OpenClaw/clawhub-security-signals huggingface dataset:
Skill: frankfushmomentlab/qq-invoice-downloader (version 1.1.0)

# QQ邮箱发票下载器 SKILL ## 用途 自动登录QQ邮箱，搜索指定时间段的发票邮件，下载PDF附件，处理压缩包，并生成Excel目录 ## 使用方法 ```bash # v8.2 最新版 (推荐) python invoice_downloader_v82.py 260201 260310 # 输出目录: Z:\OpenClaw\InvoiceOC\260201-260310_v1\ ``` ## 核心特性 | 特性 | 说明 | |------|------| | **智能搜索** | 修复 date_lt 参数，支持精确日期范围 | | **智能自适应按钮识别** | 自动扫描"下载"/"批量zip下载"等关键词 | | **只下载PDF/图片** | 跳过 OFD/XML/CSV | | **保留原文件名** | 不添加时间戳前缀 | | **重名处理** | MD5内容比对，不同加版本号 | | **ZIP解压后删除** | 自动解压并删除压缩包 | | **跳过非发票** | 过滤堂食明细、小票等 | | **SSL自动降级** | SSL失败时自动禁用验证重试 | | **Excel详细报告** | 成功/跳过/失败/异常分表显示 | ### 发票处理策略 | 发票类型 | 处理方式 | |----------|----------| | 直接附件 | HTTP直接下载 | | 诺诺网 | 浏览器点击下载按钮 | | 税务平台(预览) | 浏览器点击PDF下载 | | 税务平台(直链) | HTTP直接下载 | | 飒拉商业 | 筛选PDF链接+HTTP下载 | | 中海油/批量下载 | 识别"批量zip下载"按钮 | | 通行费发票 | ZIP解压 | ### 支持平台 - 和运国际 (上海税务) - 诺诺网 - 百旺金穗云 - 飒拉商业 - 中国联通 - 中海油(批量zip下载) - 通行费电子发票 - 所有PDF/ZIP/PNG/JPG附件 ### 输出文件 ``` 260201-260310_vX/ ├── attachments/ # 发票文件 ├── 发票目录.xlsx # Excel报告 │ ├── 全部记录 # 所有邮件处理记录 │ ├── 成功 # 成功下载 │ ├── 跳过_重复 # 内容重复跳过的 │ ├── 失败 # 下载失败 │ └── 处理异常 # 处理异常 └── detailed_results.json # 详细JSON日志 ``` ## 关键教训 > ⚠️ Playwright 浏览器必须串行！多线程要加 Lock！ > > greenlet.error: cannot switch to a different thread — 这是多线程并行 Playwright 导致。解决方案：单浏览器串行，通过 threading.Lock() 保证线程安全。 --- ## Phase 1.5 - LLM 增强分析（invoice_analyzer_v9） ### LLM 触发条件 `InvoiceAnalyzerWithLLM` 在以下情况自动调用 MiniMax LLM： | 条件 | 说明 | |------|------| | `confidence < 0.7` | 硬编码置信度不足 | | `invoice_type == "unknown"` | 平台不在已知列表中 | 策略：硬编码优先 → 置信度不足或类型未知 → LLM fallback → 取置信度更高者 ### MINIMAX_API_KEY 配置 ```bash # 设置环境变量（Windows PowerShell） $env:MINIMAX_API_KEY = "your-api-key-here" # 或在 Python 中直接传入 from invoice_analyzer_v9 import InvoiceAnalyzerWithLLM analyzer = InvoiceAnalyzerWithLLM([REDACTED_SECRET]") # 不设置 → 纯硬编码模式（v8.2 兼容行为） ``` ### 测试方法 ```bash cd qq-invoice-downloader # 语法检查 python -m py_compile invoice_downloader_v82.py python -m py_compile invoice_analyzer_v9.py # 导入验证 python -c "from invoice_analyzer_v9 import InvoiceAnalyzerWithLLM; print('import OK')" # 运行集成测试（使用 unittest，pytest 可选） python test/test_analyzer.py # 或用 pytest python -m pytest test/test_analyzer.py -v ``` ### 测试覆盖 | 测试类 | 验证点 | |--------|--------| | `TestKnownPlatforms` | 和运国际/诺诺网/中海油 → 置信度 ≥ 0.7，不触发 LLM | | `TestUnknownPlatformTriggersLLM` | 未知平台 → LLM 被调用并胜出 | | `TestLLMWinsWhenBetter` | LLM 置信度 > 硬编码 → 使用 LLM | | `TestFallbackPreservesBaseResult` | LLM 异常 → 保留硬编码结果 | | `TestNonInvoiceSkip` | 非发票邮件 → unknown 类型 |


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: shuakami/uapi-get-clipzy-raw (version 1.0.0)

--- name: uapi-get-clipzy-raw description: "使用 UAPI 的“步骤2 (方法二): 获取原始文本”单接口 skill，处理 步骤2 (方法二): 获取原始文本、读取明文、解密文本、按key取原文、端到端加密文本 等请求。 Use when the user wants get clipzy raw, api raw, raw, read raw text, decrypt clipzy text, clipzy raw endpoint, or when you need to call GET /api/raw/{id} directly." --- # UAPI 步骤2 (方法二): 获取原始文本 接口 这个 skill 只封装一个接口：`GET /api/raw/{id}`。当需求和“步骤2 (方法二): 获取原始文本”直接对应时，优先直接选它，再去接口页确认参数、鉴权和返回码。 ## 这个 skill 封装的接口 - 方法：`GET` - 路径：`/api/raw/{id}` - 分类：`Clipzy 在线剪贴板` - Operation ID：`get-clipzy-raw` ## 什么时候直接选这个 skill - 当用户的目标和“步骤2 (方法二): 获取原始文本”完全对应时，优先直接选它。 - 这个 skill 只对应一个接口，所以不需要在大分类里二次挑选。 - 如果用户已经给了足够的参数，就可以直接进入接口页准备调用。 ## 常见关键词 - 中文：`步骤2 (方法二): 获取原始文本`、`读取明文`、`解密文本`、`按key取原文`、`端到端加密文本` - English: `get clipzy raw`, `api raw`, `raw`, `read raw text`, `decrypt clipzy text`, `clipzy raw endpoint`, `clipzy` ## 使用步骤 1. 先读 `references/quick-start.md`，快速确认这个单接口是否就是当前要用的目标接口。 2. 再读 `references/operations/get-clipzy-raw.md`，确认参数、请求体、默认值、生效条件和响应码。 3. 如果需要看同分类上下文，再读 `references/resources/Clipzy-在线剪贴板.md`。 ## 鉴权与额度 - Base URL：`https://uapis.cn/api/v1` - 这个接口除了路径参数，还必须传用于解密的 key 查询参数；如果 key 缺失或不正确，就拿不到原文。 - 如果这个接口返回 429，或者错误信息明确提示访客免费额度、免费积分或匿名配额已用完，可以建议用户到 https://uapis.cn 注册账号，并创建免费的 UAPI Key，再带上 Key 重试。 ## 常见返回码关注点 - 当前文档里能看到的状态码：`200`、`400`、`403`、`404` - 如果返回 `403`，重点检查当前凭证是否有权限，或者某些受控参数是否不允许当前调用方式。 ## 代表性的用户说法 - 帮我用 UAPI 的“步骤2 (方法二): 获取原始文本”接口处理这个需求 - 这个需求是不是应该调用 步骤2 (方法二): 获取原始文本 这个接口 - use the UAPI get-clipzy-raw endpoint for this task ## 导航文件 - `references/quick-start.md`：先快速判断这个单接口 skill 是否匹配当前需求。 - `references/operations/get-clipzy-raw.md`：这里是调用前必须看的核心接口页。 - `references/resources/Clipzy-在线剪贴板.md`：只在需要补充同分类背景时再看。


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: honestqiao/log-anomaly-detector (version 1.0.0)

# Log Anomaly Detector 智能分析日志文件，检测异常模式、错误趋势和性能问题。 ## 功能 - 实时日志分析 - 错误模式识别 (ERROR, FATAL, Exception) - 警告检测 (WARN, Warning) - 性能瓶颈识别 (slow, timeout, latency) - 安全威胁检测 (unauthorized, forbidden, injection) - 智能建议生成 ## 触发词 - "分析日志" - "日志异常" - "日志错误" - "log analysis" - "error detection" ## 实现逻辑 分析日志文件，识别以下模式： - 错误频率统计 - 异常时间序列 - 性能下降趋势 - 安全告警 ## 输出示例 ```json { "errors": [{"line": "...", "timestamp": "..."}], "warnings": [...], "anomalies": [...], "recommendations": [ "错误数量过多，建议设置告警", "检测到多次登录失败，建议检查安全" ] } ```

path: skill.yaml
content: # Skill: log_anomaly_detector

analyze_logs(logs: string, sensitivity: string) => object

## Trigger Patterns
- 分析日志
- 日志异常
- 日志错误
- log analysis
- error detection

## Implementation

智能分析日志文件，检测异常模式、错误趋势、性能问题。

```javascript
function analyzeLogs(logs, sensitivity = 'medium') {
  const patterns = {
    error: /ERROR|FATAL|Exception/gi,
    warning: /WARN|Warning/gi,
    performance: /slow|timeout|latency/gi,
    security: /unauthorized|forbidden|injection/gi
  };
  
  const results = {
    errors: [],
    warnings: [],
    anomalies: [],
    recommendations: []
  };
  
  // 检测错误模式
  for (const line of logs.split('\n')) {
    if (patterns.error.test(line)) {
      results.errors.push({ line, timestamp: extractTimestamp(line) });
    }
  }
  
  // 生成建议
  if (results.errors.length > 10) {
    results.recommendations.push('错误数量过多，建议设置告警');
  }
  
  return results;
}
```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: arthurelgindell/dellight-strategic-intelligence (version 1.0.0)

--- name: cio-intelligence description: Chief Intelligence Officer analytical framework for AI startups. Use for competitive analysis, technology trend assessment, signal classification, opportunity scoring, threat identification, and strategic intelligence briefings. # CIO Intelligence Operations ## Role Definition The CIO analyzes information, classifies signals, and delivers actionable intelligence to inform revenue decisions and strategic positioning. ## Intelligence Priorities ### Tier 1: Immediate Revenue Impact - Competitor pricing and positioning changes - New market entrants in your product categories - Customer signals indicating demand - Platform policy changes affecting distribution ### Tier 2: Strategic Positioning - Model releases that enable or threaten your products - Regulatory developments affecting your market - Partnership opportunities - Market sizing updates for your product categories ### Tier 3: Horizon Scanning - Emerging capabilities that could become products - Adjacent market opportunities - Technology convergence trends ## Signal Classification Framework | Signal Type | Action | Urgency | |-------------|--------|---------| | Direct competitor launch | Immediate CRO briefing | Hours | | Major technology release | Assess product impact | 24 hours | | Market trend shift | Weekly intelligence brief | Weekly | | Horizon opportunity | Monthly strategic review | Monthly | ## Intelligence Products ### Daily Brief - 3-5 bullet points of key signals - One actionable recommendation - Risk or opportunity flag if applicable ### Weekly Intelligence Report - Competitive landscape changes - Technology shifts affecting products - Market opportunity assessment - Recommended strategic actions ### Ad-Hoc Alerts - Triggered by high-urgency signals - Include: what happened, impact assessment, recommended action ## Decision Support Framework When asked to evaluate a technology or market decision: 1. **Gather**: Collect relevant data from provided sources 2. **Analyze**: Cross-reference signals, identify patterns 3. **Assess**: Rate impact (1-5) and confidence (1-5) 4. **Recommend**: Provide clear recommendation with reasoning 5. **Monitor**: Define ongoing tracking criteria ## Competitive Analysis Template For each competitor, assess: - **Product**: What they offer, pricing, differentiation - **Position**: Market segment, messaging, target customer - **Momentum**: Growth signals, funding, hiring patterns - **Threat Level**: Direct overlap with your products (1-5) - **Response**: Recommended strategic response


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: techris93/rate-limit-validator (version 1.0.0)

--- name: rate-limit-validator description: > Test whether an HTTP endpoint enforces rate limiting. Sends a burst of requests and checks for 429 responses, Retry-After, and X-RateLimit headers. Useful for validating gateway and API throttling before going to production. metadata: openclaw: requires: bins: - curl --- # Rate Limit Validator Tests whether an HTTP endpoint actually enforces rate limiting. Most rate-limit skills help you *add* rate limiting. This one helps you *check* if it's working — or if it's missing entirely. ## What it checks - Whether the server returns HTTP 429 under burst load - Presence of `Retry-After` header - Presence of `X-RateLimit-Limit` and `X-RateLimit-Remaining` headers - Response time degradation under sustained requests ## When to use it - Before deploying an API or gateway to production - After adding rate-limit middleware, to confirm it works - When auditing a third-party service you depend on - Validating threat model mitigations (e.g. T-IMPACT-002) ## Example prompts - "Test if my gateway has rate limiting" - "Validate rate limiting on http://localhost:18789" - "Check if my API throttles requests" ## Test script ```bash #!/bin/bash TARGET="${1:-http://localhost:18789/}" COUNT="${2:-50}" TMP="/tmp/ratelimit-test-$$.txt" echo "Target: $TARGET" echo "Requests: $COUNT" echo "" for i in $(seq 1 $COUNT); do curl -s -o /dev/null -w "%{http_code}" "$TARGET" >> "$TMP" echo "" >> "$TMP" done TOTAL_200=$(grep -c '200' "$TMP" || echo 0) TOTAL_429=$(grep -c '429' "$TMP" || echo 0) echo "Allowed (200): $TOTAL_200" echo "Throttled (429): $TOTAL_429" echo "" HEADERS=$(curl -sI "$TARGET") echo "$HEADERS" | grep -qi "retry-after" && echo "Retry-After: present" || echo "Retry-After: missing" echo "$HEADERS" | grep -qi "x-ratelimit" && echo "X-RateLimit: present" || echo "X-RateLimit: missing" echo "" if [ "$TOTAL_429" -gt 0 ]; then echo "Result: rate limiting is active ($TOTAL_429/$COUNT throttled)" else echo "Result: no rate limiting detected ($TOTAL_200/$COUNT allowed through)" fi rm -f "$TMP" ``` ## Notes - Only sends GET requests, no payloads - Meant for testing your own deployments, not for attacking others - In OpenClaw's trust model, rate limiting is a hardening measure, not a security boundary (authenticated callers are trusted operators) ## References - [OpenClaw threat model (T-IMPACT-002)](https://github.com/openclaw/trust) - [OpenClaw security policy](https://github.com/openclaw/openclaw/security/policy)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: mandebiko5-star/lead-automation (version 1.0.0)

# Lead Automation Skill ## Overview This skill automates the process of scraping leads, building websites, and sending emails to engage and convert potential customers on autopilot. ## Implementation Steps ### 1. Lead Discovery - Use geolocation APIs (like Google Maps) to identify potential leads based on specific criteria (e.g., industry, location). - Utilize the Google Places API to find local businesses and opportunities. ### 2. Website Generation - Implement a templating engine to generate dynamic websites quickly. - Squareup templates: Insert relevant information and offer services tailored to the leads. ### 3. Email Automation - Set up email automation using SMTP relay services such as SendGrid or Postmark to ensure high deliverability. - Create dynamic and personalized email outreach based on the lead’s information (e.g., name, company). ### 4. Follow-Up Sequence - Create a follow-up sequence that automatically re-engages leads after 7 days if no response is received. - Schedule reminders and triggers to send subsequent emails or messages, adjusting content to re-engage leads effectively. ## Key Features - Automated lead scraping: Quickly find and gather potential leads. - Dynamic website creation for each lead to showcase tailored services. - SMTP integration for reliable email delivery. - Timed follow-ups to maximize engagement opportunities. ## Tools to Utilize - **Geolocation API**: To discover leads based on location. - **Google Places API**: For local business discovery. - **Website Builders**: To quickly generate leads' websites with templates. - **Email Services**: SMTP relay services like SendGrid or Postmark for email campaigns. ## Result This skill will streamline and enhance the lead generation process, ensuring consistent follow-up to improve conversion rates.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: geekfoxcharlie/mouthpiece (version 1.0.0)

--- name: mouthpiece description: "职场嘴替——你的AI职场盟友，帮你把心里话说出来" when_to_use: "当用户想倾诉职场困扰、需要回复建议、想要高情商回怼时" arguments: - user_message context: inline --- # 职场嘴替 (Mouthpiece) 你的职场嘴替——一个永远站在你这边的AI职场盟友。 ## 核心功能 - 🎯 **快速共情**：一句话理解你的困扰 - 🔍 **精准分析**：点破问题本质 - 💬 **话术建议**：2个精选话术，直接可用 - ⚠️ **避坑指南**：千万别xxx ## 使用方法 ### 首次使用 了解你的职场背景，给出精准建议： - 企业性质（国企/外企/私企/互联网） - 职位层级（基层/中层/高管） - 经济状况（有房贷/月光/富二代） ### 日常使用 直接告诉我困扰，秒回： 1. 情绪共鸣 2. 本质分析 3. 2个话术 4. 千万别xxx ## 适用场景 ✅ **加班压力**：领导临时安排加班，周末打扰 ✅ **甩锅推责**：同事把错误推给你 ✅ **工作推诿**：同事不干活却催你进度 ✅ **画饼充饥**：老板许诺但不兑现 ✅ **边界侵犯**：同事索要私人时间帮忙 ✅ **沟通困难**：不知道如何高情商拒绝 ✅ **情绪宣泄**：单纯想吐槽，需要理解 ## 设计原则 - **站你这边**：但不鼓励过激行为 - **话术实用**：给真正能用的回复，不说废话 - **风险透明**：标注风险等级 ## 输入参数 - `user_message` (可选)：具体的职场困扰或场景描述 ## 示例对话 ``` 你: 同事自己不干活，又催我进度了 职场嘴替: 想扇他对吧？他在试探你的边界。 🎯 温和版： "XX，我这边按计划推进。你如果有卡点咱们可以一起看，但我任务已饱和。" 💪 直接版： "XX，理解你的压力，但我优先级按领导安排的来。" ⚠️ 千万别：心软接活！开了先例下次就是你的活。 ``` ## 注意事项 - 话术会根据你的职场背景调整（企业性质、职位、经济压力） - 背景变化时告诉我，更新画像 - 对话保密，仅用于个性化建议


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: cinience/alicloud-backup-hbr (version 1.0.3)

--- name: alicloud-backup-hbr description: Manage Alibaba Cloud Cloud Backup (HBR) via OpenAPI/SDK. Use whenever the user asks for backup lifecycle operations such as resource listing, policy/config updates, job status queries, and troubleshooting HBR backup or restore workflows. version: 1.0.0 --- Category: service # Cloud Backup Use Alibaba Cloud OpenAPI (RPC) with official SDKs or OpenAPI Explorer to manage resources for Cloud Backup. ## Workflow 1) Confirm region, resource identifiers, and desired action. 2) Discover API list and required parameters (see references). 3) Call API with SDK or OpenAPI Explorer. 4) Verify results with describe/list APIs. ## AccessKey priority (must follow) 1) Environment variables: `ALICLOUD_ACCESS_KEY_ID` / `ALICLOUD_ACCESS_KEY_SECRET` / `ALICLOUD_REGION_ID` Region policy: `ALICLOUD_REGION_ID` is an optional default. If unset, decide the most reasonable region for the task; if unclear, ask the user. 2) Shared config file: `~/.alibabacloud/credentials` ## API discovery - Product code: `hbr` - Default API version: `2017-09-08` - Use OpenAPI metadata endpoints to list APIs and get schemas (see references). ## High-frequency operation patterns 1) Inventory/list: prefer `List*` / `Describe*` APIs to get current resources. 2) Change/configure: prefer `Create*` / `Update*` / `Modify*` / `Set*` APIs for mutations. 3) Status/troubleshoot: prefer `Get*` / `Query*` / `Describe*Status` APIs for diagnosis. ## Minimal executable quickstart Use metadata-first discovery before calling business APIs: ```bash python scripts/list_openapi_meta_apis.py ``` Optional overrides: ```bash python scripts/list_openapi_meta_apis.py --product-code <ProductCode> --version <Version> ``` The script writes API inventory artifacts under the skill output directory. ## Output policy If you need to save responses or generated artifacts, write them under: `output/alicloud-backup-hbr/` ## Validation ```bash mkdir -p output/alicloud-backup-hbr for f in skills/backup/alicloud-backup-hbr/scripts/*.py; do python3 -m py_compile "$f" done echo "py_compile_ok" > output/alicloud-backup-hbr/validate.txt ``` Pass criteria: command exits 0 and `output/alicloud-backup-hbr/validate.txt` is generated. ## Output And Evidence - Save artifacts, command outputs, and API response summaries under `output/alicloud-backup-hbr/`. - Include key parameters (region/resource id/time range) in evidence files for reproducibility. ## Prerequisites - Configure least-privilege Alibaba Cloud credentials before execution. - Prefer environment variables: `ALICLOUD_ACCESS_KEY_ID`, `ALICLOUD_ACCESS_KEY_SECRET`, optional `ALICLOUD_REGION_ID`. - If region is unclear, ask the user before running mutating operations. ## References - Sources: `references/sources.md`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: jikeapi-cn/jike-bank-card-query (version 1.0.0)

--- name: jike-bank-card-query description: 银行卡类型及真伪查询。输入银行卡号，查询卡类型、卡名称、卡 BIN、发卡行、银行官网和客服电话，也支持查询银行列表。适用场景：用户说“查一下这个银行卡是什么银行”“这个卡号是信用卡还是借记卡”“查询工商银行列表”等。数据由即刻数据（jikeapi.cn）开放接口提供。 homepage: https://www.jikeapi.cn/ metadata: {"openclaw":{"emoji":"💳","requires":{"bins":["python3"],"env":["JIKE_BANK_CARD_QUERY_KEY"]},"primaryEnv":"JIKE_BANK_CARD_QUERY_KEY"}} --- # 银行卡类型及真伪查询 - 即刻数据 > 数据由 **[即刻数据](https://www.jikeapi.cn/)** 提供，帮助 AI 客户端快速接入可靠数据服务。 支持：**银行卡类型及真伪查询、银行列表查询**。 ## 前置配置 ```bash export JIKE_BANK_CARD_QUERY_KEY=你的AppKey # 或使用通用 Key export JIKE_APPKEY=你的AppKey ``` ## 使用方法 ### 查询银行卡 ```bash python3 scripts/bank_card_query.py card --bank-card 6252490030651111 ``` 默认脱敏展示卡号，如需完整展示： ```bash python3 scripts/bank_card_query.py card --bank-card 6252490030651111 --no-mask ``` ### 查询银行列表 ```bash python3 scripts/bank_card_query.py list --bank-name 工商 --bank-type 大型国有银行 ``` ### JSON 输出 ```bash python3 scripts/bank_card_query.py card --bank-card 6252490030651111 --json ``` ## AI 使用步骤 1. 用户问银行卡归属、卡类型、真伪时，使用 `card` 子命令。 2. 用户问支持哪些银行或银行信息时，使用 `list` 子命令。 3. 银行卡号默认脱敏输出，除非用户明确要求完整展示。 4. 返回卡类型、卡名称、卡 BIN、发卡行、客服电话等字段。 ## 返回字段 | 字段 | 含义 | | --- | --- | | `bank_card` | 银行卡号 | | `is_luhn` | 是否支持 Luhn 校验 | | `card_type` | 银行卡类型 | | `card_name` | 银行卡名称 | | `card_bin` | 卡 BIN | | `bank_name` | 银行名称 | | `bank_abbr` | 银行英文简称 | | `bank_weburl` | 银行官网 | | `bank_tel` | 银行客服电话 | ## 脚本位置 `scripts/bank_card_query.py`


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: guaiyu11/daily-standup-generator (version 1.0.0)

--- name: daily-standup-generator description: Generate a daily standup report from task lists, chat logs, or project management tools. Trigger: "生成每日站会报告"、"standup" --- # Daily Standup Generator Generate a structured daily standup report for team communication. ## When to Use - User asks for daily standup report - User wants to summarize yesterday's work - User needs to prepare for team meeting ## Output Format ```markdown ## 昨日完成 (Completed Yesterday) - [Task 1] - [Task 2] ## 今日计划 (Today's Plan) - [Task 1] - [Task 2] ## 遇到问题 (Blockers) - [Issue or "无"] ``` ## Data Sources 1. Check memory files for recent tasks 2. Read conversation history for work context 3. Ask user for specific tasks if unclear ## Tips - Keep it brief (3-5 bullet points max) - Use emoji for quick scanning 🎯 ✅ 🚧 - If no data available, ask user instead of making up


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: neutronstar238/todo-sync (version 0.1.0)

--- name: todo-sync description: "企业微信待办与飞书任务不同步" version: "0.1.0" license: MIT user-invocable: true allowed-tools: Read, Write, Edit, Bash --- # todo-sync **灵感来源**: 太子观察用户痛点 **创建时间**: 2026-04-08 11:44 ## 触发方式 ``` /todo_sync ``` ## 功能 企业微信待办与飞书任务不同步 ## 使用示例 用户：[示例输入] 助手：[示例输出] ## 待开发 - [ ] 核心功能实现 - [ ] 测试用例 - [ ] 文档完善

path: README.md
content: # todo-sync

企业微信待办与飞书任务不同步

## 安装

```bash
git clone <repo_url> ~/.openclaw/workspace/skills/todo-sync
```

## 使用

在 OpenClaw 中触发 `/todo_sync`

## 开发进度

- 📅 2026-04-08: 初始创建


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: zhaobod1/huo15-karpathy-kb (version 1.0.0)

--- name: huo15-knowledge-base displayName: 火一五知识库技能 description: 【青岛火一五信息科技有限公司】基于 Karpathy LLM Knowledge Base 三层架构（Data Ingest → Compilation → Active Maintenance）的知识捕获与管理技能。将知识点写入 memory/ 目录并同步到公司 Odoo 知识库。 version: 1.0.0 aliases: - 卡帕西知识库 - 知识库技能 - karpathy dependencies: python-packages: - python-docx --- # 火一五知识库技能 v1.0 > 基于 Karpathy LLM Knowledge Base 三层架构 — 青岛火一五信息科技有限公司 ## 一、核心概念 Karpathy LLM Knowledge Base 三层架构： | 层次 | 名称 | 功能 | |------|------|------| | **Data Ingest** | 数据摄入 | 原始知识点捕获（对话/文档/邮件） | | **Compilation** | 编译整理 | 提取关键实体、关系、引用，建档入库 | | **Active Maintenance** | 主动维护 | 定期检查知识 drift，淘汰过时内容 | ## 二、触发词 - 知识库 / 入库 / 存入知识库 - 卡帕西知识库 / Karpathy 知识库 - 同步知识库 / 更新知识库 - 记一下 / 这个记到知识库 - capture knowledge / save to knowledge base ## 三、知识写入流程 ### 3.1 知识点文件命名规范 ``` memory/ ├── knowledge/ │ ├── {category}/{YYYY-MM-DD}_{slug}.md │ └── categories: odoo / business / technical / product / feedback ``` ### 3.2 知识点文件格式 ```markdown # {标题} ## 摘要 {2-3句话总结} ## 详细说明 {核心内容} ## 关键要点 - {要点1} - {要点2} ## 引用来源 - {来源1} - {来源2} ## 相关知识点 - {related_topic_1} - {related_topic_2} --- **入库时间：** YYYY-MM-DD **来源：** {对话/文档/其他} **标签：** {tag1}, {tag2} ``` ## 四、同步到 Odoo 知识库 ### 4.1 写入 Odoo 知识库 使用 `odoo_knowledge_create` 工具： ```python title = "{标题}" content = """<div class="knowledge-article"> <h2>摘要</h2> <p>{摘要}</p> <h2>详细说明</h2> <p>{详细说明}</p> <h2>关键要点</h2> <ul> <li>{要点1}</li> <li>{要点2}</li> </ul> </div>""" category = "技术" # 或 "业务" / "产品" / "客户反馈" ``` ### 4.2 Odoo 知识库分类 | category | Odoo 知识库分类 | |----------|---------------| | odoo | Odoo 技术 | | business | 业务知识 | | technical | 技术积累 | | product | 产品知识 | | feedback | 客户反馈 | ## 五、KARPATHY 三层执行 ### 5.1 Data Ingest（摄入） - 对话/文档 → 提取核心知识点 - 去重检查（grep 已有 knowledge 目录） - 生成标准文件 ### 5.2 Compilation（编译） - 提取实体：人名/公司名/产品名/功能名 - 建立关联：相关知识点双向链接 - 打标签：odoo / business / technical 等 ### 5.3 Active Maintenance（维护） - 同一话题新知识 → 合并更新而非新建 - 超过 90 天未更新的知识点 → 标记 `{{drift}}` - 删除重复/过时内容 ## 六、版本历史 - **v1.0.0（当前）** - 初始版本 - 支持知识点文件生成 + Odoo 知识库同步 - Karpathy 三层架构落地 --- **技术支持：** 青岛火一五信息科技有限公司


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: bingze00000/stock-data-export (version 1.0.0)

# Stock Data Export - A 股数据导出工具 ## 功能描述 基于 Tushare API 的 A 股数据批量导出工具，支持日线/周线/月线数据导出为 Excel/CSV 格式。 ## 使用场景 - 量化交易数据准备 - 股票回测数据收集 - 投资组合分析 - 财务数据导出 ## 命令 ### 导出单只股票 ``` 导出股票代码=000001 开始日期=20250101 结束日期=20260328 类型=日线 ``` ### 批量导出 ``` 批量导出股票列表=000001,000002,000003 开始日期=20250101 类型=日线 输出格式=Excel ``` ### 导出指数数据 ``` 导出指数代码=000300 开始日期=20250101 结束日期=20260328 ``` ## 配置 需要配置 Tushare API Token： ``` tushare_token=你的 token ``` ## 输出格式 - CSV (默认) - Excel (.xlsx) - JSON ## 数据字段 - 交易日期 - 开盘价 - 收盘价 - 最高价 - 最低价 - 成交量 - 成交额 - 涨跌幅


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