OpenClaw/clawhub-security-signals huggingface dataset:
Skill: jcdreamjc/wudao-ashare (version 1.0.1)

--- name: wudao-ashare version: "1.0.0" description: "A股全能数据套件，26个实时API接口：K线分时、涨停板梯队、资金流向、龙虎榜、竞价数据、板块轮动、研报热榜等。包含4个子技能包，一个Key通用。" metadata: { "openclaw": { "emoji": "📊", "requires": { "env": ["LB_API_KEY", "LB_API_BASE"], }, }, } --- # 悟道 · A股全能数据套件 A 股全能数据技能包，覆盖行情、涨停板、资金分析、市场情报四大领域，共提供 **26 个数据接口**。 本 Skill 是总索引，实际数据能力由 4 个子技能包提供，请根据需要安装对应的子技能。 ## 4 个子技能包 | 技能包 | 接口数 | 覆盖领域 | 安装 | |--------|--------|----------|------| | 📈 **wudao-market** · A股行情数据 | 6 | 股票搜索、K线、分时、排行榜、市场概况、交易日历 | [ClawHub](https://clawhub.ai/skills/wudao-market) \| [GitHub](https://github.com/jcdreamjc/wudao-ashare/tree/master/wudao-market) | | 🔥 **wudao-limitup** · A股涨停板 | 9 | 涨停梯队、涨停筛选、涨停溢价、炸板池、跌停池、冲刺涨停、涨跌停统计、最强风口、封板事件流 | [ClawHub](https://clawhub.ai/skills/wudao-limitup) \| [GitHub](https://github.com/jcdreamjc/wudao-ashare/tree/master/wudao-limitup) | | 🔍 **wudao-analysis** · A股资金分析 | 6 | 异动检测、资金流向、板块轮动、股票关联、概念排行、概念成分股 | [ClawHub](https://clawhub.ai/skills/wudao-analysis) \| [GitHub](https://github.com/jcdreamjc/wudao-ashare/tree/master/wudao-analysis) | | 📰 **wudao-intel** · A股市场情报 | 5 | 智能热榜、研报数据、竞价数据、每日简报、龙虎榜 | [ClawHub](https://clawhub.ai/skills/wudao-intel) \| [GitHub](https://github.com/jcdreamjc/wudao-ashare/tree/master/wudao-intel) | ## Setup ### 获取 API Key 1. 访问 [https://stock.quicktiny.cn](https://stock.quicktiny.cn) 注册账号并登录 2. 进入「开发者 API」页面（路径：`/developer`） 3. 点击「创建密钥」，系统会生成一个以 `lb_` 开头的 API Key 4. **立即复制保存**，密钥仅显示一次 ### 配置环境变量 将以下两个环境变量添加到你的 shell 配置文件（如 `~/.zshrc` 或 `~/.bashrc`）： - `LB_API_KEY` — 你的 API 密钥（以 `lb_` 开头） - `LB_API_BASE` — API 基础地址：`https://stock.quicktiny.cn/api/openclaw` 4 个子技能共享同一个 API Key 和 Base URL。 ## Notes - 日期格式：同时支持 `YYYY-MM-DD` 和 `YYYYMMDD` - 股票代码：6 位数字（`600519`）或完整代码（`600519.SH`） - A 股交易时间：9:15-15:00（北京时间） - 每个子技能的完整接口文档请参见对应的 Skill 文件 - 版本信息和更新日志请访问 [GitHub 仓库](https://github.com/jcdreamjc/wudao-ashare)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: lanxy32/tantan-mk (version 1.0.0)

--- name: tantan-intelligence description: 激活"探探"市场情报官角色。当用户需要了解行业趋势、竞品分析、市场规模、竞争格局、目标客户画像、新赛道机会评估、市场进入可行性、竞争对手动态等情报类需求时，必须调用此skill。关键词包括：市场分析、竞品、竞争对手、行业趋势、市场规模、客户画像、赛道、机会、市场调研、行业报告。只要用户想"了解外部市场情况"，立即触发本skill。 --- # 探探 · 市场情报官 Skill ## 角色定义 激活"探探"模式：顶尖市场情报专家，数据驱动，帮老板掌握行业动向、竞争格局、市场机会，让每个商业决策都有情报支撑。 ## 情报需求分类与处理路径 ### 类型A：行业趋势分析 **分析维度：** 1. 市场规模（当前值 + 增速 + 5年预测） 2. 驱动因素（技术/政策/消费/供给侧） 3. 行业生命周期（导入/成长/成熟/衰退） 4. 关键成功因素（行业里活得好的共同点） ### 类型B：竞品深度拆解 **拆解维度（7维竞品雷达）：** | 维度 | 分析要点 | |------|---------| | 产品 | 功能/品质/差异化亮点 | | 定价 | 价格区间/定价逻辑/利润空间 | | 渠道 | 销售渠道/覆盖范围/渠道效率 | | 营销 | 传播策略/内容风格/获客方式 | | 用户 | 目标用户/口碑评价/用户黏性 | | 团队 | 核心团队背景/融资情况 | | 战略 | 扩张方向/近期动作解读 | ### 类型C：客户画像构建 **画像框架：** - **人口属性**：年龄/性别/地域/收入/职业 - **行为特征**：购买频次/决策方式/信息获取渠道 - **痛点需求**：功能性需求 / 情感性需求 / 社会性需求 - **购买动机**：为什么买 / 为什么买你 / 为什么不买 ### 类型D：市场机会评估 **机会评估矩阵：** ``` 市场吸引力（高/低）× 己方匹配度（高/低） ↗ 高吸引力 + 高匹配 = 优先进入 ↗ 高吸引力 + 低匹配 = 能力建设后进入 ↘ 低吸引力 + 高匹配 = 谨慎评估 ↘ 低吸引力 + 低匹配 = 放弃 ``` ## 情报简报输出模板 ``` 【市场情报简报 · 探探出品】 ▌情报摘要（30秒读懂版） [2-3句话说清楚最重要的结论] ▌市场概况 规模：xxx亿 | 增速：xx% | 趋势：上升/平稳/下降 关键驱动：... ▌竞争格局 格局类型：分散/集中/寡头 主要玩家： - 头部：[公司名] 份额xx%，优势：... - 腰部：[公司名] 特点：... - 尾部机会：... ▌竞品解析（可制表） | 对手 | 产品 | 定价 | 渠道 | 核心优势 | 致命弱点 | |------|------|------|------|---------|---------| ▌市场机会 🎯 机会点1：[描述] | 市场空白原因：... 🎯 机会点2：[描述] | 切入方式建议：... ▌风险预警 ⚠️ 风险1：... ⚠️ 风险2：... ▌行动建议 基于以上情报，建议：... 情报收集时间：[说明数据局限性] ``` ## 数据获取优先级 1. **第一手数据**：用户/客户访谈洞察 2. **权威报告**：行业协会、咨询公司报告 3. **公开数据**：上市公司财报、招股书、媒体报道 4. **推理估算**：基于已知数据合理推断（需标注为"估算"） ## 沟通规则 - 结论先行，数据支撑 - 区分"事实"与"推断"，推断需标注 - 客观中立，不主观美化或丑化任何竞品 - 主动提示数据局限性和信息来源 ## 开场白 > "老板好，我是探探，您的市场情报官。告诉我您想了解哪个市场、哪家对手，我来为您全面扫描，把市场看个透。"


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: forevershu/market-chart-renderer (version 1.0.0)

--- name: market-chart-renderer summary: 中国市场行情图表渲染 skill，负责指标计算、ECharts 模板、HTML/PNG 导出 --- # market-chart-renderer 用于把标准化 `bars` 数据渲染成图表。 ## 职责边界 负责： - 指标计算：至少 `MA5/10/20/60`、`MACD` - ECharts 图表模板 - 标题区（品种名 / 代码 / 周期） - 主图 K 线 + MA 均线 - 副图默认 `MACD` - HTML / PNG 导出 不负责： - AkShare 数据抓取细节 - 品种路由 - 交易所查询逻辑 ## 最小入口 直接生成甲醇 `MA0 60m` 图： ```bash python3 skills/market-chart-renderer/render_ma0_60m.py ``` 自定义： ```bash python3 skills/market-chart-renderer/render_chart.py --symbol MA0 --period 60 --limit 120 ``` ## 输出位置 默认输出到： - `output/generated/images/*.html` - `output/generated/images/*.png` - `output/generated/images/*.json` ## 依赖关系 - 数据来源：`skills/akshare-futures-options-data/akshare_router_cn.py` - 渲染方案：ECharts + headless Chrome

path: render_ma0_60m.py
content: #!/usr/bin/env python3
from __future__ import annotations

import json
from pathlib import Path
import sys

SKILL_DIR = Path(__file__).resolve().parent
if str(SKILL_DIR) not in sys.path:
    sys.path.insert(0, str(SKILL_DIR))

from render_chart import render_chart  # noqa: E402


def main() -> int:
    result = render_chart(symbol='MA0', period='60', limit=120, output_stem='ma0_60m_market_chart_renderer')
    print(json.dumps(result, ensure_ascii=False, indent=2))
    return 0


if __name__ == '__main__':
    raise SystemExit(main())


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: chloepark85/skill-guard-pro (version 1.0.1)

--- name: clawguard description: "Security scanner for ClawHub skills. Analyze before you install." license: "MIT" metadata: { "openclaw": { "emoji": "🛡️", "requires": { "bins": ["uv"] } } } --- # ClawGuard 🛡️ **Scan ClawHub skills for security risks before installing.** ClawGuard performs static code analysis on ClawHub skills to detect: - 🌐 Network exfiltration (HTTP POST to external URLs) - 🔑 Credential access (API keys, tokens, passwords) - ⚡ Shell command execution - 💥 File destruction (rm -rf, unlink) - 🎭 Code obfuscation (eval, base64 decode) - 👻 Hidden files and directories ## Usage ### Scan by skill name Download and scan a skill from ClawHub: ```bash uv run {baseDir}/scripts/scan.py --skill <skill-name> ``` ### Scan local directory Scan a skill directory on your local filesystem: ```bash uv run {baseDir}/scripts/scan.py --path /path/to/skill ``` ### JSON output Get results in JSON format: ```bash uv run {baseDir}/scripts/scan.py --skill <skill-name> --json ``` ## Examples Scan the GitHub skill: ```bash uv run {baseDir}/scripts/scan.py --skill github ``` Scan a local skill: ```bash uv run {baseDir}/scripts/scan.py --path ~/.openclaw/skills/my-skill ``` ## Risk Levels - 🟢 **SAFE** (0-30): No significant risks detected - 🟡 **CAUTION** (31-60): Review flagged items before installing - 🔴 **DANGEROUS** (61-100): High-risk patterns detected — DO NOT INSTALL ## Exit Codes - `0`: Safe - `1`: Caution - `2`: Dangerous ## Requirements - Python 3.11+ - `uv` (Python package manager) - `clawhub` CLI (optional, for downloading skills) ## How It Works 1. **Pattern Matching**: Regex-based detection of dangerous code patterns 2. **AST Analysis**: Python AST parsing for eval/exec detection 3. **URL Extraction**: Identifies all network endpoints 4. **Risk Scoring**: Weighted severity scoring (0-100) ## What It Detects | Category | Weight | Examples | |----------|--------|---------| | Network exfiltration | 25 | POST to unknown URL with data | | Credential access | 20 | Reading API keys, tokens | | Shell execution | 15 | exec(), subprocess, system() | | File destruction | 15 | rm -rf, unlink, rmdir | | Obfuscation | 15 | eval(), atob(), Buffer.from | | Hidden files | 10 | Dotfiles, hidden directories | ## Limitations - **Static analysis only**: Cannot detect runtime behavior - **Regex-based**: May have false positives/negatives - **JS/TS**: Basic pattern matching (no full AST parsing) - **Encrypted/minified code**: Cannot analyze obfuscated payloads ## Best Practices 1. **Always scan before installing** untrusted skills 2. **Review CAUTION-level findings** manually 3. **Check network endpoints** for unknown domains 4. **Never install DANGEROUS skills** without verification 5. **Report suspicious skills** to ClawHub moderators ## License MIT


Security scan verdicts — ClawScan: clean | static analysis: suspicious | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: 786793119/pocket-money-manager (version 1.0.0)

--- name: pocket-money-manager slug: pocket-money-manager version: 1.0.0 description: 理财小助手，收支记录、消费分析、预算提醒。 homepage: https://github.com/786793119/miya-skills metadata: {"openclaw":{"emoji":"💰","requires":{"bins":["python3"]},"os":["linux","darwin","win32"]}} --- # 理财小助手 (Pocket Money Manager) 你的私人记账管家。 ## 功能 - 记收入（工资/奖金/兼职/理财/红包） - 记支出（餐饮/交通/购物/住房/娱乐/医疗/教育） - 查看余额和收支统计 - 周消费报告 - 设置月预算 - 超支预警提醒 ## 使用示例 ```bash # 记收入 python pocket-money-manager.py add_income 5000 工资 # 记支出 python pocket-money-manager.py add_expense 35 餐饮 # 查看余额 python pocket-money-manager.py get_balance # 周报告 python pocket-money-manager.py get_weekly_report # 设置预算 python pocket-money-manager.py set_budget 3000 ``` ## 数据存储 - 收支记录: `~/.memory/finance/records.json` - 预算设置: `~/.memory/finance/budget.json` --- *By Miya - 2026*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: hy-1990/dream-text-to-video (version 1.0.1)

--- name: dream-text-to-video description: "Generate videos from text using Text to Video (Wan2.1) API. Automatically generates 4-second videos from text descriptions. Powered by Dreamface - AI tools for everyone. Visit https://tools.dreamfaceapp.com/home for more AI products. API resources: https://api.newportai.com/api-reference/text-to-video-wan2-1" metadata: {"openclaw": {"emoji": "✍️", "requires": {"env": ["DREAMTEXTTOVIDEO_API_KEY"]}, "primaryEnv": "DREAMTEXTTOVIDEO_API_KEY"}} --- # Dream Text to Video (Wan2.1) - Text to Video Generate 4-second videos from text prompts. ## Quick Start ### 1. Get API Key Before using this skill, you need a DreamAPI API key. Visit **https://api.newportai.com/api-reference/get-started** to sign up and get your API key. For more AI tools, please visit: **https://tools.dreamfaceapp.com/home** ### 2. Configure API Key ```bash openclaw config patch --json '{"skills": {"entries": {"dream-text-to-video": {"env": {"DREAMTEXTTOVIDEO_API_KEY": "your-api-key-here"}}}}}'``` ### 3. Usage #### Parameters: - **prompt**: Text description, max 1500 characters - **resolution** (optional): "480p" or "720p", default "480p" ## API Details ### Text to Video API ``` POST https://api.newportai.com/api/async/wan/text_to_video/2.1 ``` ### Polling for Result ``` POST https://api.newportai.com/api/getAsyncResult ``` ## Implementation Notes - No image upload needed, text-to-video - Generates 4-second videos - Prompt max 1500 characters - Supports 480p and 720p - Status: 0=pending, 1=processing, 2=processing, 3=completed, 4=timeout


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: tanis90/pdftomd (version 1.0.4)

--- name: pdf2md description: "PDF to Markdown converter - extract text, tables and formulas from PDF files to clean Markdown. Use when converting PDF documents, extracting PDF content, parsing PDF text, or summarizing PDF reports." homepage: https://mineru.net metadata: {"openclaw":{"emoji":"📄","requires":{"bins":["mineru-open-api"]},"install":[{"id":"npm","kind":"node","package":"mineru-open-api","bins":["mineru-open-api"],"label":"Install via npm"},{"id":"uv","kind":"uv","package":"mineru-open-api","bins":["mineru-open-api"],"label":"Install via uv"},{"id":"go","kind":"go","package":"github.com/opendatalab/MinerU-Ecosystem/cli/mineru-open-api","bins":["mineru-open-api"],"label":"Install via go install","os":["darwin","linux"]}]}} --- # PDF to Markdown - Extract Text, Tables, Formulas from PDF Convert PDF files to clean Markdown using MinerU Open API. No API key required. ## Quick Start ```bash # Convert a local PDF to Markdown mineru-open-api flash-extract report.pdf # Convert a PDF from URL (no download needed) mineru-open-api flash-extract https://cdn-mineru.openxlab.org.cn/demo/example.pdf # Save to file mineru-open-api flash-extract report.pdf -o ./output/ # Convert specific pages mineru-open-api flash-extract report.pdf --pages 1-10 ``` ## Language Rule You MUST reply to the user in the SAME language they use. This is non-negotiable. ## Capabilities - Extracts text, tables, and formulas from PDF - Supports both local files and URLs directly - Page range selection with `--pages` - Language hint with `--language` (default: `ch`, use `en` for English) - No API key, no signup, no authentication - Max 10MB / 20 pages per document ## When to Use - User asks to "read", "extract", "convert", or "parse" a PDF - User shares a PDF file or PDF link and asks for its content - User wants to summarize or analyze a PDF document - User needs PDF content in Markdown format ## CLI Reference Run `mineru-open-api flash-extract --help` for all available options. ## Data Flow `flash-extract` sends the document to the MinerU API (mineru.net) for processing and returns Markdown. This is a stateless API call — no account, no persistent storage. MinerU is an open-source project by OpenDataLab (Shanghai AI Lab): https://github.com/opendatalab/MinerU ## Notes - Output is Markdown only; images/tables/formulas may be replaced with placeholders - For larger files (up to 200MB/600 pages) or precision extraction with full assets, use `mineru-open-api extract` (requires auth via `mineru-open-api auth`) - If the CLI cannot be installed via npm/uv/go, download it from https://mineru.net/ecosystem?tab=cli


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: nexecuteinc/cmdbcompass (version 1.0.6)

--- name: cmdbcompass description: The first CMDB governance skill for ServiceNow. Audit, remediate, and govern your CMDB from any AI agent. Health scoring, duplicate detection, relationship analysis, stale CI cleanup, and governed remediation with full rollback and audit trail on every write. metadata: openclaw: requires: bins: [python3, pip] env: [SERVICENOW_INSTANCE_URL, SERVICENOW_USERNAME, SERVICENOW_PASSWORD] install: - id: mcp-install kind: exec command: bash scripts/install-mcp.sh label: Install CMDB Compass MCP server --- # CMDB Compass > The first CMDB governance skill for ServiceNow. Audit, remediate, and govern your ServiceNow CMDB from any AI agent. Health scoring, duplicate detection, relationship analysis, stale CI cleanup, and governed remediation with full rollback and audit trail on every write. ## Install ```bash clawhub install cmdbcompass ``` Or manually: ```bash git clone https://github.com/cmdbcompass/cmdbcompass cd cmdbcompass bash scripts/install-mcp.sh ``` ## Configure Add to your MCP client config: ```json { "mcpServers": { "cmdb-compass": { "command": "python", "args": ["-m", "servicenow_mcp.server"], "env": { "SERVICENOW_INSTANCE_URL": "https://your-instance.service-now.com", "SERVICENOW_USERNAME": "your_username", "SERVICENOW_PASSWORD": "your_password" } } } } ``` Works with Claude Desktop, Cursor, VS Code, or any MCP-compatible client. ## Capabilities Audit tools cover health scoring, duplicate detection, relationship integrity, discovery gaps, stale CI identification, and CSDM 5.0 compliance. All audit operations are read-only and unlimited. Write operations including merging duplicates, retiring stale CIs, bulk field updates, and relationship healing create an immutable audit trail and can be fully rolled back by session. ## Requirements Python 3.10+ and a ServiceNow instance with REST API access. ## License MIT. See LICENSE for details. ## Contact [[REDACTED_SECRET]](mailto:[REDACTED_SECRET]) · [Issues](https://github.com/cmdbcompass/cmdbcompass/issues)


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: harleyscodes/hedera-tx-builder (version 1.0.0)

--- name: hedera-tx-builder description: Build and sign Hedera transactions. Use for: (1) Creating HBAR transfers, (2) Token operations, (3) Smart contract calls, (4) Submitting to Hedera network. --- # Hedera Transaction Builder ## Setup ```bash npm install @hashgraph/sdk ``` ## Client Setup ```typescript import { Client, AccountBalanceQuery, Hbar } from '@hashgraph/sdk'; const client = Client.forMainnet(); // Or for testnet: const client = Client.forTestnet(); ``` ## Transfer HBAR ```typescript import { TransferTransaction, Hbar } from '@hashgraph/sdk'; const tx = new TransferTransaction() .addHbarTransfer(fromAccountId, new Hbar(-100)) // send .addHbarTransfer(toAccountId, new Hbar(100)) // receive .setTransactionMemo("Payment for goods"); // Sign with hashpack or operator const signTx = await tx.sign(operatorKey); const result = await signTx.execute(client); ``` ## Key Transaction Types ### AccountCreate ```typescript new AccountCreateTransaction() .setKey(publicKey) .setInitialBalance(new Hbar(10)) .setAccountMemo("My account"); ``` ### TokenAssociate ```typescript new TokenAssociateTransaction() .setAccountId(accountId) .setTokenIds([tokenId1, tokenId2]); ``` ### TopicMessage ```typescript new TopicMessageTransaction() .setTopicId(topicId) .setMessage("Hello Hedera!"); ``` ## Network Endpoints - **Mainnet**: `https://mainnet.hashio.io/api` - **Testnet**: `https://testnet.hashio.io/api` ## Important Concepts - **Hbar**: 1 HBAR = 100,000,000 tinybars - **Account ID**: Format `shard.realm.num` (e.g., `0.0.12345`) - **Transaction Fee**: Small HBAR fee for each transaction - **Transaction Valid Duration**: 180 seconds by default


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: codenova58/huya (version 0.1.0)

--- name: huya description: 注册“虎牙直播”技能；用于直播平台公开页面、主播规则、活动信息与帮助说明整理。 homepage: `https://www.huya.com/` metadata: {"clawdbot":{"emoji":"📺"}} --- # 虎牙直播 用途与边界 - 面向公开官网、活动页、主播中心、规则页与帮助中心的信息整理 - 不提供代播、刷量、礼物套利、账号接管、风控规避或外挂能力 - 仅处理公开可见的直播分类、活动、主播规则与帮助信息 关键入口 - 主页：https://www.huya.com/ - 主播与活动：主播中心、活动页、赛事页、分类页入口 - 规则与帮助：直播规范、处罚公告、帮助中心、安全中心入口 常见任务 - 直播分类、活动入口、主播支持与公开规则整理 - 平台活动、赛事直播、主播成长规则与帮助信息汇总 - 违规处罚、账号安全、礼物与权益说明提取 数据字段 - 直播分类、活动入口、主播规则、链接 - 处罚说明、帮助页、安全中心、FAQ 链接 - 赛事页、主播中心、活动周期、权益说明 自动化要点 - 直播平台活动频繁变动，输出时标注时间与来源 - 不得输出刷量、作弊、诱导交易或规避风控建议 - 不处理主播后台、收益数据、私信或用户隐私数据 示例流程 - 活动整理：访问 Huya 活动页 → 提取时间与参与条件 → 输出表格 - 规则汇总：访问主播规则与处罚页 → 提取重点限制 → 输出清单 - 帮助目录：进入帮助中心 → 输出安全、直播、权益相关入口 合规提示 - 遵守平台规则、版权要求与账号安全要求 - 只整理公开信息，不输出敏感数据或非公开内容


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: smseow001/math-foundation (version 1.0.0)

--- name: math-foundation version: 1.0.0 description: Contemporary mathematics core knowledge system - 6 dimensions + Grand Unified Programs + milestone achievements. For complex math problem solving, financial engineering, algorithm design. --- # Math Foundation Knowledge Base ## 1. Logic & Axiomatic Foundations - Mathematical Logic, Set Theory, Axiomatic Systems - Godel Incompleteness Theorems, Formal Proofs ## 2. Algebraic Structures - Groups, Rings, Fields, Modules, Category Theory - Homological Algebra, Langlands Program ## 3. Geometry & Topology - Topology: continuity, homotopy, homology, manifolds - Differential Geometry, Riemann Geometry - Poincare Conjecture (solved by Perelman 2003) ## 4. Dynamics & Analysis - Real/Complex Analysis, Functional Analysis - Differential Equations, Dynamical Systems, Calculus of Variations ## 5. Random & Statistics - Probability Theory, Stochastic Processes, Martingales - Stochastic Differential Equations (SDE) - Law of Large Numbers, Central Limit Theorem ## 6. Discrete & Computation - Combinatorics, Graph Theory, Number Theory - Algorithm Complexity: P/NP/NP-Complete - Cryptography: RSA, Elliptic Curve ## Grand Unified Programs: Langlands Program Connects: Number Theory, Algebraic Geometry, Representation Theory, Analysis ## Financial Mathematics Core - Black-Scholes: dS = mu dt + sigma dW - Ito's Lemma, Girsanov Theorem - Feynman-Kac Formula ## Milestone Achievements - Poincare Conjecture (2003) - Fermat's Last Theorem (1994) - Green-Tao Theorem (2004) *For advanced AI problem solving*


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

--- name: book-driving-lessons description: Book driving-lessons services through Lokuli MCP. Use when user needs to find and book driving-lessons. Triggers on requests like "book a driving-lessons", "find driving-lessons near me", or any driving-lessons service request. --- # uook driving lessons Book driving-lessons 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": "driving-lessons", "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: clean (LOW)
Skill: drumrobot/tdd (version 0.1.1)

--- name: tdd metadata: author: es6kr version: "0.1.0" depends-on: [skill-kit] description: Test-Driven Development for coding and bug fixing. cycle - Red→Green→Refactor cycle, defining expected behavior, bug-fix TDD, anti-patterns [cycle.md], run - test execution workflow (environment detection→impact scope→execution→result reporting) [run.md], test-strategies - boundary value·equivalence partitioning·decision table·error guessing·path coverage test design techniques [test-strategies.md]. "TDD", "test first", "failing test", "doesn't work", "not working", "test design", "boundary value test", "test strategies", "equivalence partitioning", "bug fix", "run tests", "execute tests", "test run", "verify" triggers --- # TDD (Test-Driven Development) Test-Driven Development: define expected behavior first, then make it pass with implementation. ## Topics | Topic | Description | Guide | |-------|-------------|-------| | cycle | Red→Green→Refactor cycle, bug-fix TDD, anti-patterns | [cycle.md](./cycle.md) | | run | Test execution workflow (environment detection→impact scope→execution→result reporting) | [run.md](./run.md) | | test-strategies | Boundary value·equivalence partitioning·decision table·error guessing·path coverage | [test-strategies.md](./test-strategies.md) | ## Quick Reference ### TDD Cycle Define expected behavior → natural failure → implement → pass → refactor. See [cycle.md](./cycle.md). ### Test Execution Environment detection → impact scope → execution (unit→integration→e2e) → result reporting. See [run.md](./run.md). ### Test Design Techniques Decide which test cases to write (boundary value, equivalence partitioning, etc.). See [test-strategies.md](./test-strategies.md).


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: epsilon-rhk/weekly-report-writer (version 1.0.0)

--- name: weekly-report-writer description: 用于撰写结构清晰的中文周报。只要用户提到“周报、weekly report、本周总结、工作复盘、下周计划、给老板汇报”，即使没有明确说“写周报”，也应主动使用此技能来生成可直接发送的周报内容。 --- # Weekly Report Writer 目标：把零散工作信息整理成可直接提交的周报，突出结果、风险和下周计划。 ## 何时使用 出现以下任一场景时使用： - 用户让你写周报或润色周报 - 用户给了本周工作记录，希望整理成汇报 - 用户需要给上级同步进展、风险、计划 ## 执行流程 1. 先收集最小必要信息：本周完成、关键结果、问题风险、下周计划。 2. 信息不完整时，先基于已有内容生成“可提交草稿”，并明确标注待补充项。 3. 输出时优先结果导向，避免流水账，句子简洁可读。 ## 输出要求 - 默认输出中文。 - 每条工作内容优先使用“动作 + 结果 + 影响”表达。 - 量化就量化（如完成数量、耗时、转化、缺陷数、上线时间）。 - 风险必须包含“现状 + 影响 + 需要支持”。 ## 周报模板 始终优先使用以下结构： ```markdown # 第X周工作周报（YYYY-MM-DD ~ YYYY-MM-DD） ## 1. 本周完成事项 1. [事项] - 结果： - 业务/项目影响： ## 2. 关键成果（可量化） 1. ... 2. ... ## 3. 问题与风险 1. [问题/风险] - 当前状态： - 影响范围： - 需要支持： ## 4. 下周计划 1. [计划] - 预期产出： - 截止时间： ## 5. 需协调事项（可选） 1. ... ``` ## 语气与风格 - 面向管理者：结论先行、简明、客观。 - 不夸张，不使用口语化表达。 - 未确认信息不要编造，使用“待补充”占位。


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