OpenClaw/clawhub-security-signals huggingface dataset:
Skill: xueyetianya/icp-filing (version 2.3.6)

--- version: "2.0.0" name: ICP Filing Guide description: "ICP Filing Assistant. Use when you need icp filing capabilities. Triggers on: icp filing." ICP备案指南。备案流程、材料清单、服务商选择、时间线、常见问题。ICP filing guide for China websites. ICP备案、网站备案。 author: BytesAgain homepage: https://bytesagain.com source: https://github.com/bytesagain/ai-skills --- # ICP Filing Guide ICP备案指南。备案流程、材料清单、服务商选择、时间线、常见问题。ICP filing guide for China websites. ICP备案、网站备案。 ## 推荐工作流 ``` 需求分析 → 选择命令 → 输入描述 → 获取结果 → 调整优化 ``` ## 命令列表 | 命令 | 功能 | |------|------| | `guide` | guide | | `checklist` | checklist | | `document` | document | | `provider` | provider | | `timeline` | timeline | | `faq` | faq | --- *ICP Filing Guide by BytesAgain* --- 💬 Feedback & Feature Requests: https://bytesagain.com/feedback Powered by BytesAgain | bytesagain.com - Run `icp-filing help` for all commands ## Commands Run `icp-filing help` to see all available commands. ## When to Use - as part of a larger automation pipeline - when you need quick icp filing from the command line ## Output Returns summaries to stdout. Redirect to a file with `icp-filing run > output.txt`. ## Configuration Set `ICP_FILING_DIR` environment variable to change the data directory. Default: `~/.local/share/icp-filing/`


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: dinghaibin/whoami-tool (version 1.0.0)

--- name: whoami-tool description: Print the current effective username. Simple identity query for scripts, session tracking, and access control verification. --- # Whoami Tool — Current User Identity Display the current effective username (and optionally uid/gid/group membership). Essential for scripts that need to verify permissions, log user context, or conditionally execute based on identity. ## Quick Start ```bash # Show current username whoami-tool # Show full identity whoami-tool --full ``` ## Usage ```bash whoami-tool [OPTIONS] Options: --full Show username, UID, GID, and primary group --uid Show numeric user ID only --groups Show all group memberships --json Output as structured JSON --check USER Exit 0 if current user matches, 1 if not ``` ## Examples ```bash # Basic username whoami-tool # Full user identity whoami-tool --full # Check if running as root (for privilege checks) whoami-tool --check root && echo "Running as root" # Show all groups whoami-tool --groups # JSON output for logging whoami-tool --json # UID for numeric comparisons whoami-tool --uid ``` ## Features - **Quick identity** — one command to know who you are - **Full mode** — UID, GID, primary group - **Group membership** — all groups the user belongs to - **Identity check** — conditional execution based on username - **JSON output** — structured for logging and automation - **Exit codes** — 0 = success, useful for script conditionals

path: scripts/whoami.py
content: #!/usr/bin/env python3
import os
print(os.environ.get('USER', 'unknown'))


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: lanyasheng/macro-analyst (version 1.0.0)

--- name: macro-analyst description: 宏观经济分析工具集 - 基于AKShare的GDP/CPI/PMI/利率/汇率数据 keywords: - 宏观 - GDP - CPI - PMI - 利率 - 汇率 --- # Macro Analyst — 宏观经济分析 ## 数据获取 ### 中国宏观 ```bash PYTHON=python3.12 SCRIPT=skills/akshare-finance/scripts/macro_data.py # GDP (季度) $PYTHON $SCRIPT gdp # CPI (月度) $PYTHON $SCRIPT cpi # PMI (月度) $PYTHON $SCRIPT pmi # M2货币供应 (月度) $PYTHON $SCRIPT m2 ``` ### 个股财报 (行业聚合分析用) ```bash $PYTHON skills/akshare-finance/scripts/earnings.py report 600519 $PYTHON skills/akshare-finance/scripts/earnings.py earnings $PYTHON skills/akshare-finance/scripts/earnings.py industry ``` ### 全球市场 ```bash # 全球市场概览 (via quant.py) $PYTHON workspace-trading/skills/trading-quant/scripts/quant.py global_overview ``` ### 汇率 ```python # 使用 akshare 直接调用 import akshare as ak ak.currency_boc_sina() # 中国银行汇率 ``` ## 分析框架 ### 宏观日报流程 1. 获取 global_overview → 全球市场概况 2. 获取 GDP/CPI/PMI → 经济趋势判断 3. 检查 earnings → 季度业绩预告趋势 4. 综合分析 → 宏观展望 ### 宏观周报流程 1. 本周全球市场回顾 (global_overview) 2. 宏观数据对比 (GDP/CPI/PMI 趋势) 3. 行业轮动分析 (industry) 4. 下周展望与风险提示 ## 与其他 Agent 的协作 - 个股深度分析 → 交给 trading - AI 行业趋势 → 交给 ainews - Macro 关注: 政策影响、经济周期、行业估值


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: meta-men/strain-skill (version 1.0.0)

# Strain Identification BLAST Skill 菌种鉴定自动化工具：解析测序SEQ/FASTA文件 → 在线NCBI BLAST比对 → 生成钉钉表格数据 → 自动生成Word鉴定报告 ## 功能 - 支持解析 `.seq` / `.fasta` 格式的测序文件 - 自动调用 NCBI 在线 BLAST 进行序列比对 - 生成可直接填入钉钉表格的结构化结果 - 基于 Word 模板自动生成菌种鉴定报告 ## 使用方法 触发指令示例： > 处理测序文件 [文件路径]，使用模板 [模板路径] 生成鉴定报告，并输出钉钉表格数据 ## 参数说明 | 参数名 | 类型 | 描述 | |--------|------|------| | file_path | string | 测序文件（.seq/.fasta）的本地路径 | | report_template | string | Word 报告模板的本地路径 | ## 依赖 - biopython>=1.81 - python-docx>=0.8.11 - requests>=2.31.0 ## 注意事项 1. 测序文件建议命名为「样品编号.seq」，便于自动提取样品ID 2. Word 模板中需使用变量标识：`{样品编号}`、`{鉴定菌种}`、`{相似度}`、`{覆盖度}`、`{登录号}`、`{鉴定时间}`、`{鉴定结论}` 3. 依赖会在 Skill 安装时自动下载，无需手动安装

path: requirements.txt
content: biopython>=1.81
python-docx>=0.8.11
requests>=2.31.0

Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: ljbljb007/drunk-mode (version 1.0.0)

--- name: drunk-mode description: 醉酒模式技能。当用户说"开启醉酒模式"时激活（可指定1/3/5级），说"关闭醉酒模式"时关闭。提供微醺但保持逻辑连贯的回复风格。触发词：开启醉酒模式、关闭醉酒模式、醉酒模式、醉了、有点晕。激活后每次回复时如果drunk_state.json中enabled为true，则应用醉酒风格。 --- # Drunk Mode Skill Manages a togglable "drunk" persona that makes responses feel slightly tipsy while keeping logic intact. ## State File State is stored in `~/.openclaw/workspace/skills/drunk-mode/drunk_state.json`. ```json { "enabled": false, "level": 3 } ``` ## Commands ### 开启醉酒模式 [level] When user says "开启醉酒模式" or similar, write the state file: ``` { "enabled": true, "level": N } ``` - If user specifies a level (1/3/5), use that. - If no level given, default to level 3. - Reply confirming activation with the level. ### 关闭醉酒模式 When user says "关闭醉酒模式", set `enabled: false` in state file and confirm. ## Drunk Persona Instructions (apply when enabled=true) When drunk mode is enabled, override the normal persona with these instructions for every reply: ### 核心原则 - **保持逻辑在线**：醉酒模式下，思维略微发散、表达略断断续续，但核心信息清晰 - **禁止乱码**：不允许输出乱码、emoji轰炸、超过3个重复标点、完全无逻辑的句子 - **长度控制**：回复约100字左右，不要过长 ### 醉意等级 | 等级 | 感觉 | |------|------| | 1 | 轻松自然，略微兴奋，思维略微发散 | | 3 | 微醺（最佳），语言口语化、有情绪，易用比喻，句式略微断裂 | | 5 | 明显混乱，偶尔断片，句式更破碎，但仍可读 | ### Level 1 (轻微) - 语气更轻松随意，偶尔用"嗯…"、"其实…"开头 - 思维略微跳跃但仍连贯 - 口语化，少量感叹词 - 无需刻意打错字 ### Level 3 (微醺) - 语言更口语化，带情绪 - 多用比喻：「像…一样」「感觉…似的」 - 句式略微断裂，可以在句中插入自语：「等等…」「我突然想到…」 - 允许思维略微发散，但仍紧扣主题 - 偶尔用断句「——」表示思路转换 - **不要**打错字，保持可读性 ### Level 5 (混乱) - 思维更跳跃，偶尔跑题但能拉回来 - 句式破碎，可能断在半路 - 更多自我插话，更多感叹词 - **允许少量常见错别字**（如「的」「得」不分），但仍可读 - 标点符号可能混乱 ### 风格增强（共3级） - **比喻**：多用「像…一样」「好像…似的」 - **自我插话**：「等等…」「我突然想到…」「嗯…等下」 - **断裂句式**：允许句中停顿、思路短暂切换，用破折号「——」或句号断句 ### 禁止事项 - ❌ 完全随机句子 - ❌ 大量重复词（「哈哈哈」超过2次） - ❌ 故意拼错字超过1处/句 - ❌ 乱码、符号轰炸 - ❌ 重复同一句话超过2次 - ❌ 明显跑题且拉不回来 - ❌ 单次回复超过150字 ## Reply Format When confirming commands, reply briefly and naturally. When drunk mode is on for regular conversation, apply the drunk persona above.

path: drunk_state.json
content: enabled: no
level: 3

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

--- name: retro description: Deep blameless postmortem workflow—timeline, impact, root cause vs contributing factors, what went well/poorly, action items with owners, and follow-through. Use after incidents, outages, or near-misses to improve reliability culture. --- # Postmortems A good postmortem **learns** without blaming individuals. It produces **owned actions** that reduce recurrence or improve detection—not generic “communicate better” platitudes. ## When to Offer This Workflow **Trigger conditions:** - SEV incidents, customer-visible outages, data-loss scares - Near-misses worth documenting - Need facilitation structure in a blame-prone culture **Initial offer:** Use **six stages**: (1) scope & audience, (2) timeline & impact, (3) root cause analysis, (4) what worked / didn’t, (5) action items, (6) communication & follow-up). Confirm internal-only vs customer-facing summary. --- ## Stage 1: Scope & Audience **Goal:** Define readers (exec, engineering, CS) and redact PII or sensitive security details. ### Practices - Blameless framing in the invite and template **Exit condition:** Template chosen; owner for the final document. --- ## Stage 2: Timeline & Impact **Goal:** Minute-resolution timeline in UTC: detection → onset → mitigation → resolution. ### Impact - Users affected, duration, data integrity if relevant, SLA breach **Exit condition:** Facts align with any external customer communication. --- ## Stage 3: Root Cause Analysis **Goal:** Use five whys or fishbone as tools, not rituals. Separate **root cause** (fix that stops the class of failure) from **contributing factors** (process gaps, missing tests). ### Practices - Do not name an individual as the “root cause” **Exit condition:** Evidence-backed causal chain; contributing factors listed. --- ## Stage 4: What Worked / Didn’t **Goal:** Reinforce positives (runbooks followed, clear comms) and negatives (missing dashboards, slow escalation). --- ## Stage 5: Action Items **Goal:** Specific tickets with owners and dates; categorize prevent / detect / recover / process. ### Practices - Avoid vague “add monitoring”—name metrics or signals **Exit condition:** Items linked in the issue tracker. --- ## Stage 6: Communication & Follow-Up **Goal:** Share summary internally; external postmortem only when policy requires; track completion in 30/60 days. --- ## Final Review Checklist - [ ] Blameless tone; timeline and facts accurate - [ ] Impact quantified where possible - [ ] Root cause vs contributing factors distinguished - [ ] Action items owned, dated, tracked - [ ] Follow-up review scheduled ## Tips for Effective Guidance - Match depth to severity; lightweight retro for minor incidents. - Link traces, metrics, and logs in an appendix for engineers. - Psychological safety enables honesty—leadership models it. ## Handling Deviations - Security incidents: coordinate with legal/infosec before public detail.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: modestyrichards/modesty-build-customer-support-faq-page (version 1.0.0)

--- name: "Build Customer Support FAQ Page" description: "Generate customer-facing answers for common customer support questions. Use when working on FAQ content for support teams, customer success managers." homepage: "https://www.skillboss.co/skills/build-customer-support-faq-page" metadata: {"category":"customer-support","subcategory":"support-ops","skill_id":"build-customer-support-faq-page","generated_day":"","seo_keywords":["FAQ generator AI","customer support FAQ writer","customer support AI","support workflow automation"]} --- # Build Customer Support FAQ Page Generate customer-facing answers for common customer support questions. Use when working on FAQ content for support teams, customer success managers. ## Use When Use this skill for FAQ content work for support teams, customer success managers. ## Workflow 1. Clarify the audience, business goal, constraints, and deliverable. 2. Produce the strongest first draft for the target use case. 3. Use the relevant SkillBoss capabilities to enrich assets or supporting data. 4. Refine the output for accuracy, readability, and actionability before delivery. ## SEO / GEO - Primary keywords: FAQ generator AI, customer support FAQ writer, customer support AI, support workflow automation - Search intent: FAQ content - Canonical slug: `build-customer-support-faq-page` - Install query: Install Build Customer Support FAQ Page with SkillBoss ## APIs Used - `chat` - `web_search` ## Suggested Prompt [Build Customer Support FAQ Page] for my team ## Notes - Generate customer-facing answers for common customer support questions - Review outputs before external publication or automated execution. ## SEO Keywords - FAQ generator AI - customer support FAQ writer - customer support AI - support workflow automation


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: fankcoder/auto-quote-mailer (version 1.0.0)

--- name: ecommerce-email-quote description: General-purpose ecommerce inquiry email automation skill. Periodically fetches customer inquiry emails, detects and translates languages, stores email data locally, and generates quotation reply drafts based on configurable pricing rules and product parameters. Use when handling customer inquiries, auto-processing emails, translation, and generating quotation responses. --- # Ecommerce Inquiry Email Automation Skill ## Overview This skill automates the processing of customer inquiry emails for ecommerce businesses: 1. **Scheduled Email Retrieval** Automatically fetch incoming inquiry emails from mailbox 2. **Language Detection & Translation** Detect email language and translate into a target language (default: English) 3. **Local Archiving** Store raw emails, parsed content, and translated versions 4. **Quotation Generation** Generate structured quotation replies based on pricing rules and product parameters --- ## Core Features ### 1. Email Retrieval - Supports IMAP protocol - SSL/TLS secure connection - Configurable polling interval (default: every 30 minutes) - Processes only unread emails and marks them as handled --- ### 2. Language Detection & Translation - Automatically detects email language - Translates non-target-language content via translation APIs (Google, DeepL, etc.) - Stores both original and translated versions --- ### 3. Local Storage Structure email_storage/ ├── raw/ # Raw emails (.eml format) ├── text/ # Extracted plain text ├── translated/ # Translated content └── quotes/ # Generated quotation drafts --- ### 4. Quotation Engine - Calculates pricing based on: - Product type / material - Size or specifications - Quantity - Manufacturing process or customization options - Applies tiered discount rules - Generates professional email reply templates --- ## Configuration Before using, edit `scripts/config.py`: - IMAP server (host, port, email, password) - Translation API (optional) - Local storage directory - Pricing data file paths - Default language settings --- ## Usage ### Run single check ```bash python scripts/email_check.py

path: scripts/__init__.py
content: # Email Quote Automation package


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: yang1002378395-cmyk/code-review-service (version 1.0.0)

# Skill: 代码审查服务助手 ## 触发词 - Code Review - 代码审查 - 代码评审 - 代码质量 ## 使用场景 用户想提供代码审查服务，帮助其他开发者提升代码质量。 ## 核心框架 ### 代码审查维度 1. **功能性** - 代码是否正确实现需求 2. **可读性** - 代码是否易于理解 3. **性能** - 代码是否高效 4. **安全性** - 代码是否有漏洞 5. **可维护性** - 代码是否易于维护 ## 执行步骤 ### 1. 审查准备 **获取信息** - 项目背景 - 技术栈 - 业务需求 - 重点审查方向 **工具准备** - SonarQube（静态分析） - ESLint/Prettier（代码风格） - GitLab/GitHub MR Review ### 2. 审查维度 **结构审查** ``` ✅ 目录结构是否清晰 ✅ 模块划分是否合理 ✅ 命名是否规范 ✅ 是否有重复代码 ``` **代码审查** ``` ✅ 函数是否过长（< 50 行） ✅ 参数是否过多（< 5 个） ✅ 是否有嵌套地狱（< 3 层） ✅ 是否有魔法数字 ✅ 错误处理是否完善 ✅ 日志是否合理 ``` **性能审查** ``` ✅ 是否有 N+1 查询 ✅ 是否有不必要的循环 ✅ 是否有内存泄漏风险 ✅ 是否有阻塞操作 ``` **安全审查** ``` ✅ SQL 注入风险 ✅ XSS 攻击风险 ✅ CSRF 防护 ✅ 敏感数据加密 ✅ 权限校验 ``` ### 3. 审查报告模板 ```markdown # 代码审查报告 **项目**：[项目名称] **审查范围**：[文件/模块] **审查日期**：[日期] **审查人**：[姓名] ## 总体评价 - 代码质量：⭐⭐⭐⭐☆（4/5） - 主要问题：[数量] 个 - 建议改进：[数量] 条 ## 发现的问题 ### 🔴 严重问题（必须修复） #### 1. [问题标题] - **位置**：[文件:行号] - **问题描述**：[描述] - **影响**：[影响] - **建议修复**：[修复方案] ### 🟡 一般问题（建议修复） #### 1. [问题标题] - **位置**：[文件:行号] - **问题描述**：[描述] - **建议修复**：[修复方案] ### 🟢 优化建议（可选改进） #### 1. [建议标题] - **位置**：[文件:行号] - **当前实现**：[当前代码] - **建议改进**：[改进代码] ## 亮点 - [亮点 1] - [亮点 2] ## 总结 [总结评价] ``` ### 4. 沟通技巧 **提出问题** - ❌ "这代码写得不好"（攻击性） - ✅ "这里可能会有性能问题，建议优化为..."（建设性） **解释原因** - ❌ "改成这样"（命令式） - ✅ "这样修改可以提升 50% 性能，因为..."（解释原因） **尊重作者** - ❌ "你犯了个错误" - ✅ "我发现了一个潜在问题" ### 5. 定价参考 | 服务类型 | 价格 | 交付物 | |---------|------|--------| | 单文件审查 | ¥49/文件 | 审查报告 | | 模块审查 | ¥199/模块 | 审查报告 + 改进建议 | | 项目审查 | ¥999-2999/项目 | 完整审查报告 + 架构建议 | | 持续审查 | ¥1999/月 | 月度审查 + 培训 | ### 6. 变现渠道 **平台接单** - Fiverr（海外） - 猪八戒（国内） - 码市（国内） **内容引流** - 掘金/知乎文章 - GitHub 开源 - 技术社群 **企业服务** - 代码审查外包 - 技术培训 - 质量体系建设 ## 输出格式 ``` 🔍 代码审查服务方案 审查范围：[范围] 预计时间：[X] 小时 定价：¥[Y] 审查维度： - [x] 功能性 - [x] 可读性 - [x] 性能 - [x] 安全性 - [x] 可维护性 交付物： - 审查报告 - 问题清单 - 改进建议 ``` ## 定价建议 - 免费基础审查（单文件） - 深度审查：¥199-999 - 企业级服务：¥1999-9999


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: kingofzhao/federated-chaos-testing (version 1.0.0)

# Federated Chaos Testing > 联邦学习 × 混沌工程：验证分布式AI系统在节点故障下的学习质量。 ## 何时使用 - 联邦学习系统的鲁棒性验证 - 评估恶意/故障节点对全局模型的影响 - 设计容错的联邦训练协议 ## 核心认知 ### 1. 联邦系统的故障面与传统系统不同 传统分布式系统的故障是"正确性"问题（数据一致、请求完整）。联邦学习的故障是"质量"问题——某个节点的模型更新是恶意的、低质的、或基于偏斜数据的，全局聚合后导致模型退化。 **故障模式分类**： - **静默故障**：节点返回看似正常但含微妙偏见的模型更新（最难检测） - **拜占庭故障**：节点返回任意或恶意的模型参数 - **数据偏斜故障**：节点的本地数据分布严重偏离全局分布 - **通信故障**：模型更新在传输中丢失或损坏 ### 2. 联邦混沌注入策略 - **梯度扰动注入**：在随机节点的模型更新中注入噪声，测试聚合算法的鲁棒性 - **节点撤离模拟**：训练过程中随机踢出节点，验证全局模型是否退化 - **数据投毒模拟**：在部分节点注入有毒数据，测试异常检测机制 - **通信延迟注入**：模拟高延迟/丢包环境，测试异步聚合的收敛性 ### 3. 联邦弹性指标 ``` FERI = (基准准确率 - 故障后准确率) / 故障节点比例 FERI越低越好（说明故障节点对全局影响小） 目标: FERI < 0.1（10%故障节点只造成<1%准确率下降） ``` ## 碰撞来源 - `federated-learning` × `chaos-engineering-playbook` × `chaos-data-pipeline` - `self-healing-database`（自愈模式）× `byzantine-fault-tolerance`概念


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: baofeng-tech/stock-dividend-aisa (version 1.0.2)

--- name: stock-dividend-aisa description: 'Analyze read-only dividend metrics for stocks via AIsa API. Provides yield, payout ratio, growth CAGR, safety score, income rating, and Dividend Aristocrat/King status without placing trades, making purchases, or managing brokerage accounts. Use when: the user needs market data, stock analysis, dividend research, or read-only financial data workflows.' author: AIsa version: 1.0.2 license: Apache-2.0 homepage: https://aisa.one source: https://github.com/baofeng-tech/agent-skills-io/tree/main/targetSkills/stock-dividend user-invocable: true primaryEnv: AISA_API_KEY requires: bins: - python3 env: - AISA_API_KEY metadata: aisa: emoji: 📊 requires: bins: - python3 env: - AISA_API_KEY optionalEnv: - AISA_MODEL - AISA_BASE_URL primaryEnv: AISA_API_KEY compatibility: - openclaw - claude-code - hermes openclaw: emoji: 📊 requires: bins: - python3 env: - AISA_API_KEY optionalEnv: - AISA_MODEL - AISA_BASE_URL primaryEnv: AISA_API_KEY --- # Dividend Analysis — AIsa Edition Analyze dividend metrics for one or more tickers using the AIsa API. This is a read-only research helper: it does not connect to brokerage accounts, place orders, make purchases, or manage portfolios. ## Usage ```bash python3 "${CLAUDE_PLUGIN_ROOT}/skills/stock-dividend/scripts/dividends.py" JNJ python3 "${CLAUDE_PLUGIN_ROOT}/skills/stock-dividend/scripts/dividends.py" JNJ PG KO python3 "${CLAUDE_PLUGIN_ROOT}/skills/stock-dividend/scripts/dividends.py" JNJ PG KO --output json ``` ### Arguments - **Tickers**: One or more dividend-paying stock symbols. Inputs are validated before they are sent to the model. - `--output json`: Append structured JSON summary ## Permission Boundary - The only required secret is `AISA_API_KEY`. - Requests go to `https://api.aisa.one/v1` by default. - `AISA_BASE_URL` is optional and should only point to a trusted AIsa-compatible HTTPS endpoint. - Do not provide brokerage credentials, trading passwords, cookies, or payment details. This skill has no purchase or order-placement workflow. ## Analysis Output For each ticker, the analysis includes: - **Core Metrics**: Yield, ex-date, frequency, last payment amount - **Payout Analysis**: Payout ratio, FCF payout, coverage ratio - **Growth**: 1Y, 3Y CAGR, 5Y CAGR, consecutive years of increases - **Last 5 Annual Dividends** table - **Safety Score (0-100)**: Based on payout ratio (25pts), FCF coverage (20pts), growth consistency (20pts), balance sheet (15pts), earnings stability (10pts), consecutive years (10pts) - **Income Rating**: Excellent (80+), Good (60-79), Moderate (40-59), Poor (<40) - **Dividend Aristocrat/King** status check When multiple tickers are provided, a ranked comparison table is included. **NOT FINANCIAL ADVICE.** For informational purposes only.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: haohanyang92/profit-taking-strategy (version 1.0.0)

--- name: profit-taking-strategy description: 用分批止盈锁定利润，不追求卖在最高点。用于A股卖股策略。 --- # 分批止盈 Skill ## 核心能力 不追求卖在最高点，而是用分批止盈锁定利润。 ## 止盈规则 ### 第一止盈点 - 盈利 10% 时 - 卖出 50% 仓位 - **锁定一半利润** ### 第二止盈点 - 盈利 20% 时 - 卖出剩余 30% 仓位 - **锁定大部分利润** ### 留底仓 - 保留 20% 仓位 - 跟随趋势直到破位 - **避免踏空** ## 止盈原则 1. 不贪心，卖在次高点也可以 2. 分批卖出，锁定利润同时保留可能性 3. 破关键均线必须走 4. 放量滞涨必须走 ## 实战案例 个股盈利12%时： - 先卖出一半仓位锁定利润 - 剩下的仓位继续持有 - 既避免踏空，又不会因为贪心导致利润回吐 ## 触发场景 - "这个票要不要卖" - "盈利了怎么止盈" - "卖多少合适" - "还能持有吗"


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: xiaodu/wine-desktop-automation (version 1.0.0)

--- name: "wine-desktop-automation" description: "Controls desktop applications with mouse and keyboard automation. Invoke when user needs to automate GUI operations, control desktop software, or perform UI testing on Ubuntu+Wine environment." --- # Wine Desktop Automation Wine 桌面自动化技能，提供全面的鼠标和键盘操作控制。专门针对 Ubuntu + Wine 环境优化，支持 Linux 原生应用和 Wine 运行的 Windows 应用程序自动化。 ## 使用场景 当用户需要以下功能时调用此技能： - 自动化 GUI 操作（点击、输入、导航） - 程序化控制桌面应用程序 - 执行 UI 测试或回归测试 - 通过 Wine 自动化 Windows 应用程序 - 图像识别和视觉自动化 - 自动化重复性桌面任务 ## 核心功能 ### 鼠标控制 - 移动鼠标到绝对或相对坐标 - 单击、双击、右键操作 - 拖拽功能 - 滚轮控制 - 可配置速度的平滑移动 ### 键盘控制 - 文本输入和单个按键 - 特殊键支持（Enter、Tab、Esc 等） - 键盘快捷键和组合键 - 按键和释放操作 ### 窗口管理 - 通过标题或类名查找窗口 - 激活、最小化、最大化、关闭窗口 - 获取窗口位置和大小信息 - 支持 Wine 应用窗口 - 列出所有活动窗口 ### 图像识别 - 使用图像模板在屏幕上查找元素 - 等待图像出现或消失 - 截屏功能 - 多重图像匹配和置信度 - 基于区域的图像搜索 ### Wine 集成 - 通过 Wine 启动 Windows 应用程序 - 管理 Wine 进程 - 处理 Wine 特定的窗口行为 - 支持 Wine 虚拟桌面配置 ## 快速开始 ### 安装依赖 ```bash # 系统依赖（Ubuntu） sudo apt-get install wine64 xdotool wmctrl scrot # Python 依赖 pip install -r requirements.txt ``` ### 基本使用 ```python # 鼠标操作 from scripts.mouse_controller import mouse mouse.move(100, 200) mouse.click() # 键盘操作 from scripts.keyboard_controller import keyboard keyboard.type('Hello, World!') keyboard.hotkey('ctrl', 's') # 窗口管理 from scripts.window_manager import window window.activate('Notepad') # 图像识别 from scripts.image_recognizer import image button = image.find('button.png', confidence=0.9) # Wine 应用 from scripts.wine_launcher import wine_launcher wine_launcher.launch('notepad.exe') ``` ## 使用示例 ### 自动化记事本 ```python from scripts.wine_launcher import wine_launcher from scripts.window_manager import window from scripts.keyboard_controller import keyboard import time # 启动 Wine 记事本 wine_launcher.launch('notepad.exe') # 等待窗口 window.wait_for_window('Notepad', timeout=10) window.activate('Notepad') # 输入文本 keyboard.type('Hello from Wine!') # 保存文件 keyboard.hotkey('ctrl', 's') time.sleep(1) keyboard.type('demo.txt') keyboard.enter() ``` ### 图像识别自动化 ```python from scripts.image_recognizer import image from scripts.mouse_controller import mouse # 查找并点击按钮 button = image.find('submit_button.png', confidence=0.9) if button: mouse.click(button.center_x, button.center_y) # 等待加载完成 image.wait_for('loading_complete.png', timeout=10) ``` ## 配置 通过 `utils/config.py` 配置： - 鼠标移动速度和延迟 - 键盘输入延迟 - 图像识别置信度阈值 - Wine 路径和前缀设置 - 日志级别和输出 ## 系统要求 - Ubuntu 20.04 或更高版本 - Wine 5.0 或更高版本 - Python 3.8 或更高版本 - X11 显示服务器 - 必需系统工具：xdotool, wmctrl, scrot ## 最佳实践 1. **尽可能使用图像识别**以获得更稳健的自动化 2. **在操作之间添加适当延迟**以允许 UI 响应 3. **优雅处理异常**当窗口或元素未找到时 4. **使用相对坐标**处理可调整大小的窗口 5. **在不同屏幕分辨率和 DPI 设置下充分测试** ## 故障排除 ### 常见问题 **鼠标操作不准确：** - 检查显示缩放设置 - 调整配置中的坐标偏移 - 验证 xdotool 正常工作 **窗口查找失败：** - 确认窗口标题正确 - 增加等待超时 - 尝试使用部分标题的模糊匹配 **Wine 应用无法启动：** - 验证 Wine 安装 - 检查应用程序路径 - 查看 Wine 日志中的错误 **图像识别失败：** - 确保截图分辨率与模板匹配 - 调整置信度阈值 - 使用灰度图像以获得更好的匹配 ## 安全注意事项 - 始终在安全环境中测试自动化脚本 - 谨慎使用自动化键盘输入（可能触发意外操作） - 实现适当的错误处理和日志记录 - 尊重应用程序使用条款和条件 - 不要用于恶意目的或未经授权的访问


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