OpenClaw/clawhub-security-signals huggingface dataset:
Skill: syajask/srt-to-md (version 1.0.0)

--- name: srt-to-doc description: |- 将 SRT 字幕文件转换为结构化 Markdown 文档的完整工作流。无需配置任何额外 API key，完全依赖当前 session 内置能力完成全部处理。 适用场景：用户上传或提供 .srt 字幕文件，想要将视频内容转化为可读文档；整理 Bilibili / YouTube 视频字幕；将字幕内容分析后生成知识文章；对视频内容做补充研究后输出报告。 触发关键词：.srt 文件、字幕文件、srt转文章、srt转文档、字幕整理、视频转文档、字幕分析。当用户提到 SRT、字幕、想要把视频内容变成文章或笔记时，务必使用此技能。 --- # SRT to Doc ## 输入 / 输出 - **输入**：`.srt` 字幕文件（支持 UTF-8 / GBK / Big5 等编码） - **输出**： - `{标题}.md` — 结构化分析文档（主交付物） - `{标题}_subtitles.txt` — 带时间轴的字幕原文（辅助交付物） - **命名约定**：取视频标题，中文保留，空格→下划线，特殊字符删除 - 例：`大明王朝1566_第一集.md` + `大明王朝1566_第一集_subtitles.txt` --- ## 四阶段流程 ### 阶段一：SRT → TXT ```bash python scripts/srt_to_txt.py input.srt {标题}_subtitles.txt ``` 脚本自动检测编码，输出带时间轴的干净 TXT，并打印条数与时长。 TXT 为独立交付物，**不进入最终 Markdown**。 --- ### 阶段二：内容分析 读取 TXT 后在当前 session 内完成，**不调用任何外部 API**： - 识别主题与核心议题，划分内容段落 - 提取 3-7 个核心观点（附时间戳） - 列出关键概念、人物、专有名词 - **判断内容类型**（影响阶段三的搜索方向）： - 知识/技术类 → 四个搜索方向全用 - 人文/叙事类（历史剧、访谈、纪录片）→ 跳过"工具/产品"方向，"最新动态"改搜史料/研究/评论 --- ### 阶段三：搜索补充 详细搜索策略见 `references/search_strategy.md`。 简要规则： - 基于阶段二提炼的概念、人物、工具名提炼搜索词 - 优先调用 **multi-search-engine** skill（若可用）；不可用时用内置 `web_search` - 每个关键词取最相关 2-3 条，记录来源 URL --- ### 阶段四：输出 Markdown 读取 `assets/doc_template.md`，按模板结构填写内容。 **必填章节**：内容概要、内容结构、核心观点 **条件章节**（有内容则写，无则省略）：关键概念、主要人物、相关动态/延伸阅读 --- ## 下游联动 文档生成后可直接交给以下 skill 继续处理： | 目标 | 调用 skill | |------|-----------| | 发布为微信公众号文章 | **wechat-mp-writer**（建议用"历史"或"技术/AI"模板） | | 搜索同类公众号参考文章 | **wechat-article-search** |


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: frank-bot07/openclaw-voice (version 1.0.0)

# OpenClaw Voice Skill ## Overview Voice conversation with AI via Whisper STT + ElevenLabs TTS. Records audio, transcribes, generates spoken responses, stores transcripts. For v1: CLI-based commands only (no real-time conversation mode — that's v1.1). Focus on transcript storage/search + TTS/STT wrappers. ## Tech Stack - Node.js ESM - better-sqlite3 (WAL mode always) - commander for CLI - uuid for IDs - @openclaw/interchange (import from ../interchange/src/index.js) - NO external audio packages — use child_process to call sox/rec and ffplay ## Database See migrations/001_initial.sql for schema. ## CLI See src/cli.js for commands. ## Tests Run with npm test.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: czwfelix/daily-game-news (version 2.0.0)

# Daily-Game-News Skill 每日游戏资讯自动抓取与报告生成技能。 ## 功能描述 1. 根据配置文件抓取 9 个游戏资讯网站的文章（成功率 100%） 2. 自动分类到 7 个大类（头条要闻、新品动态、厂商动态、行业数据、值得关注、投融资、其他） 3. 生成格式化报告并发送给用户 4. 同时生成 Word 文档存档 ## 支持的网站 ✅ 机核 GCORES、游民星空、游戏陀螺、触乐、游研社、GameLook、IGN、GameSpot、GameDeveloper ## 触发条件 - 定时任务：每天北京时间 10:00 自动执行 - 手动触发：用户提及 "每日游戏资讯"、"游戏新闻报告"、"Daily Game News" ## 配置文件 位置：`/home/admin/.openclaw/workspace/configs/news-crawler-config.json` ## 依赖 - python-docx>=1.1.0 - beautifulsoup4>=4.14.3 - lxml>=6.0.2 - httpx>=0.28.1 ## 输出 1. **飞书消息**: 格式化报告直接发送给用户 2. **Word 文档 (.docx)**: 存储在 `/home/admin/.openclaw/workspace/reports/daily-game-news/` 3. **文本文件 (.txt)**: 保留用于兼容，同目录 ## 使用示例 ```bash # 手动触发（使用 uv） cd ~/.openclaw/workspace/skills/daily-game-news uv run python scripts/crawler.py # 获取今日报告 获取今日游戏资讯报告 # 获取历史报告 获取 2026-03-07 游戏资讯报告


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: kostja94/showcase-page-generator (version 1.0.1)

--- name: showcase-page-generator description: When the user wants to create, optimize, or audit a showcase or gallery page for user-generated content. Also use when the user mentions "showcase," "gallery," "user work," "UGC," "creator showcase," "examples," or "made with [product]." For social proof components, use testimonials-generator. metadata: version: 1.0.1 --- # Pages: Showcase Guides showcase and gallery pages that display user-generated work, creator content, or "made with [product]" examples. Builds community, social proof, and inspiration. Common for design tools, no-code, and creator-focused products. **When invoking**: On **first use**, if helpful, open with 1–2 sentences on what this skill covers and why it matters, then provide the main output. On **subsequent use** or when the user asks to skip, go directly to the main output. ## Initial Assessment **Check for project context first:** If `.claude/project-context.md` or `.cursor/project-context.md` exists, read it for product, audience, and UGC sources. Identify: 1. **Content type**: Designs, sites, videos, templates, campaigns 2. **Source**: Curated, user-submitted, or both 3. **Format**: Grid, masonry, carousel; filter by category 4. **Primary goal**: Inspiration, sign up, community ## Page Structure | Section | Purpose | |---------|---------| | **Headline** | "See What Others Built" or "Made with [Product]" | | **Intro** | Inspire; show what's possible | | **Gallery** | Grid of items; thumbnail, title, creator, link | | **Categories** | Filter by type, industry, use case | | **Submit** | CTA to submit work; link to form or guidelines | | **Featured** | Highlight top picks; rotate periodically | ## Best Practices ### Curation - **Quality over quantity**: Show best work; maintain bar - **Diversity**: Mix industries, styles, use cases - **Permissions**: Ensure creator consent; attribution ### Display - **Visual-first**: Thumbnails, previews; minimal text - **Click-through**: To full project or creator profile - **Attribution**: Creator name, link; respect copyright ### Community - **Submit CTA**: "Share your work"; clear guidelines - **Featured creators**: Spotlight; builds loyalty - **Social**: Share to X, LinkedIn; encourage tagging ## Output Format - **Headline** and intro - **Gallery structure** (layout, filters) - **Per-item** format (thumbnail, title, creator) - **Submit** CTA and guidelines - **SEO** metadata ## Related Skills - **card**: Gallery card structure; thumbnail, title, creator, link - **grid, masonry, carousel**: Grid for uniform; masonry for varying heights (Pinterest-style); carousel for featured rotation - **customer-stories-page-generator**: Case studies vs. visual showcase; different formats - **testimonials-generator**: Testimonials as component; showcase can include quotes - **creator-program**: Creator showcase for program members - **landing-page-generator**: Showcase as conversion page


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: darker314159/anytocopy-extractor (version 1.0.0)

--- name: anytocopy-extractor description: | 使用 anytocopy.com 在线提取抖音、小红书等平台的视频文案。 当用户发送抖音、小红书等链接，想要提取文案时使用此技能。 支持50+平台：无水印视频下载、视频转音频、图片去水印等。 --- # AnyToCopy 文案提取器 使用 [AnyToCopy](https://www.anytocopy.com/) 在线提取抖音、小红书等平台的视频文案。 ## 工作流 ### Step 1：打开网站 使用 browser 工具导航到 AnyToCopy 首页： ``` https://www.anytocopy.com/ ``` ### Step 2：输入链接 找到输入框（textbox），输入用户提供的链接。 ### Step 3：点击提取 找到并点击 "Start Extract" 按钮。 ### Step 4：等待结果 等待页面加载出结果（文案内容）。可能需要等待几秒钟。 ### Step 5：提取文案 从页面中提取文案内容，返回给用户。 ## 注意事项 - 这是一个基于浏览器的自动化方案，适合临时使用 - 如果 AnyToCopy 网站有变化，需要更新 selectors - 提取是免费的，无需登录 - 支持 50+ 平台：抖音、小红书、快手、B站、微博等 ## 输出格式 向用户返回提取到的文案内容，包括： - 标题 - 正文/脚本 - 如果有视频/图片链接也可以一并提供


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

--- name: vantage description: "Vantage — Autonomous trading agent for Hyperliquid perpetual futures. Signal-to-execution in one loop. Runs on your machine. No cloud infra. No ongoing cost after purchase. Includes: live signal engine (funding rates, momentum, THORChain volume), Kelly criterion position sizing, EIP-712 signed order execution, paper trading mode, pre-flight setup validator, and profit sweep alerts." metadata: { "openclaw": { "emoji": "📈", "requires": { "bins": ["node"] } } } --- # Vantage — HL Autonomous Trading Agent **Version:** 1.0.0 **Author:** MoreBetter Studios (@morebetterclaw) **Product page:** https://morebetterstudios.com/products --- ## What It Does Vantage runs a continuous signal → decision → execution loop on Hyperliquid perpetual futures. ``` Every N minutes (configurable) Signal Engine → funding rates + momentum + THORChain volume Decision Layer → Qwen (local) | OpenAI | rule-based fallback Kelly Sizing → live account balance × confidence × fraction Hyperliquid → signed market order (your private key) Profit Alert → log when balance exceeds threshold ``` All market data uses **public APIs only** — no external API keys required. --- ## Setup ```bash npm install cp .env.example .env # Fill in your Hyperliquid private key + wallet address + trading limits node src/setup-check.js # validate before going live ``` --- ## Usage ```bash # Paper mode (no real orders) node src/index.js start --paper # Live trading node src/index.js start # Market data node src/index.js hl-data RUNE node src/index.js hl-arb BTC ETH RUNE # Positions node src/index.js hl-positions --paper node src/index.js hl-positions 0xYourWallet # Setup validator node src/setup-check.js ``` --- ## Configuration (`.env`) | Variable | Required | Default | Description | |---|---|---|---| | `HYPERLIQUID_PRIVATE_KEY` | Yes | — | Your HL account private key | | `HYPERLIQUID_WALLET_ADDRESS` | Yes | — | Your wallet address | | `MAX_POSITION_SIZE_USD` | Yes | 500 | Hard cap per position | | `MAX_OPEN_POSITIONS` | Yes | 3 | Max concurrent positions | | `MAX_ACCOUNT_RISK_PCT` | Yes | 2 | Max % of balance per trade | | `KELLY_FRACTION` | Yes | 0.25 | Kelly multiplier (0.25 = conservative) | | `CRON_INTERVAL_MINUTES` | Yes | 60 | Scan frequency | | `OLLAMA_URL` | No | — | Local Ollama (recommended) | | `OLLAMA_MODEL` | No | qwen3.5:35b | Ollama model | | `OPENAI_API_KEY` | No | — | OpenAI fallback | | `PROFIT_SWEEP_ENABLED` | No | false | Enable profit sweep alerts | | `PROFIT_SWEEP_THRESHOLD_USD` | No | 100 | Sweep trigger (USD) | --- ## Moving Funds Cross-Chain - **Crypto Swap Agent** — [LINK TO BE UPDATED] - **THORChain Swap Website** — [LINK TO BE UPDATED] _(MoreBetter Studios products — morebetterstudios.com/products)_


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: omactiengartelle/liminal-space-generator (version 1.0.0)

--- name: liminal-space-generator description: Generate eerie liminal space images, dreamcore backgrounds, and backrooms-style scenes — empty hallways, abandoned pools, fluorescent-lit rooms, uncanny dreamscapes, analog horror aesthetics, nostalgic unsettling environments, weirdcore and oneiric art for creators, horror writers, aesthetic Tumblr/TikTok accounts, and atmospheric game or video backdrops via the Neta AI image generation API (free trial at neta.art/open). tools: Bash --- # Liminal Space Generator Generate eerie liminal space images, dreamcore backgrounds, and backrooms-style scenes — empty hallways, abandoned pools, fluorescent-lit rooms, uncanny dreamscapes, analog horror aesthetics, nostalgic unsettling environments, weirdcore and oneiric art for creators, horror writers, aesthetic Tumblr/TikTok accounts, and atmospheric game or video backdrops. ## Token Requires a Neta API token (free trial at <https://www.neta.art/open/>). Pass it via the `--token` flag. ```bash node <script> "your prompt" --token YOUR_TOKEN ``` ## When to use Use when someone asks to generate or create liminal space generator images. ## Quick start ```bash node liminalspacegenerator.js "your description here" --token YOUR_TOKEN ``` ## Options - `--size` — `portrait`, `landscape`, `square`, `tall` (default: `landscape`) - `--ref` — reference image UUID for style inheritance ## Install ```bash npx skills add omactiengartelle/liminal-space-generator ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: aineuralagency/neural-learning-engine (version 1.0.0)

# Neural Learning Engine ## Description Neural Learning Engine is a lightweight AI skill that simulates a neural network learning loop. It processes user inputs, detects patterns, stores them in memory, and generates improved outputs over time. The system mimics neural behavior using a structured pipeline of input, processing, memory, and adaptive response. This skill is designed as a foundational module for building neural-based AI systems inside AI agents. --- ## Features - Neural-style input processing - Basic pattern recognition (simulated) - Memory-based learning structure - Adaptive response generation - Structured output format --- ## How It Works Input → Processing → Memory → Output 1. The system receives an input (command, event, or request) 2. It analyzes the input and detects a pattern 3. The pattern is conceptually stored in memory 4. The system generates an improved response --- ## Example ### Input download guide ### Output Step 1: Complete the payment Step 2: Access the members area Step 3: Download your guide --- ## Output Format The system returns a structured response: { "input": "user request", "pattern": "detected pattern", "output": "generated response", "confidence": 0.82 } --- ## Memory Concept The system simulates a neural memory layer where patterns are stored and reused. Example structure: [ { "input": "download guide", "pattern": "user intent: acquisition", "response": "step-by-step instructions" } ] --- ## Use Cases - AI assistants - Neural-based decision systems - Dashboard integrations - Voice-controlled AI interfaces - Automation workflows --- ## Integration This skill can be integrated with: - AI agents - Web dashboards - API systems - Voice interaction layers Optional enhancements: - AI reasoning APIs (e.g. Groq) - Real-time event tracking - Backend neural systems (Python) --- ## Notes This skill simulates neural behavior in a lightweight way and is designed for easy integration and scalability. It can be extended with real machine learning models or external AI APIs. --- ## Author AI Neural Agency


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: jhauga/finalize-agent-prompt (version 1.0.0)

--- name: finalize-agent-prompt description: 'Finalize prompt file using the role of an AI agent to polish the prompt for the end user.' --- # Finalize Agent Prompt ## Current Role You are an AI agent who knows what works best for the prompt files you have seen and the feedback you have received. Apply that experience to refine the current prompt so it aligns with proven best practices. ## Requirements - A prompt file must be provided. If none accompanies the request, ask for the file before proceeding. - Maintain the prompt’s front matter, encoding, and markdown structure while making improvements. ## Goal 1. Read the prompt file carefully and refine its structure, wording, and organization to match the successful patterns you have observed. 2. Check for spelling, grammar, or clarity issues and correct them without changing the original intent of the instructions.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: rickzhou/stock-study (version 1.0.0)

--- name: stock_study description: Senior equity research analysis for single tickers. Use when user requests detailed stock analysis with company overview, Wall Street consensus (analyst ratings, price targets), institutional activity, and analyst upgrades/downgrades. Cite all metrics with source and date. --- # Stock Study - Senior Equity Research Analyst You are a senior equity research analyst at a top-tier investment bank with access to Bloomberg, FactSet, and SEC filings. Cite every metric with its source and date. If data is unavailable or potentially outdated, say so explicitly. Do not estimate or fabricate any numbers. ## Required Analysis ### Step 1 — Company Overview - **What the company does in plain English**: 2-3 sentence business description - **Business model and revenue streams**: Breakdown by percentage of total revenue in a table - **Key competitive advantage**: One-sentence moat ### Step 2 — Wall Street Consensus - Number of analysts covering this stock - Buy / Hold / Sell breakdown with firm names - Average, highest, and lowest price targets - Most recent analyst upgrade or downgrade (firm name and date) ### Step 3 — Institutional Activity - Top 5 institutional holders with position changes (QoQ) - Notable hedge fund activity (new positions or exits with SEC filing dates) ## Format - Clear markdown headers - Tables for quantitative data - Source citations immediately after each metric - Flag any data that may be more than 30 days old ## Watchlist (for reference) Current tickers: MIAX, YUM, GM, PENG, FUTU


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: nexscope/google-shopping-optimization (version 1.0.0)

--- name: google-shopping-optimization description: "Optimize Google Shopping campaigns and product feeds for maximum visibility and ROAS. Feed optimization, bidding strategy, campaign structure, and Performance Max integration." metadata: nexscope: emoji: "🛒" category: ecommerce --- # Google Shopping Optimization 🛒 Optimize Google Shopping campaigns and product feeds for maximum visibility and ROAS. Feed optimization, bidding strategy, campaign structure, and Performance Max integration. **Supported platforms:** Amazon, Shopify, WooCommerce, Walmart, TikTok Shop, Etsy, eBay, BigCommerce. Built by [Nexscope](https://www.nexscope.ai/) — your AI assistant for smarter e-commerce decisions. ## Install ```bash npx skills add nexscope-ai/eCommerce-Skills --skill google-shopping-optimization -g ``` ## Usage ``` Optimize my Google Shopping feed. I have 200 products on Shopify. Current ROAS is 3x, I want to hit 5x. Monthly budget: $5,000. ``` ## Capabilities - Product feed optimization (titles, descriptions, images, attributes) - Google Merchant Center compliance and error resolution - Campaign structure (Standard Shopping vs Performance Max) - Bidding strategy selection and optimization - Product segmentation and priority campaign structure - ROAS optimization by product group - Negative keyword management for Shopping campaigns ## How This Skill Works **Step 1:** Collect information from the user's message — product, platform, current situation, and goals. **Step 2:** Ask one follow-up with all remaining questions using multiple-choice format. Allow shorthand answers (e.g., "1b 2c 3a"). **Step 3:** Research and analyze using the frameworks and methodology below. **Step 4:** Deliver structured, actionable output with specific recommendations, not vague advice. ## Output Format - Start with a summary of findings - Include specific data points and benchmarks where available - Provide prioritized action items - Mark estimates with ⚠️ when based on incomplete data - End with concrete next steps ## Other Skills More e-commerce skills: [nexscope-ai/eCommerce-Skills](https://github.com/nexscope-ai/eCommerce-Skills) Amazon-specific skills: [nexscope-ai/Amazon-Skills](https://github.com/nexscope-ai/Amazon-Skills) Built by [Nexscope](https://www.nexscope.ai/) — your AI assistant for smarter e-commerce decisions.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: quant520/stock-index-monitoring-ai (version 1.0.0)

--- name: hello-world description: 向用户返回Hello World问候，支持自定义名称 version: 1.0.0 metadata: openclaw: emoji: 👋 requires: bins: [] # 无外部依赖 --- # Hello World Skill ## 触发方式 1. 命令格式：/hello-world [用户名] 2. 自然语言：“说一句Hello World”“跟XX打个招呼” ## 执行逻辑 1. 若用户传入用户名（如/hello-world 小明），返回“Hello 小明！👋”； 2. 若无用户名，返回“Hello World！👋”。 ## 示例 - 输入：/hello-world 输出：Hello World！👋 - 输入：/hello-world 豆包 输出：Hello 豆包！👋


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: golngod/sushi-perspective (version 1.0.0)

--- name: sushi-perspective description: 基于女娲心智蒸馏方法论，对苏轼思维模式、人格特质、决策逻辑进行系统性蒸馏 license: MIT compatibility: - claude-code - copilot - cursor - openclaw - coze author: 胡田 version: 1.0.0 --- # 胡田-OPC导师-苏轼-心智模型.Skill ## 技能概述 本Skill基于女娲心智蒸馏方法论，对苏轼的思维模式、人格特质、决策逻辑进行系统性蒸馏提炼，形成可复用的心智模型，为OPC、超级个体、创业者提供逆境生存智慧和生活美学指导。 ### 苏轼的核心定位 **罕见全能型顶级才华的知识分子** - 职业发展不顺，因政局观点不同多次被贬 - 乌台诗案差点送命，但随后开启旷达人生的辉煌 - 书法、诗词、绘画、美食、工艺都有顶级水准 - 越是落魄越是显出伟大，如同《定风波》中的表述 **与其他三家的本质区别** - 不是范仲淹的硬刚 → 而是旷达随遇而安 - 不是王阳明的调和 → 而是逍遥自在 - 不是曾国藩的隐忍 → 而是苦中作乐 --- ## 核心心法 ### 1. 竹杖芒鞋轻胜马，谁怕？一蓑烟雨任平生 - 面对不确定性的超然态度 - 内心强大才是真正的强大 - 外界无法伤害内心自由的人 ### 2. 也无风雨也无晴 - 超越成败，超越荣辱 - 不以物喜，不以己悲 - 二元对立之外的第三境界 ### 3. 此心安处是吾乡 - 随遇而安的生存智慧 - 流动性时代的心理建设 - 心安即是归处 --- ## 六路采集体系 | 维度 | 文件 | 核心内容 | |------|------|---------| | **核心论点库** | [01-核心论点库.md](references/01-核心论点库.md) | 旷达哲学、全能才华、逆境升华 | | **即兴反应库** | [02-即兴反应库.md](references/02-即兴反应库.md) | 生死危机、逆境、政敌、创作反应模式 | | **句式指纹库** | [03-句式指纹库.md](references/03-句式指纹库.md) | 千古名句、语言风格DNA、四家对比 | | **外部观察模式库** | [04-外部观察模式库.md](references/04-外部观察模式库.md) | 同时代人、政敌、史家、民间评价 | | **逻辑权重矩阵** | [05-逻辑权重矩阵.md](references/05-逻辑权重矩阵.md) | 决策框架、逆境创造力矩阵、四家对比 | | **思想演化时间线** | [06-思想演化时间线.md](references/06-思想演化时间线.md) | 人生五阶段，从锋芒毕露到旷达逍遥 | --- ## 三重验证（待完成） ### 1. 跨域复现验证 用苏轼的思维方式解决当代问题 ### 2. 生成力验证 生成苏轼风格的内容 ### 3. 排他性验证 与王阳明、曾国藩、范仲淹的量化对比 --- ## 对OPC/超级个体的价值 ### 逆境生存智慧 - 越是低谷越是创造力爆发 - 限制反而激发想象力 - 苦难是艺术的催化剂 ### 超级个体生存模板 - 一技在身不如多技傍身 - 才华是最好的通行证 - 内心强大才是真强大 ### 生活美学实践 - 工作是为了生活，不是生活为了工作 - 会工作更要会生活 - 在平凡中发现美创造美 --- ## 使用方法 ``` # 当你遇到以下场景时调用本Skill - 创业失败陷入低谷时 - 被排挤被误解被陷害时 - 工作不顺感到迷茫时 - 想平衡工作与生活时 - 想提升生活美学品味时 # 调用方式 "用苏轼的思维方式分析这个问题" "如果苏轼遇到这种情况会怎么做" "苏轼的旷达哲学对我有什么启示" ``` --- ## 蒸馏进度 | 阶段 | 进度 | 状态 | |------|------|------| | 六路采集框架 | 100% | ✅ 完成 | | 核心内容填充 | 30% | 🟡 进行中 | | 三重验证 | 0% | ⚪ 待启动 | | 深度解读 | 0% | ⚪ 待启动 | | **总进度** | **30%** | 🟡 进行中 |


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