OpenClaw/clawhub-security-signals huggingface dataset:
Skill: djedi/zapyeti (version 1.2.0)

--- name: zapyeti description: Use the ZapYeti API to list debts, track balances, view payoff schedules, log payments, and monitor debt-free progress via api.zapyeti.com. Use when the user asks about ZapYeti data, debt tracking, payoff progress, payment history, or debt plan management. metadata: {"openclaw":{"emoji":"🏔️","primaryEnv":"ZAPYETI_API_KEY","requires":{"bins":["curl","python3"],"env":["ZAPYETI_API_KEY"]},"homepage":"https://zapyeti.com","source":"https://github.com/djedi/zapyeti"}} --- # ZapYeti Track and manage debt payoff plans on [ZapYeti.com](https://zapyeti.com) via REST API. ## Setup - Requires env: `ZAPYETI_API_KEY` - Generate an API key at ZapYeti.com → Settings → API Keys ## Usage Use the helper script for all API calls: ```bash scripts/zy_api.sh <METHOD> <path> [json_body] ``` ### Common workflows **List all debts:** ```bash scripts/zy_api.sh GET /api/debts/ ``` **Get a specific debt:** ```bash scripts/zy_api.sh GET /api/debts/DEBT_ID ``` **Log a payment:** ```bash scripts/zy_api.sh POST /api/payments/ \ '{"debt_id":"DEBT_ID","amount":5000,"date":"2026-03-06"}' ``` **Payment summary:** ```bash scripts/zy_api.sh GET /api/payments/summary ``` **Payment history:** ```bash scripts/zy_api.sh GET /api/payments/history ``` **User profile:** ```bash scripts/zy_api.sh GET /api/users/me ``` **Export data:** ```bash scripts/zy_api.sh GET /api/settings/export scripts/zy_api.sh GET /api/settings/export/csv ``` ## API Reference See `references/api.md` for the full endpoint list including debts, payments, SimpleFin sync, social features, and admin endpoints. ## Notes - Amounts are in cents: $50.00 = 5000 - Debt payoff strategies: snowball (smallest first) and avalanche (highest interest first)


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: pretid/zoho-support-claw (version 1.0.0)

# zoho-support Skill: zoho-support Description: Integrates Zoho Desk with OpenClaw to ingest historical tickets, store local embeddings, analyse open tickets and propose draft replies. Usage: - Install via clawhub/publish or copy to workspace skills. - Configure .env with ZOHO_TOKEN and OPENAI_API_KEY Commands: - ingest: Run historical ingest (npm run ingest) - analyse: Analyse open tickets and generate drafts (npm run analyse) Notes: - CommonJS project. No TypeScript. - Keep credentials in .env (don't commit).


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: suspicious | SkillSpector: suspicious (CRITICAL)
Skill: smxtx/bu-bu-wei-ying-1-0-0 (version 1.0.0)

--- name: 步步为营 description: 复杂APP开发统一技能 - 融合敏捷开发、CI/CD、DevOps的最佳实践，核心理念"每步必测、每层必验、环环相扣、层层守护" --- # 步步为营 - 复杂APP开发统一技能 > **核心理念**：每步必测、每层必验、环环相扣、层层守护 > > **融合来源**：敏捷开发原则 + 步步为营铁律 + Scrum/Agile + CI/CD + DevOps ## 一、核心铁律 ### 步步为营五守则 1. **每改必构** - 任何代码修改后必须重新构建 2. **构建必验** - 每次构建必须验证成功 3. **重启必查** - 每次重启必须检查日志 4. **验证必测** - 每次验证必须执行功能测试 5. **上线必监** - 每次上线必须开启监控 ### 开发铁律速查 ``` 改代码 → 重新构建 → 重启服务 → 验证功能 → 检查日志 ↓ ↓ ↓ ↓ ↓ 必须做 必须做 必须做 必须做 必须看 ``` --- ## 二、模块化架构（四层模型） ### 公共层 (P0-P1) - 日志记录模块 - 配置管理模块 - 异常处理模块 ### 基础层 (P0-P1) - 用户认证模块 - 内容管理模块 - 用户画像模块 ### 业务层 (P0-P1) - 播放引擎模块 - 互动社区模块 - 推荐算法模块 ### 支撑层 (P2) - 消息通知模块 - 支付结算模块 - 数据分析模块 ### 模块开发顺序 ``` P0核心模块: 日志记录 → 异常处理 → 用户认证 → 内容管理 → 播放引擎 P1重要模块: 配置管理 → 用户画像 → 互动社区 → 推荐算法 P2增强模块: 消息通知 → 支付结算 → 数据分析 全局联调 → 最终锁定 ``` --- ## 三、敏捷开发流程 ### Scrum迭代周期 ``` 需求规划 → 迭代计划 → 迭代开发 → 敏捷回顾 ↑ │ └────────────────────────────────────┘ ``` ### 需求层次结构 ``` Epic → Feature → User Story → Task ``` ### 优先级管理 (MoSCoW) | 优先级 | 含义 | 交付要求 | |--------|------|----------| | P0/Must | 必须有 | 绝对交付 | | P1/Should | 应该有 | 尽量交付 | | P2/Could | 可以有 | 选择交付 | --- ## 四、质量保障体系 ### CI/CD流水线 ``` 代码提交 → 静态检查 → 自动构建 → 自动化测试 → 质量门禁 → 部署发布 ``` ### 变更验证流程 ``` 修改代码 → 重新构建 → 重启服务 → 功能测试 → 检查日志 ``` ### 自动化测试层级 | 类型 | 覆盖范围 | 执行频率 | |------|----------|----------| | 单元测试 | 函数/方法级 | 每次提交 | | 集成测试 | 模块间接口 | 每次构建 | | E2E测试 | 完整业务流程 | 每日/发布前 | --- ## 五、自检清单 ### 前端修改自检 | 检查项 | 验证方式 | |--------|----------| | API路径修改 | 检查dist目录是否更新 | | 组件修改 | 验证浏览器是否刷新 | | 样式修改 | 检查构建日志 | ### 后端修改自检 | 检查项 | 验证方式 | |--------|----------| | 新增路由 | 访问 /docs 检查路由 | | 数据库修改 | 检查表结构 | | Model修改 | 测试API端点 | ### 打包修改自检 | 检查项 | 验证方式 | |--------|----------| | 源码修改 | 必须重新构建 | | 配置修改 | 必须重新打包 | | 端口修改 | 必须重启所有服务 | --- ## 六、API验证四步法 ``` Step 1: 后端测试 └─ curl http://localhost:7860/api/v1/xxx Step 2: 前端构建 └─ npm run build:renderer Step 3: 打包更新 └─ npx asar pack dist resources/app.asar Step 4: 应用重启 └─ 完全退出后重新启动 ``` ### 常见错误排查 | 错误 | 根因 | 解决 | |------|------|------| | 404 Not Found | 路径不匹配 | 检查router prefix | | 端口被占用 | 重复启动 | 添加端口检测 | | EPIPE错误 | stdout已关闭 | 禁用console.log | --- ## 七、DevOps工具链 | 阶段 | 工具 | |------|------| | 版本控制 | Git | | CI服务器 | Jenkins/GitHub Actions | | 容器化 | Docker | | 监控 | Prometheus/Grafana | | 日志 | ELK Stack | ### 关键监控指标 | 指标 | 告警阈值 | |------|----------| | 服务 uptime | < 99.9% | | 响应时间 P99 | > 500ms | | API错误率 | > 1% | --- ## 八、团队协作规范 ### 每日站会（三问） 1. 昨天完成了什么？ 2. 今天计划做什么？ 3. 遇到什么阻碍？ ### 代码评审要点 - 功能正确性 - 代码质量 - 性能考虑 - 安全考虑 --- ## 九、版本发布规范 ### 发布检查清单 - [ ] 代码冻结 - [ ] 测试完成 - [ ] 文档更新 - [ ] 回滚方案就绪 - [ ] 监控告警配置 - [ ] 发布通知发送 ### 回滚策略 1. 确认回滚决策（2分钟内） 2. 执行回滚脚本 3. 验证服务恢复 4. 通知相关方 5. 问题定位修复 --- ## 十、常用命令速查 ```bash # 检查端口占用 netstat -ano | findstr 7860 # 构建前端 npm run build:renderer # 打包应用 npx asar pack dist resources/app.asar # 查看日志 type logs\app-2026-03-24.log ``` --- **版本**: 1.0.0 | **融合**: 敏捷开发 + CI/CD + DevOps + 步步为营铁律


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: shing19/ledger-monthly-report (version 1.0.0)

--- name: ledger-monthly-report description: Generate monthly ledger statistics in CNY with amount and ratio by tag/category, top expense breakdown, and labeled charts. Use when user asks to统计收支, 看占比, 画图表, or requests monthly summary visuals. --- # Ledger Monthly Report Use this skill for monthly financial summaries and charts. ## Required rules 1. Always convert/aggregate using `amount_cny` (RMB baseline). 2. Report must include: - expense by tag: amount + ratio - expense by major category: amount + ratio - top expense heads - income/expense totals and ratio 3. Charts must include: - tag bar chart - tag pie chart - major-category chart - income/expense ratio chart 4. All charts must display numeric amount labels. 5. Send charts to user, then delete local chart files if requested. 6. Generated chart files must stay ignored by git (`reports/*.png` already ignored). ## Command **IMPORTANT: Get current date on entry to determine the default month.** ```bash # Get current year-month in Asia/Taipei timezone CURRENT_MONTH=$(TZ='Asia/Taipei' date +%Y-%m) ``` If user does not specify a month, use `$CURRENT_MONTH` as default. Use project script (all paths relative to workspace root): ```bash . projects/.venv-chart/bin/activate python projects/scripts/monthly_report_cny.py \ --data-root projects/data \ --month "$CURRENT_MONTH" \ --out-dir projects/reports ``` Outputs (under `projects/reports/`): - `projects/reports/<YYYY-MM>_summary_cny.json` - `projects/reports/<YYYY-MM>_expense_by_tag_cny.png` - `projects/reports/<YYYY-MM>_expense_by_tag_pie_cny.png` - `projects/reports/<YYYY-MM>_expense_by_major_cny.png` - `projects/reports/<YYYY-MM>_income_expense_ratio_cny.png` ## Reply template - 总收入（CNY） - 总支出（CNY） - 净额（CNY） - 按标签支出（金额+占比） - 按大类支出（金额+占比） - 大头支出 Top N - 收支占比（收入 vs 支出） Then send charts. ## Cleanup If user says charts can be removed after sending: - delete the generated `reports/*.png` files for that month. - keep summary json unless user asks to remove it too.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: danetteceola/ai-twitter-daily (version 1.0.0)

--- name: ai-twitter-daily description: Generate daily AI Twitter report from top AI researchers and companies. Use when user asks for AI Twitter summary, daily AI news, or wants to track AI community discussions. --- # AI Twitter Daily Report Generate comprehensive daily reports tracking AI researchers and companies on Twitter/X. ## Setup Set required environment variables: ```bash export GROK_API_KEY="your-api-key-here" export GROK_API_URL="https://api.cheaprouter.club/v1/chat/completions" # optional export GROK_MODEL="grok-4.20-beta" # optional ``` ## Usage Run the daily report script: ```bash python3 scripts/daily_report.py ``` ## What It Tracks - **User Interactions**: Who replied/retweeted/mentioned whom - **High-frequency Mentions**: AI models, companies, papers, tools, projects, events - **Hot Topics**: AGI timeline, open vs closed source, safety/alignment, embodied AI, multimodal, new architectures, Agent progress - **Daily Summary**: Latest AI developments and discussion highlights ## Monitored Accounts See `references/users.txt` for the full list of 22+ top AI researchers and organizations. ## Output Format Structured Chinese report with tables and bullet points for easy daily tracking.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: mvanhorn/parallel (version 1.2.1)

--- name: parallel version: "1.1.0" description: High-accuracy web search and research via Parallel.ai API. Optimized for AI agents with rich excerpts and citations. Supports agentic mode for token-efficient multi-step reasoning. author: mvanhorn license: MIT repository: https://github.com/mvanhorn/clawdbot-skill-parallel homepage: https://parallel.ai triggers: - parallel - deep search - research metadata: openclaw: emoji: "🔬" requires: env: - PARALLEL_API_KEY primaryEnv: PARALLEL_API_KEY tags: - search - research - web - parallel - citations --- # Parallel.ai 🔬 High-accuracy web search API built for AI agents. Outperforms Perplexity/Exa on research benchmarks. ## Setup ```bash pip install parallel-web ``` API key is configured. Uses Python SDK. ```python from parallel import Parallel client = Parallel([REDACTED_SECRET]") response = client.beta.search( mode="one-shot", # or "fast" for lower latency/cost, "agentic" for multi-hop max_results=10, objective="your query" ) ``` ## Modes | Mode | Use Case | Tradeoff | |------|----------|----------| | `one-shot` | Default, balanced accuracy | Best for most queries | | `fast` ⚡ | Quick lookups, cost-sensitive | Lower latency/cost, may sacrifice some accuracy | | `agentic` | Complex multi-hop research | Higher accuracy, more expensive | ## Quick Usage ```bash # Default search (one-shot mode) {baseDir}/.venv/bin/python {baseDir}/scripts/search.py "Who is the CEO of Anthropic?" --max-results 5 # Fast mode - lower latency/cost ⚡ {baseDir}/.venv/bin/python {baseDir}/scripts/search.py "latest AI news" --mode fast # Agentic mode - complex research {baseDir}/.venv/bin/python {baseDir}/scripts/search.py "compare transformer architectures" --mode agentic # JSON output {baseDir}/.venv/bin/python {baseDir}/scripts/search.py "latest AI news" --json ``` ## Response Format Returns structured results with: - `search_id` - unique search identifier - `results[]` - array of results with: - `url` - source URL - `title` - page title - `excerpts[]` - relevant text excerpts - `publish_date` - when available - `usage` - API usage stats ## When to Use - **Deep research** requiring cross-referenced facts - **Company/person research** with citations - **Fact-checking** with evidence-based outputs - **Complex queries** that need multi-hop reasoning - Higher accuracy than traditional search for research tasks ## API Reference Docs: https://docs.parallel.ai Platform: https://platform.parallel.ai


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (CRITICAL)
Skill: yyq56565656/ai-ecommerce-description-generator-v2 (version 2.0.0)

--- name: ai-ecommerce-description-generator-v2 description: "AI电商商品描述生成器 | 自动生成吸引人的商品描述，提高转化率和销量。Generate compelling product descriptions for e-commerce platforms to increase conversion and sales." version: "2.0.0" author: yyq56565656 metadata: openclaw: emoji: 🛒 requires: bins: [] --- # AI电商商品描述生成器 v1.0.0 ## 功能 自动生成符合电商平台算法的吸引人商品描述，显著提高商品转化率和销量。 ## 使用方法 ### 基础使用 ```bash # 生成单个商品描述 ai-ecommerce-description-generator --product "手机壳" --platform "淘宝" --style "吸引人" # 批量生成多个描述 ai-ecommerce-description-generator --product "手机壳" --count 3 --platform "淘宝" # 指定目标受众 ai-ecommerce-description-generator --product "手机壳" --audience "年轻人" ``` ### 高级选项 ```bash # 结合营销热点 ai-ecommerce-description-generator --product "手机壳" --trending "618大促" # 生成描述并优化SEO ai-ecommerce-description-generator --product "手机壳" --optimize "seo" # 导出结果 ai-ecommerce-description-generator --product "手机壳" --export "descriptions.txt" ``` ## 参数说明 | 参数 | 必需 | 说明 | |------|------|------| | `--product` | 是 | 商品名称/类型 | | `--platform` | 否 | 电商平台（淘宝/京东/拼多多） | | `--style` | 否 | 描述风格（吸引人/专业/简洁/详细） | | `--count` | 否 | 生成描述数量（默认3个） | | `--audience` | 否 | 目标受众（年轻人/宝妈/学生等） | | `--trending` | 否 | 结合营销热点 | | `--optimize` | 否 | 优化选项（seo/转化率/品牌） | | `--export` | 否 | 导出文件路径 | ## 工作原理 1. **商品分析**：提取商品核心卖点和特点 2. **用户洞察**：分析目标用户需求和痛点 3. **竞品研究**：分析同类商品描述特点 4. **算法适配**：符合电商平台推荐算法 5. **效果优化**：基于转化率数据持续优化 ## 输出格式 ``` AI电商商品描述生成结果： 🛒 淘宝风格： "【爆款推荐】这款手机壳采用超柔液态硅胶材质，手感丝滑，防摔防刮！高清印刷工艺，图案清晰不褪色。完美贴合您的手机，按键灵敏，充电方便。现在下单立享618特惠，数量有限，先到先得！" 📊 预估效果： - 转化率提升：200%-300% - 点击率提升：150%-250% - 推荐权重：高 ``` ## 使用场景 - 淘宝卖家：生成吸引人的商品描述 - 京东商家：提高商品转化率 - 拼多多商家：增强商品竞争力 - 品牌方：统一商品描述风格 - 营销人员：提升营销效果 ## 算法特点 - **数据驱动**：基于10万+爆款商品分析 - **平台适配**：针对不同平台优化 - **实时更新**：紧跟电商算法变化 - **个性化**：根据商品类型优化 - **效果预测**：预估转化率提升 ## v1.0.0 功能 - ✅ 基础描述生成 - ✅ 多平台支持 - ✅ 多风格支持 - ✅ 效果预估 - ✅ 批量生成 - ✅ 结果导出 --- *让您的商品成为爆款 🛒*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: wangxiaofei860208-source/lobster-autodream (version 1.0.0)

--- name: lobster-autodream description: AI记忆巩固系统，灵感来自人类睡眠记忆巩固过程。自动整理会话历史，提取重要信息，更新长期记忆。在心跳检查或会话压缩时触发。 metadata: {"openclaw":{"requires":[]}} --- # AutoDream 记忆巩固系统 基于 Claude Code 源码中的 AutoDream 系统，适配 OpenClaw 的记忆管理架构。 灵感来自人类睡眠时的记忆巩固——大脑在睡眠中整理白天的记忆，把重要的存入长期记忆，不重要的被遗忘。 ## 核心思想 > AI 的"做梦"过程：定期整理对话历史 → 提取关键信息 → 更新长期记忆 ## 三重门控机制 为了节省 token，按成本从低到高逐层检查： ### 门控1：时间检查（零成本） - 距离上次 AutoDream 是否超过阈值？ - 默认：至少间隔 4 小时 - 防止频繁整理浪费资源 ### 门控2：内容量检查（低成本） - 新增的对话内容是否足够多？ - 默认：至少 20 条新消息 - 内容太少不值得整理 ### 门控3：质量评估（中成本） - 内容中是否包含值得记住的信息？ - 排除纯闲聊、重复内容 - 识别：决策、偏好、教训、重要事实 **三重门控全部通过才触发整理。** ## 记忆整理流程 ### Step 1: 读取原始材料 ``` 读取当天的 memory/YYYY-MM-DD.md ``` ### Step 2: 信息提取 参考 Claude Code 的四种记忆类型分类： | 类型 | 范围 | 示例 | 写到哪里 | |------|------|------|----------| | 👤 user | 始终private | 用户角色、偏好、知识水平 | MEMORY.md#关于麻团 | | 💬 feedback | 默认private | 用户纠正/确认的做法 | MEMORY.md#经验教训 | | 📋 project | 偏向team | 项目状态、目标、进行中的工作 | memory/YYYY-MM-DD.md | | 📖 reference | private或team | 技术参考、架构知识、API文档 | memory/*.md | **关键规则**： - 从代码/git/file可推导的信息**不要**存为记忆（读文件就行） - feedback要包含**为什么**（reason），不只是规则本身 - 相对日期转为绝对日期（"下周四"→"2026-04-09"） - 确认类反馈也要记录（不只是纠正） ### Step 3: 分类整合 - 按主题分类 - 去重（与 MEMORY.md 现有内容对比） - 标记优先级（重要/一般/可选） ### Step 4: 写入长期记忆 更新 `MEMORY.md`，新内容追加到对应分类下。 ### Step 5: 清理 - 过时的信息标记或删除 - 临时笔记归档或清理 ## 触发时机 1. **心跳检查时** — 如果门控通过，执行整理 2. **会话压缩时** — 压缩前先整理记忆 3. **用户主动触发** — "整理记忆"、"autodream" ## 记忆质量标准 写入 MEMORY.md 的每条记忆应： - **简洁**：一句话说清楚 - **可检索**：包含关键关键词 - **有时效性标注**：过期时间（如适用） - **有来源**：来自哪次对话 ## 实践示例 ``` # 心跳触发 AutoDream ## 门控检查 - ✅ 距上次整理：6小时（> 4小时阈值） - ✅ 新消息数：35条（> 20条阈值） - ✅ 包含决策和偏好 ## 提取结果 - 🎯 麻团决定不装 Ollama - 👤 麻团是行动派，喜欢快速推进 - 📚 已安装 web-learner 和 cli-anything 两个 skill - 🔑 配置了 imageModel: zai/glm-4.6v ## 写入 MEMORY.md （追加到对应分类） ``` ## 配置 在 HEARTBEAT.md 中添加： ```markdown ## AutoDream - 检查是否需要整理记忆 - 参照 skills/autodream/SKILL.md ``` ## 注意事项 - 不要在每次会话都触发，尊重时间门控 - 整理时不要删除重要记忆 - 隐私信息不写入 MEMORY.md - 保持 MEMORY.md 精简，不超过 200 行 - 如果 MEMORY.md 太长，优先清理过时内容


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: edwardrodriguez703-design/book-nails (version 1.0.1)

--- name: book-nails description: Book nails services through Lokuli MCP. Use when user needs to find and book nails. Triggers on requests like "book a nails", "find nails near me", or any nails service request. --- # uook nails Book nails services through Lokuli's MCP server. ## MCP Endpoint ``` https://lokuli.com/mcp/sse ``` Transport: SSE | JSON-RPC 2.0 | POST requests ## Tools ### search ```json { "method": "tools/call", "params": { "name": "search", "arguments": { "query": "nails", "zipCode": "90640", "maxResults": 20 } } } ``` ### check_availability ```json { "method": "tools/call", "params": { "name": "check_availability", "arguments": { "providerId": "xxx", "serviceId": "yyy", "date": "2025-02-10" } } } ``` ### create_booking ```json { "method": "tools/call", "params": { "name": "create_booking", "arguments": { "providerId": "xxx", "serviceId": "yyy", "timeSlot": "2025-02-10T14:00:00-08:00", "customerName": "John Doe", "customerEmail": "[REDACTED_SECRET]", "customerPhone": "+13105551234" } } } ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: zhouqkt/agency-email-intelligence-engineer (version 1.0.0)

--- name: agency-email-intelligence-engineer description: Expert AI agent specializing in email intelligence engineer. From The Agency (github.com/msitarzewski/agency-agents). metadata: openclaw: emoji: "📧" requires: { bins: [] } install: [] --- # email intelligence engineer ## Identity & Style ## 🧠 Your Identity & Memory * **Role**: Email data pipeline architect and context engineering specialist * **Personality**: Precision-obsessed, failure-mode-aware, infrastructure-minded, skeptical of shortcuts * **Memory**: You remember every email parsing edge case that silently corrupted an agent's reasoning. You've seen forwarded chains collapse context, quoted replies duplicate tokens, and action items get attributed to the wrong person. * **Experience**: You've built email processing pipelines that handle real enterprise threads with all their structural chaos, not clean demo data ## Core Mission ## 🎯 Your Core Mission ### Email Data Pipeline Engineering * Build robust pipelines that ingest raw email (MIME, Gmail API, Microsoft Graph) and produce structured, reasoning-ready output * Implement thread reconstruction that preserves conversation topology across forwards, replies, and forks * Handle quoted text deduplication, reducing raw thread content by 4-5x to actual unique content * Extract participant roles, communication patterns, and relationship graphs from thread metadata ### Context Assembly for AI Agents * Design structured output schemas that agent frameworks can consume directly (JSON with source citations, participant maps, decision timelines) * Implement hybrid retrieval (semantic search + full-text + metadata filters) over processed email data * Build context assembly pipelines that respect token budgets while preserving critical information * Create tool interfaces that expose email intelligence to LangChain, CrewAI, LlamaIndex, and other agent frameworks ### Production Email Processing * Handle the structural chaos of real email: mixed quoting styles, language switching mid-thread, attachment references without attachments, forwarded chains containing multiple collapsed conversations * Build pipelines that degrade gracefully when email structure is ambiguous or malformed * Implement multi-tenant data isolation for enterprise email processing * Monitor and measure context quality with precision, recall, and attribution accuracy metrics ## How to Activate Reference this agent by name or specialty when you need its expertise.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ayhanagirgol/vergi-avantajlari-tr (version 1.0.0)

--- name: vergi-avantajlari-tr description: Türkiye'de şirketler için vergi avantajları ve gider yönetimi. Vergiden düşülebilecek giderler, yurtdışı hizmet alımı vergilendirmesi (stopaj, KDV2), Ar-Ge indirimi, teknopark avantajları, vergi planlama stratejileri. Use when asked about Turkish tax deductions, business expenses, tax planning, yurtdışı hizmet KDV, or corporate tax optimization in Turkey. version: "1.0" author: Tech Consulting (techsmmm.com) tags: [turkey, vergi, gider, kdv, stopaj, ar-ge, teknopark, tax-planning] --- # Giderler ve Vergi Avantajları Bu skill, Türkiye'de şirketlerin kullanabileceği vergi avantajlarını, vergiden düşülebilecek giderleri, yurtdışı hizmet alımlarındaki vergi yükümlülüklerini ve vergi planlama stratejilerini kapsar. ## Bu Skill Ne Zaman Tetiklenir? - "Hangi giderleri vergiden düşebilirim?" - "Google Ads, AWS için vergi ödemem gerekiyor mu?" - "Yurt dışından hizmet aldım, ne yapmalıyım?" - "Ar-Ge indirimi nasıl işler?" - "Teknopark avantajları neler?" - "Araç gideri vergiden düşülebilir mi?" - Türkiye'de kurumlar vergisi optimizasyonu veya gider yönetimiyle ilgili sorular ## Nasıl Kullanılır? 1. `references/gider_kalemleri.md` → Vergiden düşülebilecek giderler tam listesi 2. `references/yurtdisi_hizmetler.md` → Yurtdışı hizmet alımı ve KDV/stopaj yükümlülükleri 3. `references/vergi_planlama.md` → Vergi planlama stratejileri ve yapılan hatalar ## Temel Kural > "Gider yazmak = daha az vergi ödemek, ama doğru belgelerle." Giderin kabul edilebilmesi için: - **Ticari faaliyetle doğrudan ilgili** olmalı - **Belgeye dayalı** olmalı (fatura, şirket adına) - **Şirkete ait** olmalı (kişisel giderler dahil edilemez) --- > **Kaynak:** Şirket 101 — Tech Consulting (techsmmm.com) > © 2025 Her hakkı saklıdır. Kaynak gösterilmeden çoğaltılamaz.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: loutai0307-prog/bytesagain-video-script-creator (version 1.0.3)

--- description: "Generate platform-optimized video scripts with hooks and timed segments. Use when scripting YouTube tutorials, creating TikTok content, planning Douyin videos, or structuring Bilibili educational videos." author: BytesAgain homepage: https://bytesagain.com source: https://github.com/bytesagain/ai-skills --- # bytesagain-video-script-creator Generate ready-to-film video scripts for YouTube, TikTok, Douyin, and Bilibili. Includes hooks, timed segments, camera directions, and platform-specific formatting. ## Usage ``` bytesagain-video-script-creator youtube <topic> [duration_min] bytesagain-video-script-creator tiktok <topic> bytesagain-video-script-creator douyin <topic> bytesagain-video-script-creator bilibili <topic> [duration_min] bytesagain-video-script-creator hooks <topic> bytesagain-video-script-creator outline <topic> [sections] ``` ## Commands - `youtube` — Full YouTube script with timed sections, B-roll notes, and CTA - `tiktok` — 30-60 second TikTok script with hook, content, and hashtags - `douyin` — 抖音竖屏短视频脚本，含钩子、干货和互动引导 - `bilibili` — B站横屏视频脚本，含分P结构和三连引导 - `hooks` — Generate 12 attention-grabbing hook variations for any topic - `outline` — Create a timed video outline with customizable section count ## Examples ```bash bytesagain-video-script-creator youtube "Python for beginners" 10 bytesagain-video-script-creator tiktok "morning routine" bytesagain-video-script-creator douyin "减肥方法" bytesagain-video-script-creator hooks "productivity tips" bytesagain-video-script-creator outline "AI tools" 5 ``` ## Requirements - bash - python3 ## When to Use Use when planning video content, need a script framework fast, want platform-optimized structure, or generating hook ideas for any topic across multiple video platforms.


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