视频内容助手，精通视频推荐、内容分析、观影指南、创作建议" tags: ["video", "streaming", "tencent", "content"] author: "ClawSkills Team" category: "video" --- # 腾讯视频 AI 助手 你是一个精通腾讯视频平台的 AI 内容助手，能够提供影视推荐、内容分析和创作者指导。 ## 身份与能力 - 精通影视内容推荐和分类检索 - 熟悉剧情分析、角色解读、影评写作 - 掌握腾讯视频创作者生态和内容分成机制 - 了解视频平台内容趋势和用户偏好 ## 内容推荐策略 ### 按类型推荐 - 电视剧：国产剧、韩剧、美剧、日剧、港剧、泰剧 - 电影：院线新片、经典佳作、纪录片、动画电影 - 综艺：真人秀、脱口秀、选秀、慢综艺 - 动漫：国漫、日漫、儿童动画 - 纪录片：人文历史、自然科学、社会纪实 ### 推荐维度 - 豆瓣评分 8.0+ 的高口碑作品优先推荐 - 结合用户偏好（喜欢的演员、导演、题材） - 区分"轻松下饭"和"烧脑深度"两种观影需求 - 注明是否需要 VIP/超前点播 ### 热门内容追踪 - 关注腾讯视频首页推荐和热搜榜 - 新剧开播前提供预告分析和期待值评估 - 完结剧提供整体评价和同类推荐 ## 影视分析框架 ### 剧情解读 - 主线梳理：核心冲突是什么，主角目标是什么 - 伏笔分析：哪些细节暗示了后续发展 - 结局解读：开放式结局的多种理解角度 - 避免剧透：分析时标注剧透预警 ### 角色分析 - 人物弧光：角色从开始到结束的成长变化 - 动机分析：角色行为背后的心理逻辑 - 关系图谱：主要角色之间的关系梳理 - 演员表现：演技亮点和角色契合度 ### 影评写作指导 - 短评（100字内）：一句话概括 + 评分 + 推荐理由 - 中评（300-500字）：不剧透的整体评价，适合社交分享 - 长评（1000字+）：深度分析，可含剧透但需标注 ## 观影指南 ### 新片速递 - 每周更新腾讯视频新上线内容盘点 - 标注独播/首播内容，突出平台优势 - 提供同期各平台新片对比参考 ### 经典推荐清单 - 入门级：豆瓣 Top250 中腾讯视频可看的 - 类型片单：悬疑十佳、爱情十佳、科幻十佳 - 短剧推荐：12集以内，适合周末一口气看完 - 亲子片单：适合全家一起看的影视作品 ### 观影效率 - 倍速建议：节奏慢的剧 1.25x-1.5x，烧脑剧保持原速 - 跳过片头片尾：腾讯视频支持自动跳过 - 弹幕互动：开弹幕增加观影乐趣，但悬疑剧建议关闭防剧透 ## 创作者指南 ### 腾讯视频号入驻 - 入驻条件：实名认证，原创内容能力 - 内容领域：影视解说、生活vlog、知识科普、游戏、美食 - 审核标准：原创、无水印、画质清晰、内容合规 ### 内容分成机制 - 流量分成：根据播放量和完播率计算收益 - 广告分成：视频中插入广告的收益分成 - 付费内容：知识付费、付费专栏等模式 - 提升收益：提高完播率比单纯追求播放量更重要 ### 内容创作建议 - 标题：包含关键词，制造好奇心但不做标题党 - 封面：高清、有信息量、与内容强相关 - 时长：短视频 1-3 分钟，中视频 5-15 分钟 - 更新频率：保持稳定更新，每周至少 2-3 条 ## 会员体系 ### VIP 等级 - 普通 VIP：去广告、VIP 内容、1080P - 超级影视 VIP：含 TV 端权益 - 体育会员：NBA、英超等体育赛事 ### 省钱技巧 - 年卡比月卡划算，活动期间购买更优惠 - 腾讯视频 + 其他平台联合会员套餐 - 学生认证享受优惠价格 ## 工作规范 - 推荐内容时注明是否需要会员，尊重用户选择 - 影评分析要客观，个人喜好不等于作品质量 - 涉及剧透内容必须提前标注警告 - 不推荐盗版资源，引导正版观影 --- **最后更新**: 2026-03-16


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: liupython520/jin-duo-duo-strategy-skill (version 1.0.0)

--- name: jin-duo-duo-strategy-skill description: 基于技术分析的股票交易策略分析工具；当用户需要进行股票买卖点分析、技术形态识别、交易策略评估时使用 dependency: python: - pandas>=2.0.0 - numpy>=1.24.0 --- # 金多多股票策略分析 Skill ## 任务目标 - 本 Skill 用于：根据技术指标和K线形态分析股票，匹配6种交易策略 - 能力包含：技术指标计算、形态识别辅助、策略评分与推荐 - 触发条件：用户提供股票数据或询问买卖时机、策略分析时 ## 前置准备 - 依赖说明：scripts 脚本所需的依赖包 ``` pandas>=2.0.0 numpy>=1.24.0 ``` - 输入数据准备：准备股票历史数据，包含以下字段 - 日期、开盘价、最高价、最低价、收盘价、成交量 - 支持 CSV 文件或 JSON 格式 - 数据格式说明见 references/strategy-guide.md ## 操作步骤 - 标准流程： 1. **数据准备**：用户提供股票历史数据（CSV 文件或 JSON 格式） 2. **指标计算**：调用 `scripts/technical_indicators.py` 计算技术指标 - 该脚本会计算移动平均线（MA5/10/20/60）、MACD 指标、成交量分析 - 输出结构化的指标数据 3. **形态识别与策略匹配**：智能体根据计算结果和 K 线数据，识别技术形态 - 识别 K 线形态（出水芙蓉、金针探底、看跌吞没等） - 匹配策略条件（见 references/strategy-guide.md） 4. **评分与推荐**：根据策略条件计算得分，输出匹配的策略和操作建议 5. **生成报告**：输出包含策略类型、操作建议、关键信号、风险提示的分析报告 - 可选分支： - 当 数据不足（少于20个交易日）→ 提示用户补充数据，策略判断可能不准确 - 当 信号模糊（多个策略条件接近）→ 标记为"观望等待"（策略六），建议继续观察 - 当 满足多个买入策略 → 选择得分最高的策略，并列出次要信号 ## 资源索引 - 必要脚本：见 [scripts/technical_indicators.py](scripts/technical_indicators.py)（用途：计算 MA、MACD、成交量等技术指标） - 领域参考：见 [references/strategy-guide.md](references/strategy-guide.md)（何时读取：策略匹配、形态识别时） - 输出资产：无固定模板，根据分析结果动态生成报告 ## 注意事项 - 本 Skill 仅基于技术分析，不构成投资建议 - 所有技术指标计算由脚本完成，确保数据准确性 - K 线形态识别和策略判断由智能体基于数据分析完成 - 评分机制仅供参考，实际操作需结合市场环境和个人风险偏好 - 建议至少提供 60 个交易日的数据以获得更准确的分析结果 ## 使用示例 ### 示例1：从 CSV 文件分析股票 - 功能说明：读取股票 CSV 数据，计算指标并匹配策略 - 执行方式：调用脚本 + 智能体分析 - 关键步骤： ```bash python scripts/technical_indicators.py --input stock_data.csv ``` - 指导要点：CSV 需包含日期、开盘、最高、最低、收盘、成交量字段 ### 示例2：从 JSON 数据快速分析 - 功能说明：直接传入 JSON 格式的股票数据进行分析 - 执行方式：调用脚本 + 智能体分析 - 关键步骤： ```bash echo '{"data":[{"date":"2024-01-01","open":10.0,"high":10.5,"low":9.8,"close":10.2,"volume":1000000}]}' | \ python scripts/technical_indicators.py --input-format json ``` - 指导要点：JSON 数组需包含完整的 OHLCV 数据 ### 示例3：策略条件查询 - 功能说明：用户询问特定策略的判断条件 - 执行方式：仅智能体（无需脚本） - 关键步骤：读取 references/strategy-guide.md 中的策略定义，解释给用户 - 指导要点：策略六（观望等待）是所有其他策略不匹配时的默认选项


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

--- name: CHRO / Chief Human Resources Officer slug: chro version: 1.0.1 homepage: https://clawic.com/skills/chro description: Manage HR operations with hiring pipelines, compliance frameworks, compensation strategy, and workforce analytics. changelog: Added Core Rules structure and HR leadership frameworks. metadata: {"clawdbot":{"emoji":"👥","os":["linux","darwin","win32"]}} --- ## When to Use User needs HR leadership for people operations: hiring, compensation, terminations, compliance, or workforce planning. Agent acts as virtual Chief Human Resources Officer for companies without formal HR leadership. ## Quick Reference | Domain | File | |--------|------| | Hiring, offers, onboarding | `hiring.md` | | Legal compliance, documentation | `compliance.md` | | Day-to-day HR operations | `operations.md` | | Analytics, reporting, alerts | `analytics.md` | ## Core Rules ### 1. Documentation First - No termination without paper trail - 3+ documented conversations before PIP - Signed acknowledgments for every warning - If it's not written, it didn't happen ### 2. Retaliation Watch - Block adverse actions within 90 days of HR complaints - Document business justification separately - When in doubt, delay the action ### 3. Jurisdiction-Aware - Apply most restrictive rule in multi-country ops - Local labor law trumps company policy - At-will doesn't mean at-whim ### 4. Escalate Uncertainty - When legal exposure is unclear, flag for review - HR mistakes are expensive to fix - Better to ask than to apologize ### 5. Privacy by Default - Minimize PII collection - Log access to sensitive data - Need-to-know basis for personnel files ### 6. Compensation Integrity - Pay equity audits annually - Document reasons for band exceptions - Market data beats internal precedent ### 7. Culture is Operations - Values without enforcement are decoration - Investigate every complaint - Consistency builds trust ## HR Focus by Stage | Stage | Focus | |-------|-------| | Seed | Founder-led hiring, basic compliance, offer templates | | Series A | First HR hire, HRIS setup, comp bands, handbook | | Series B | HR team, performance cycles, workforce planning | | Series C+ | HR org, HRBP model, global compliance, analytics | ## Common Traps - At-will overconfidence — wrongful termination suits happen anyway - Verbal promises — "we discussed it" isn't documentation - Inconsistent enforcement — policies must apply to everyone - Delayed investigations — complainants lose trust fast - Comp secrecy — pay transparency laws are spreading ## Human-in-the-Loop These decisions require human approval: - Final termination decisions - Executive/senior hires - Compensation exceptions above band - Org restructures - Settlement amounts ## Related Skills Install with `clawhub install <slug>` if user confirms: - `ceo` — executive alignment - `cfo` — compensation budgets - `coo` — operational workforce - `legal` — employment law ## Feedback - If useful: `clawhub star chro` - Stay updated: `clawhub sync`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: zhoutianwang/sales-report-parser (version 1.0.1)

# sales-report-parser 销售报表图片 OCR 识别与大模型解析工具集 ## 功能 从销售报表图片中提取数据，支持： - 图片 OCR 文字识别（使用 cnocr） - 大模型 JSON 解析（支持 MiniMax API） - JSON 转 Excel 表格 ## 工具 ### 1. OCR 识别 ```bash cd /data/sales-report-parser/skills/sales-report-parser/scripts pip install -r requirements.txt # 识别图片中的文字 python ocr_images.py --image /path/to/image.jpg ``` ### 2. 大模型 JSON 生成 ```bash python llm_json.py \ --content "OCR识别结果文本" \ --prompt "你的提示词" \ --api_key "你的API密钥" \ --output result.json ``` ### 3. JSON 转 Excel ```bash python json_to_excel.py --input result.json --output result.xlsx ``` ### 4. 批量提取（推荐） ```bash python batch_extract.py \ --api_key "你的API密钥" \ --image /path/to/images/ \ --output my_output ``` ## 参数说明 ### ocr_images.py - `--image/-i`: 图片路径（必需） ### llm_json.py - `--content/-c`: 要处理的内容（必需） - `--prompt/-p`: 提示词（必需） - `--api_key/-k`: API 密钥（必需） - `--output/-o`: 输出 JSON 路径（必需） - `--base_url`: API 地址（默认 https://api.minimaxi.com/v1） - `--model`: 模型名（默认 MiniMax-M2.5） - `--temperature`: 温度（默认 1.0） ### json_to_excel.py - `--input/-i`: 输入 JSON 文件（必需） - `--output/-o`: 输出 Excel 文件（必需） - `--sheet/-s`: 工作表名（默认 Sheet1） - `--no-transpose`: 不转置表格 - `--no-sort`: 不按日期排序 ### batch_extract.py - `--api_key/-k`: API 密钥（必需） - `--image/-i`: 图片或目录（必需） - `--output/-o`: 输出文件名（默认 output） - `--base_url`: API 地址 - `--retries`: 重试次数（默认 3） ## 销售报表字段 ``` 日期, 总销售, 产品净销售, 现烤面包, 袋装面包, 软点, 西点, 中点, 蛋糕个数, 蛋糕金额, 卡劵, 交易次数 ``` ## 示例 ```bash # 完整流程示例 cd /data/sales-report-parser/skills/sales-report-parser/scripts # 方式一：一步到位批量处理 python batch_extract.py \ --api_key "your-api-key" \ --image ../ \ --output sales_data # 方式二：分步骤处理 # 1. OCR 识别 python ocr_images.py --image 1,8.png # 2. 大模型解析（将 OCR 结果粘贴到 content） python llm_json.py \ --content "OCR识别结果" \ --prompt "提取销售数据..." \ --api_key "your-api-key" \ --output data.json # 3. 转 Excel python json_to_excel.py --input data.json --output data.xlsx ``` ## 依赖 ``` cnocr langchain-openai langchain-core pandas openpyxl pillow pydantic ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: mikeclaw007/anjuke (version 0.1.0)

--- name: anjuke description: 注册“安居客”技能；用于房产平台公开页面、房源服务、经纪规则与帮助信息整理。 homepage: `https://www.anjuke.com/` metadata: {"clawdbot":{"emoji":"🏘️"}} --- # 安居客 用途与边界 - 面向公开官网、房源展示页、经纪服务页、帮助中心与规则页的信息整理 - 不提供账号代发、虚假房源包装、私下交易指导或风控规避能力 - 仅处理公开可见的房源服务、咨询流程、经纪规则与帮助信息 关键入口 - 主页：https://www.anjuke.com/ - 房源与服务：房源入口、经纪服务、买房租房、装修或金融服务入口 - 帮助与规则：帮助中心、经纪规范、平台规则、客服入口 常见任务 - 房源分类、服务类型、咨询流程与帮助信息整理 - 经纪服务、平台规则、线索流程与帮助中心汇总 - 公开活动、专题页、城市站点与使用说明提取 数据字段 - 房源类型、服务入口、城市站点、链接 - 经纪规则、帮助页、客服入口、协议链接 - 流程说明、限制条件、活动入口、FAQ 自动化要点 - 房产信息高度依赖城市、时间和具体楼盘，整理时必须保留上下文 - 不得输出虚假房源、规避审核或违规获客建议 - 不处理用户隐私、经纪后台、业主数据或未公开房源信息 示例流程 - 服务整理：访问 Anjuke 房源与服务页 → 输出产品结构清单 - 规则汇总：进入帮助中心与经纪规范 → 提取关键限制 → 输出摘要 - 城市入口：访问城市站点与专题页 → 输出入口与说明 合规提示 - 遵守平台规则、版权要求与账号安全要求 - 只整理公开信息，不输出敏感数据或非公开内容


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: djc00p/openclaw-ollama-memory (version 1.1.0)

--- name: openclaw-ollama-memory description: "Set up local semantic memory search in OpenClaw using Ollama + nomic-embed-text. Free, private, offline-capable. Replaces cloud embedding APIs (OpenAI, Gemini) with a locally-running model. Trigger phrases: ollama memory, local embeddings, private memory search, offline memory, free embeddings, nomic-embed-text." metadata: {"clawdbot":{"emoji":"🧠","requires":{"bins":["ollama","curl"]},"os":["linux","darwin","win32"]}} --- # OpenClaw Ollama Memory Local semantic memory search for OpenClaw using Ollama — free, private, no API key required. ## Quick Start ### 1. Install & Run Ollama Download from https://ollama.ai, then pull the embedding model: ```bash ollama pull nomic-embed-text ``` Verify Ollama is running: ```bash curl http://127.0.0.1:11434/api/tags ``` ### 2. Configure OpenClaw Edit `~/.openclaw/openclaw.json` and add under `agents.defaults.memorySearch`: ```json { "agents": { "defaults": { "memorySearch": { "provider": "ollama", "model": "nomic-embed-text:latest", "remote": { "baseUrl": "http://127.0.0.1:11434" } } } } } ``` ### 3. Restart OpenClaw ```bash openclaw gateway restart ``` ## Key Points - **Ollama must be explicit** — use `provider: "ollama"`, not `"openai"`. OpenClaw doesn't auto-detect Ollama. - **No API key needed** — runs locally, fully offline. - **nomic-embed-text** — 274MB model, fast, accurate, free. - **baseUrl format** — `http://127.0.0.1:11434` (no `/v1` suffix, no trailing slash). ## Next Steps - See `references/config-reference.md` for advanced tuning (hybrid search, MMR, temporal decay, extra paths). - See `references/troubleshooting.md` for common errors and fixes.


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