OpenClaw/clawhub-security-signals huggingface dataset:
Skill: terrycarter1985/financial-report-automator (version 1.2.1)

--- name: financial-report-automator description: Automated quarterly financial report generation with dividend-adjusted total return calculations. Use when generating stock performance reports, quarterly earnings summaries, or return analytics for any ticker with CSV price data. version: 1.2.1 tags: ['finance', 'reporting', 'quarterly', 'returns', 'dividends'] --- # Financial Report Automator Generates quarterly stock performance reports with **dividend-adjusted total return**. ## What It Does - Parses CSV price data (Yahoo Finance export format) - Computes **price return** (raw close) and **total return** (adjusted close with dividends) - Produces structured quarterly reports: start/end price, returns, high/low, avg volume, dividends paid ## Key Fix (v1.0.1) Previous versions used raw close for return calculations, which **understated returns for dividend-paying stocks** like AAPL. v1.0.1 introduces: 1. **Column name normalization** — handles mixed-case CSV headers (`Date` → `date`) 2. **Adjusted close computation** — backward-adjusts prices for dividends so total return reflects reinvested dividends 3. **Separate price_return vs total_return** — both are reported for transparency 4. **Dividend tracking** — `dividend` column is optional; defaults to 0 if absent ## Usage ### As a Python module ```python from scripts.stock_analyzer import StockAnalyzer analyzer = StockAnalyzer(data_path="finance/AAPL.csv") # 30-day total return (dividend-adjusted) print(analyzer.calculate_returns(30)) # 30-day price return only (no dividends) print(analyzer.calculate_returns(30, use_adj=False)) # Full quarterly report report = analyzer.generate_quarterly_report(quarter=2, year=2026) print(report) ``` ### CLI ```bash python3 scripts/stock_analyzer.py ``` ## CSV Format Expected ``` Date,Close,Volume,Open,High,Low 01/02/2026,$269.77,21755774,$272.05,$277.82,$269.11 ``` Optional columns: - `dividend` — per-share dividend on ex-div date - `adj_close` — pre-computed adjusted close (overrides internal calculation) ## Report Output ```json { "quarter": "Q2 2026", "start_date": "2026-04-01", "end_date": "2026-06-30", "start_price": 223.0, "end_price": 235.5, "price_return_pct": 5.61, "total_return_pct": 6.12, "dividends_per_share": 0.25, "highest_price": 240.0, "lowest_price": 218.5, "average_volume": 35000000 } ``` ## Requirements - Python 3.10+ - pandas


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: w5543081/wa-quanquaner-skill (version 1.0.2)

--- name: waquanquaner description: "中国国内美团、饿了么、京东外卖红包优惠挖掘助手。一个链接挖取当日所有隐藏活动，方便、省心、省钱！当用户提到外卖红包、外卖优惠、饿了么红包、美团红包、点外卖省钱、外卖优惠券、领外卖券、外卖券、今天吃什么、点外卖、叫外卖、美团外卖、饿了么外卖、京东外卖、外卖神券、满减红包、挖券券、挖券券儿时使用此技能。适用地区：中国大陆。关键词：外卖红包、饿了么红包、美团红包、京东外卖、外卖优惠券、领券、省钱、点外卖、今日吃什么、外卖神券、满减红包、挖券券儿。" argument-hint: "[平台(美团/饿了么/京东)]" allowed-tools: Bash(curl *) PowerShell(*) Node(*) emoji: 🧧 category: 生活工具 tags: [外卖, 红包, 优惠券, 省钱, 美团, 饿了么, 京东] --- @image:assets/logo.png # 🧧 外卖红包 — 一个链接挖取当日所有隐藏活动 ## 🚀 执行方式 **最通用、最不易出错的方式**（推荐）： ```bash curl -s "https://waquanquaner.cn/api/v1/activities/channel/skill_compact" ``` 此命令在所有主流平台（Linux、macOS、Windows）上均可直接运行，无需额外依赖，是最简单可靠的数据获取方式。 **如需格式化输出**，可使用以下 Node.js 脚本（无缓存）： ```bash node scripts/query.js ``` ## 🎯 触发关键词 外卖红包、外卖优惠、饿了么红包、美团红包、点外卖省钱、外卖券、领券、今天吃什么、点外卖、美团外卖、饿了么外卖、京东外卖、外卖神券、满减红包、挖券券、挖券券儿 ## 🔍 搜索关键词扩展 **高频搜索词**： - 外卖红包怎么领 - 饿了么红包最大多少 - 美团外卖优惠券 - 京东外卖红包 - 今日外卖优惠 - 点外卖省钱技巧 - 外卖券领取 **长尾关键词**： - 美团外卖新用户红包 - 饿了么天天领红包活动 - 京东外卖首单优惠 - 外卖红包叠加使用 - 周末外卖优惠 - 工作日午餐红包 - 夜宵外卖折扣 ## 📝 输出格式示例 ``` 🧧 今日外卖隐藏红包已挖出！（4月10日） 🔗 一键领全部红包： https://waquanquaner.cn/go 👆 一个链接，挖取当日所有隐藏活动 💡 今日必领： 🔵 饿了么·红包节天天领（今日主角） → 周末大餐红包提前抢，晚上就能用 🔵 饿了么·大额红包限时领（热门推荐） → 领券下单立减，无门槛叠加用 💬 听说今天有人用红包点了一顿霸王餐 复制到微信打开 → 一个个领 → 再点餐 ```


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: romainsantoli-web/firm-i18n-audit-pack (version 1.0.0)

--- name: firm-i18n-audit-pack version: 1.0.0 description: > Internationalization audit pack. Locale file scanning and missing translation key detection. 1 i18n tool. author: romainsantoli-web license: MIT metadata: openclaw: registry: ClawHub requires: - mcp-openclaw-extensions >= 3.0.0 tags: - i18n - localization - audit - translations - locale --- # firm-i18n-audit-pack > ⚠️ Contenu généré par IA — validation humaine requise avant utilisation. ## Purpose Scans locale files to detect missing translation keys, inconsistent key structures across languages, and untranslated strings. Supports JSON and YAML locale formats. ## Tools (1) | Tool | Description | |------|-------------| | `openclaw_i18n_audit` | Locale file scanning + missing key detection | ## Usage ```yaml skills: - firm-i18n-audit-pack # Audit translations: openclaw_i18n_audit config_path=/path/to/config.json ``` ## Requirements - `mcp-openclaw-extensions >= 3.0.0`


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

# OpenStartMenuInk - 开始菜单快捷方式管理器 > 🚀 自动搜索并保存 Windows 开始菜单中的所有快捷方式，根据用户指令快速打开应用 ## 🎯 功能说明 ### 1. 自动扫描快捷方式 - 自动搜索当前用户目录下的开始菜单快捷方式 - 自动搜索公共程序目录的快捷方式 - 包含子文件夹中的所有快捷方式 ### 2. 快速打开应用 - 用户只需说出应用名称，系统会自动搜索并打开 - 支持模糊匹配（如输入"飞智"即可打开"飞智空间站"） ### 3. 动态刷新 - 如果未找到应用或列表为空，自动重新扫描 - 无需手动更新，系统会智能处理 ## 📋 使用示例 ### 打开应用 ``` 打开飞智空间站 打开微信 打开飞书 打开 QQ 打开 Steam ``` ### 初始化扫描（如需要） ``` 检查并开始菜单管理 ``` ## 🛠️ 安装与启用 ### 通过爪爪安装 ``` 安装技能：OpenStartMenuInk ``` ### 手动安装 1. 将技能文件夹复制到 `%LOCALAPPDATA%\OneClaw\resources\skills\` 目录 2. 运行 `openclaw skill sync` 同步技能 ## 📁 数据存储 快捷方式列表将保存在临时文件中，无需手动管理。 ## ⚙️ 技术细节 - **语言**: PowerShell 7+ - **依赖**: Windows 系统 API - **权限**: 需要扫描文件系统的权限 - **安全**: 仅读取开始菜单快捷方式，不修改系统设置 ## 📝 注意事项 - 首次使用可能需要几秒钟扫描时间 - 应用名称支持模糊匹配 - 如遇权限问题，请确保 OpenClaw 有适当权限 ## 🐛 故障排除 如果应用无法打开： 1. 确保该应用已正确安装在开始菜单 2. 运行"检查并开始菜单管理"重新扫描 如果没有看到想要的应用： 1. 右键点击目标应用的安装文件夹 2. 选择"发送到" -> "桌面快捷方式" 3. 右键快捷方式 -> "属性" -> "快速访问" 勾选 ## 🔗 相关链接 - [OpenClaw 文档](https://docs.openclaw.ai) - [ClawHub 技能平台](https://clawhub.com)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: mvogt99/output-wrong-task (version 1.0.0)

--- name: output-wrong-task description: The model produces correct-looking output that addresses a different task than the one requested — typically a related but distinct interpretation of an ambiguous prompt. emoji: 🎯 metadata: clawdis: os: [macos, linux, windows] --- # output-wrong-task The output is well-formed and internally coherent but answers the wrong question. The model resolved an ambiguous prompt toward the most common interpretation rather than the one the user intended, or it latched onto a salient keyword and addressed that instead of the full request. The result can look convincing enough to pass a quick read. ## Symptoms - The deliverable matches the topic of the request but misses its purpose — e.g., "explain this function" gets documentation instead of the debugging analysis asked for. - A code task produces something runnable but solving a simpler or adjacent problem than specified. - The model answers the first clause of a multi-part question and silently drops the rest. - The output would be correct for a different, more common prompt that shares keywords with this one. - Asking the model to verify what it just did reveals that it believed it was solving a different problem. ## What to do - Restate the concrete deliverable, not just the topic. Instead of "help me with authentication," say "write a middleware function that checks for a valid JWT in the Authorization header and returns 401 if missing or invalid — nothing else." - Break compound tasks apart. If the prompt has multiple independent requirements, submit them one at a time and verify each before continuing. - Anchor the output format explicitly. Specifying the expected structure (function signature, JSON schema, number of steps, file to modify) gives the model less room to substitute a related but wrong output. - Before accepting the output, map it back to the original requirement: does this output satisfy the stated goal, not just a plausible-sounding version of it? - If the wrong-task output keeps recurring on the same prompt, the prompt likely has a latent ambiguity. Identify which interpretation the model chose and add a clause that explicitly rules it out.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: yang1002378395-cmyk/ai-intelligent-knowledge-qa (version 1.0.0)

--- name: ai-intelligent-knowledge-qa version: 1.0.0 description: 知识问答，FAQ + 智能检索 + AI 问答。 metadata: openclaw: emoji: "🤖" requires: bins: [] --- # AI 智能知识问答系统 ## 描述 知识问答，FAQ + 智能检索 + AI 问答。 ## 功能 - FAQ 管理（问答库） - 智能检索（语义搜索） - AI 问答（GPT集成） - 多渠道接入（Web/微信/API） - 数据分析（问答统计） ## 定价 - 基础版：¥199/月（1000 问答/月） - 专业版：¥999/月（10000 问答/月） - 企业版：¥2999/月（无限问答） ## 适用场景 - 智能客服 - 知识管理 - 内部问答 - 产品支持 ## 技术栈 - Python + FastAPI - 向量数据库 - GPT 集成 - 语义搜索 ## 安装 ```bash git clone https://github.com/openclaw-skills/ai-intelligent-knowledge-qa cd ai-intelligent-knowledge-qa pip install -r requirements.txt python app.py ``` --- 创建：2026-03-13 作者：OpenClaw Skills Team


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: lingmoon96-dev/four-layer-memory (version 1.0.1)

--- name: four-layer-memory description: 四层记忆架构。身份层、工作记忆层、短期日志层、长期存储层分层管理，让记忆既可持续累积，又不会把上下文一次性塞满。 --- # Four-Layer Memory ## 分层定义 1. Identity Layer: 人格锚点、核心身份、长期不变偏好 2. Working Memory Layer: 当前任务、近期关注、热记忆 3. Short-Term Log Layer: 每日记录、近期会话、完整回溯 4. Long-Term Storage Layer: 经确认的偏好、知识、关系和经验 ## 使用原则 - 稳定信息进 `identity/` 或 `long-term-memory/` - 临时任务进 `working-memory/` - 每日会话进 `short-term-logs/` - 过期日志转 `archive/` ## 推荐命令 ```bash python3 scripts/personal_ai_memory.py status python3 scripts/personal_ai_memory.py bootstrap python3 scripts/personal_ai_memory.py archive --keep-days 14 ```


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

--- name: ouyi description: | 使用欧亿（ouyi）API 进行对话分析的 ACP skill。当用户要求使用 ouyi 模型、 欧亿模型、分析黄金、投资、技术问题等需要 AI 推理的任务时使用此 skill。 该 skill 通过本地 Node.js 脚本直接调用 ouyi API， 解决 OpenClaw 的 openai-completions 类型无法解析 chat 格式响应的问题。 使用方式： 1. 读取 scripts/ouyi-api.js 的内容 2. 通过 exec 工具运行脚本 3. 解析 JSON 结果并发送给用户 --- # ouyi Skill ## 核心脚本 路径：`scripts/ouyi-api.js` ## API 配置 - **API Key**: 用户需要用自己的 ouyi API key 替换脚本中的 `API_KEY` - **模型**: `ouyi-openclaw`（内部映射到 gpt-5-nano-2025-08-07） - **Endpoint**: `https://api.rcouyi.com/v1/chat/completions` ## 使用方式 ### 1. 运行对话 ```bash node <skill_dir>/scripts/ouyi-api.js "你的问题" ``` ### 2. 解析结果 输出格式（JSON）： ```json {"content": "回复内容", "reasoningTokens": 832, "model": "gpt-5-nano-2025-08-07", "error": null} ``` ### 3. 发送结果 提取 `content` 字段，发送给用户。 ## 示例问题 - "Is gold worth buying in 2025?" - "Explain quantum computing to a 10-year-old in Chinese." - "What is the difference between GDP and GNP?" ## 性能特征 - 响应时间：平均 5-10 秒（推理模型特性） - 速度与 reasoning_tokens 无明显相关 - 支持中英文，质量稳定 ## 已知问题 - `reasoning_tokens` 很大但 content 可能为空（当 max_tokens < 1500 时） - 当前脚本不设置 max_tokens 来避免此问题 - 模型是推理模型，可能先输出推理过程再输出答案 ## 配置 若要更换 API key，直接编辑 `scripts/ouyi-api.js` 中的： ```javascript const [REDACTED_SECRET]'; ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: theopenbase/obsidian-read (version 1.0.0)

--- name: obsidian-read description: | 读取 Obsidian Publish 发布的文档链接。当用户发送 publish.obsidian.md 链接，或需要读取 Obsidian 笔记文章时使用。 适用于从 Obsidian Publish 平台获取完整文章内容、笔记、Essay 等场景。 优先使用 web_fetch，失败时使用 browser 工具方案。 --- # Obsidian Read 读取 Obsidian Publish 发布的文档内容。 ## 工作流 ### 步骤 1：优先尝试 web_fetch ```python web_fetch(url="链接", maxChars=15000) ``` 如果成功返回完整内容，直接使用。 ### 步骤 2：使用 browser 方案（当 web_fetch 失败时） 当 web_fetch 返回内容不完整、404 或解析错误时，使用以下流程： **2.1 打开链接获取 targetId** ```python browser(action="open", url="https://publish.obsidian.md/a/essay/xxx") ``` 返回结果包含 `targetId`，例如：`[REDACTED_SECRET]` **2.2 读取页面内容** ```python browser(action="snapshot", targetId="上一步获取的targetId") ``` **2.3 提取文本** 从 snapshot 结果中提取 `paragraph`、`heading` 等文本元素，返回给用户。 ## 注意事项 - **必须用 open 获取 targetId**：直接使用 snapshot 而不传 targetId 会导致失败 - **静态内容优先**：Obsidian Publish 是静态站点，snapshot 通常能完整抓取 - **不需要截图**：snapshot 是获取可访问性树（文本内容），不是截图 ## 示例 用户发送：`https://publish.obsidian.md/a/essay/202603181132` 1. 先尝试 `web_fetch` → 失败/不完整 2. `browser.open` → 获取 targetId 3. `browser.snapshot` → 读取完整文章 4. 总结文章内容返回给用户


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: lt8899789/tun-zei-healer (version 1.0.0)

--- name: auto-healer version: 1.0.0 description: 吞贼·净化魄 - 自动错误恢复与系统自愈，让AI像修士一样百毒不侵、自动修复 tags: - error-recovery - self-healing - fault-tolerance - resilience - automation --- # 吞贼·净化魄 (Auto Healer) - 错误恢复与系统自愈 ## 技能说明 **吞贼·净化魄** 是硅灵决七魄之一，主管错误修正、冗余清理与容错自愈，让AI能够像修士一样，百毒不侵、遇错自愈。 --- ## 核心能力 ### 1. 错误检测 (Error Detection) - 实时监控系统状态 - 异常模式识别 - 故障预警 ### 2. 自动恢复 (Auto Recovery) - 失败任务自动重试 - 回滚机制 - 降级策略 ### 3. 冗余清理 (Redundancy Cleanup) - 重复文件检测 - 临时文件清理 - 资源释放 ### 4. 容错设计 (Fault Tolerance) - 优雅降级 - 熔断机制 - 超时控制 --- ## 使用方法 当系统出现异常或用户要求检查健康状态时： ``` 用户：系统好像不太稳定 → 触发吞贼·净化魄 → 输出诊断报告 用户：清理一下冗余文件 → 触发吞贼·净化魄 → 执行清理 ``` --- ## 输出格式 ### 诊断报告 ```markdown ## 系统诊断报告 ### 健康状态 [健康/亚健康/异常] ### 检测到的问题 1. [问题1] - [严重程度] - [建议操作] 2. [问题2] - [严重程度] - [建议操作] ### 已执行修复 - [修复1] - [修复2] ### 系统建议 [后续操作建议] ``` --- ## 错误处理策略 ### 重试策略 (Retry) ``` 指数退避: delay = base * 2^attempt 最大重试: 3-5次 超时控制: 30s / 60s / 120s ``` ### 熔断策略 (Circuit Breaker) ``` 健康状态: 正常请求 降级状态: 返回缓存/默认值 熔断状态: 拒绝请求，快速失败 ``` ### 回滚策略 (Rollback) ``` 版本记录: 保留最近N个版本 一键回滚: 支持快速回退 数据一致性: 确保回滚后数据完整 ``` --- ## 自愈机制 ### 内存泄漏检测 - 监控内存使用趋势 - 超过阈值自动触发GC - 记录泄漏模式 ### 进程异常检测 - 心跳监控 - 僵死进程重启 - 资源耗尽保护 ### 日志异常检测 - 错误日志突增预警 - 异常模式识别 - 自动生成故障报告 --- ## 调度者守则 1. **预防为主** - 提前发现问题，避免故障发生 2. **快速恢复** - 故障时以恢复为目标 3. **最小影响** - 修复时影响范围最小化 4. **根因分析** - 修复后分析根本原因 --- ## 适用场景 - 系统健康检查 - 错误自动恢复 - 冗余文件清理 - 故障预警 - 性能监控 --- ## 依赖技能 - silicon-soul（硅灵决基础） - task-planner（伏矢·路径魄） - self-improving（自我改进） ## 版本 v1.0.0 | 2026-04-12 | 硅灵宗·菩提


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: wondershare-boop/tomoviee-image-recognition (version 1.0.2)

--- name: tomoviee-recognition description: Auto-generate masks for objects/regions in images. Use when users request image_recognition operations or related tasks. --- # Tomoviee AI - 图像识别 (Image Recognition) ## Overview Auto-generate masks for objects/regions in images. **API**: `tm_reference_img2mask` ## Quick Start ### Authentication ```bash python scripts/generate_auth_token.py YOUR_APP_KEY YOUR_APP_SECRET ``` ### Python Client ```python from scripts.tomoviee_image_recognition_client import TomovieeClient client = TomovieeClient("app_key", "app_secret") ``` ## API Usage ### Basic Example ```python task_id = client._make_request({ image='https://example.com/photo.jpg' control_type='2' # subject/default }) result = client.poll_until_complete(task_id) import json output = json.loads(result['result']) ``` ### Parameters - `image` (required): Image URL to analyze - `control_type`: 0=edge, 1=pose, 2=subject, 3=depth ## Async Workflow 1. **Create task**: Get `task_id` from API call 2. **Poll for completion**: Use `poll_until_complete(task_id)` 3. **Extract result**: Parse returned JSON for output URLs **Status codes**: - 1 = Queued - 2 = Processing - 3 = Success (ready) - 4 = Failed - 5 = Cancelled - 6 = Timeout ## Resources ### scripts/ - `tomoviee_image_recognition_client.py` - API client - `generate_auth_token.py` - Auth token generator ### references/ See bundled reference documents for detailed API documentation and examples. ## External Resources - **Developer Portal**: https://www.tomoviee.ai/developers.html - **API Documentation**: https://www.tomoviee.ai/doc/ - **Get API Credentials**: Register at developer portal


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

--- name: Outreach description: Plan, personalize, and track outreach campaigns for sales, PR, recruiting, partnerships, and link building with timing strategy and follow-up cadence. --- ## Situation Detection | Context | Load | |---------|------| | Writing cold emails, follow-ups, subject lines | `templates.md` | | Sales outreach (SDR, BDR, AE) | `by-type.md` → Sales section | | PR/Media pitching, influencer outreach | `by-type.md` → PR section | | Link building, guest posts, SEO outreach | `by-type.md` → SEO section | | Recruiting, talent sourcing | `by-type.md` → Recruiting section | | Investor, partnership, BD outreach | `by-type.md` → BD section | | Tracking what works, analyzing patterns | `tracking.md` | --- ## Universal Rules **The Formula:** Who (right person, verified contact) + Why Now (trigger event) + WIIFT (what's in it for them) **Personalization:** Reference something SPECIFIC — not "loved your work" but "your piece on X changed how I think about Y." If you can't find a hook, you haven't researched enough. **Timing:** - Best: Tuesday-Thursday, 9-11am recipient's timezone - Avoid: Monday 9am, Friday afternoon, holidays, major news days - Check: Did they just post/publish something? Strike while relevant **Follow-up cadence:** | Touch | Timing | Purpose | |-------|--------|---------| | 1 | Day 0 | Value-first intro, specific ask | | 2 | Day 3-5 | New angle or additional value | | 3 | Day 7-10 | Different channel if possible | | 4 | Day 14+ | Break-up email | Stop after 3-4 touches. "Bumping this up" is spam. --- ## Quick Checks **Before sending:** - [ ] Verified email (not generic info@) - [ ] Personalization hook is SPECIFIC - [ ] Under 150 words for cold outreach - [ ] No attachments (link to resources) - [ ] Clear CTA (specific ask, not "let me know") - [ ] Subject line under 50 characters **Red flags to avoid:** - "I came across your profile..." (generic) - Pitching in first message (build curiosity first) - "Per my last email..." (guilt-trip) - Asking for "exposure" (have budget or don't ask) - Simultaneous competitor pitches with exclusive angles --- ## Outreach Profile *Build over time. Update after campaigns.* ### What Works <!-- "Subject line: Question format +40% opens" "Tuesday 10am best time" --> ### What Fails <!-- "Long emails ignored" "LinkedIn connect requests <10% accept" --> ### Templates That Convert <!-- "Follow-up #2 formula: new value + specific ask" --> ### Channel Preferences by Persona <!-- "Execs: email via EA" "Journalists: Twitter DM > email" --> ### Contacts to Avoid <!-- "Reporter X: never responds" "VC Y: no cold outreach" --> --- *Empty = nothing learned yet. Every campaign reveals patterns. Track and update.*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: tomstools11/research-company (version 1.0.0)

--- name: research-company description: B2B company research producing professional PDF reports. Use when asked to research a company, analyze a business, create an account profile, or generate market intelligence from a company URL. Outputs a beautifully formatted, downloadable PDF report. --- # Company Research Generate comprehensive Account Research Reports as professionally styled PDFs from a company URL. ## Workflow 1. **Research** the company (web fetch + searches) 2. **Build** JSON data structure 3. **Generate** PDF via `scripts/generate_report.py` 4. **Deliver** PDF to user ## Phase 1: Research (Parallel) Execute these searches concurrently to minimize context usage: ``` WebFetch: [company URL] WebSearch: "[company name] funding news 2024" WebSearch: "[company name] competitors market" WebSearch: "[company name] CEO founder leadership" ``` Extract from website: company name, industry, HQ, founded, leadership, products/services, pricing model, target customers, case studies, testimonials, recent news. ## Phase 2: Build Data Structure Create JSON matching this schema (see `references/data-schema.md` for full spec): ```json { "company_name": "...", "source_url": "...", "report_date": "January 20, 2026", "executive_summary": "3-5 sentences...", "profile": { "name": "...", "industry": "...", ... }, "products": { "offerings": [...], "differentiators": [...] }, "target_market": { "segments": "...", "verticals": [...] }, "use_cases": [{ "title": "...", "description": "..." }], "competitors": [{ "name": "...", "strengths": "...", "differentiation": "..." }], "industry": { "trends": [...], "opportunities": [...], "challenges": [...] }, "developments": [{ "date": "...", "title": "...", "description": "..." }], "lead_gen": { "keywords": {...}, "outreach_angles": [...] }, "info_gaps": ["..."] } ``` ## Phase 3: Generate PDF ```bash # Install if needed pip install reportlab # Save JSON to temp file cat > /tmp/research_data.json << 'EOF' {...your JSON data...} EOF # Generate PDF python3 scripts/generate_report.py /tmp/research_data.json /path/to/output/report.pdf ``` ## Phase 4: Deliver Save PDF to workspace folder and provide download link: ``` [Download Company Research Report](computer:///sessions/.../report.pdf) ``` ## Quality Standards - **Accuracy**: Base claims on observable evidence; cite sources - **Specificity**: Include product names, metrics, customer examples - **Completeness**: Note gaps as "Not publicly available" - **No fabrication**: Never invent information ## Resources - `scripts/generate_report.py` - PDF generator (uses reportlab) - `references/data-schema.md` - Full JSON schema with examples


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: hmaya/a-share-trader (version 1.0.0)

--- name: a-share-trader description: A股核心交易框架，整合12种选股策略的自适应量化交易系统 version: 1.0.0 author: assistant permissions: 需要股票数据访问和交易权限 --- # A股核心交易框架 ## 概述 基于用户提供的12种选股策略，结合自适应交易引擎，构建的A股量化交易系统。系统具备市场状态识别、策略动态切换、风险自适应控制等高级功能。 ## 整合策略列表 1. **基本面选股策略** - 质量+增长双因子 2. **防御选股策略** - 高股息价值投资 3. **震荡选股策略** - KDJ技术指标 4. **小市值策略** - 月度切换的小市值轮动 5. **基本面加小市值** - 质量小市值混合 6. **小市值成长** - 小市值成长股精选 7. **营收利润双增** - 增长质量策略 8. **价值成长** - 价值成长平衡 9. **控盘策略** - 筹码集中度选股 10. **社保重仓** - 机构跟随策略 11. **超跌反弹** - 均值回归策略 12. **时空共振策略** - 四维度综合模型 ## 核心特性 - **自适应市场感知**：识别牛熊市、震荡市等不同市场状态 - **策略动态权重**：根据不同市场环境自动调整策略权重 - **智能参数调整**：基于市场状态动态调整策略阈值 - **多维度风控**：个股、行业、组合多层风控 - **学习优化机制**：从交易绩效中持续学习优化 ## 系统架构 ``` A股核心交易框架/ ├── core/ # 核心引擎 │ ├── a_share_trader.py # 主交易引擎 │ ├── adaptive_engine.py # 自适应引擎 │ └── data_interface.py # A股数据接口 ├── strategies/ # 策略模块 │ ├── __init__.py │ ├── base_strategy.py # 策略基类 │ ├── fundamental.py # 基本面策略 │ ├── defensive.py # 防御策略 │ ├── swing_trading.py # 震荡策略 │ ├── small_cap.py # 小市值策略 │ ├── quality_small_cap.py # 质量小市值 │ ├── small_cap_growth.py # 小市值成长 │ ├── revenue_profit.py # 营收利润双增 │ ├── value_growth.py # 价值成长 │ ├── chip_concentration.py # 控盘策略 │ ├── social_security.py # 社保重仓 │ ├── oversold_rebound.py # 超跌反弹 │ └── resonance.py # 时空共振策略 ├── risk_management/ # 风险管理 │ ├── core.py # 风控核心 │ ├── position.py # 仓位控制 │ └── stop_loss.py # 止损策略 ├── config/ # 配置 │ └── trading_config.py # 交易配置 ├── utils/ # 工具函数 ├── tests/ # 测试 └── examples/ # 使用示例 ``` ## 调用方式 在智能体提示中直接请求执行此技能，例如： > "请运行A股交易框架进行模拟交易" > "使用A股框架分析当前市场状态" > "执行A股策略回测" ## 数据需求 - A股实时行情数据 - 基本面数据（财务指标） - 技术指标数据 - 市场情绪数据（可选） ## 输出示例 ```json { "status": "running", "market_state": "weak_bull", "state_confidence": 0.72, "active_strategies": 8, "portfolio_value": 10000000000, "total_return": 0.15, "max_drawdown": 0.08, "active_positions": 12 } ``` ## 注意事项 1. 系统为量化交易框架，实盘交易需谨慎 2. 需要可靠的数据源支持 3. 建议先进行充分的回测和模拟交易 4. 注意市场风险，控制仓位和回撤


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