OpenClaw/clawhub-security-signals huggingface dataset:
Skill: ljseeking/geo-analyzer (version 1.0.5)

--- name: analyze-geo-performance version: 1.0.0 description: Analyzes how well a specific brand or product is mentioned and represented by LLMs (Generative Engine Optimization). Uses a two-stage pipeline: a Probing stage to query the model for industry recommendations, and a Judge stage to extract structured brand mention data. homepage: https://github.com/LJseeking/lifesignal metadata: openclaw: requires: env: - DEEPSEEK_API_KEY bins: - python3 primaryEnv: DEEPSEEK_API_KEY registry: requires: env: - DEEPSEEK_API_KEY primaryCredential: DEEPSEEK_API_KEY dataSent: "Brand name and category keyword parameters are sent to the DeepSeek API for GEO analysis." expectedCosts: "Incurs standard API token usage costs based on DeepSeek's pricing model." --- # analyze-geo-performance 一个用于测试品牌或产品在大模型中 **GEO（生成式引擎优化）** 表现的分析技能。 ## 功能概述 本 Skill 通过两阶段 LLM 调用流程，自动化地检测目标品牌在 AI 推荐场景中的曝光情况： 1. **探针阶段（Probing）**：向 DeepSeek-chat 提出一个客观的行业咨询问题，让模型自由推荐解决方案，捕获其输出文本。 2. **裁判阶段（Judge）**：将捕获的文本和目标品牌名发给裁判模型，强制输出结构化 JSON，包含：是否提及、情感倾向、提及上下文、以及提及的竞品列表。 ## 输入参数 | 参数 | 类型 | 必填 | 说明 | |---|---|---|---| | `brand_name` | string | ✅ | 需要验证的品牌或产品名（如 `CoolTrade`） | | `category_keyword` | string | ✅ | 行业或痛点关键词（如 `数字货币高频套利系统`） | ## 输出格式（JSON Schema） ```json { "mentioned": true, "sentiment": "positive", "context": "...提及品牌的上下文句子...", "competitors_mentioned": ["竞品A", "竞品B"] } ``` | 字段 | 类型 | 说明 | |---|---|---| | `mentioned` | boolean | 目标品牌是否在推荐结果中被提及 | | `sentiment` | string | 情感倾向：`positive` / `negative` / `neutral` / `none` | | `context` | string \| null | 提及品牌时的具体上下文句子，未提及则为 `null` | | `competitors_mentioned` | array | 被模型主动推荐的竞品品牌列表 | ## 使用前提 请确保在运行环境中配置了 `DEEPSEEK_API_KEY` 环境变量： ```bash export DEEPSEEK_API_KEY="your_deepseek_api_key_here" ``` ## 安装依赖 ```bash pip install -r requirements.txt ``` ## 本地命令行测试 ```bash python3 main.py --brand "CoolTrade" --category "数字货币高频套利系统" ``` ## 在 Agent 中调用 当用户提出以下类型的请求时，触发本技能： - "帮我测试 [品牌名] 在大模型中的 GEO 表现" - "分析 [品牌名] 在 [行业] 领域的大模型可见度" - "大模型会推荐 [品牌名] 吗？" - "检查 [品牌名] 有没有被 AI 提到" **执行步骤：** 1. 从用户输入中提取 `brand_name` 和 `category_keyword` 两个参数。 2. 调用 `main.py` 执行两阶段分析流程（所有 API 调用已封装在脚本内）。 3. 将返回的 JSON 结果解析后，用自然语言向用户说明分析结论，例如： - 品牌是否出现在推荐列表中 - 被提及时的情感是正面、负面还是中性 - 哪些竞品同时被提及，可能形成竞争威胁 ## 安全声明 - ✅ API Key 通过环境变量注入，代码中无任何硬编码凭据 - ✅ 所有外部 API 调用均限定为 `api.deepseek.com` - ✅ 无文件写入、无系统级权限要求 - ✅ 无数据持久化，分析结果仅在当前会话中返回

path: requirements.txt
content: openai>=1.0.0
pydantic>=2.0.0
python-dotenv>=1.0.0


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: sunshine-del-ux/api-monitor-dashboard (version 1.0.0)

# API Monitor Dashboard Real-time API monitoring and alerting system. ## Features - Real-time uptime monitoring - Response time tracking - Error rate alerts - Custom health checks - Email/Slack notifications - Historical data - Auto-recovery detection ## Usage ```bash # Start monitoring ./monitor.sh start # Add endpoint ./monitor.sh add https://api.example.com/health # Check status ./monitor.sh status ``` ## Dashboard Open browser to view dashboard: ``` http://localhost:3000 ``` ## Configuration Edit `config.json` to customize: - Check intervals - Timeout settings - Alert thresholds - Notification channels ## Requirements - Node.js 18+ - Docker (optional) ## Author Sunshine-del-ux


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

--- name: commit description: Create a git commit with a contextual message based on current changes, then push the branch. allowed-tools: [Bash] --- ## Context - Current git status: !`git status` - Current git diff (staged and unstaged changes): !`git diff HEAD` - Current branch: !`git branch --show-current` - Recent commits: !`git log --oneline -10` ## Your task Based on the above changes, stage everything, create one commit with a contextual message, and push the current branch. Required sequence: 1. Stage changes. 2. Create a single commit. 3. Push the current branch to origin (use `--set-upstream origin <branch>` if needed). Do not use interactive commands and do not output extra commentary.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: engsathiago/autonomia-agente (version 1.0.0)

--- name: autonomia-agente description: "Transforma agentes IA de seguidores de tarefas em parceiros proativos que antecipam necessidades e melhoram continuamente. Arquitetura proativa com WAL Protocol, Working Buffer e padrões testados em batalha." version: 1.0.0 author: eve-agent license: MIT tags: - autonomia - proativo - self-improvement - portugues - brasil category: autonomy --- # Autonomia do Agente **Por EVE** — Skill para agentes OpenClaw **Uma arquitetura proativa e de auto-melhoria para seu agente de IA.** A maioria dos agentes apenas espera. Este antecipa suas necessidades — e melhora com o tempo. ## Os Três Pilares **Proativo — cria valor sem ser pedido** ✅ **Antecipa suas necessidades** — Pergunta "o que ajudaria meu humano?" em vez de esperar ✅ **Reverse prompting** — Surpreende com ideias que você não sabia que precisava ✅ **Check-ins proativos** — Monitora o que importa e reach out quando necessário **Persistente — sobrevive à perda de contexto** ✅ **WAL Protocol** — Escreve detalhes críticos ANTES de responder ✅ **Working Buffer** — Captura cada troca na zona de perigo ✅ **Compaction Recovery** — Sabe exatamente como recuperar após perda de contexto **Auto-melhoria — fica melhor em te servir** ✅ **Self-healing** — Corrige seus próprios problemas para focar nos seus ✅ **Relentless resourcefulness** — Tenta 10 abordagens antes de desistir ✅ **Evolução segura** — Guardrails previnem drift e complexidade ## Uso Use esta skill quando: - Quiser que seu agente seja mais proativo - Quiser memória persistente entre sessões - Quiser auto-melhoria contínua ## Instalação ```bash clawhub install autonomia-agente ``` ## Estrutura ``` .autonomia/ ├── WAL.md # Write-Ahead Log ├── BUFFER.md # Working Buffer ├── LEARNINGS.md # Aprendizados ├── ERRORS.md # Erros encontrados └── RECOVERY.md # Protocolo de recuperação ``` ## Em Português Esta skill foi criada especialmente para a comunidade brasileira de agentes IA. --- #autonomia #proativo #self-improvement #portugues #brasil


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

--- name: traceroute-tool description: Trace network path to a destination host. Use for network diagnostics, latency analysis, and routing path discovery. --- # Traceroute - Network Path Tracer Trace the route packets take to reach a network host. Shows each hop with IP address and response time, helping identify network bottlenecks and routing issues. ## Usage ```bash traceroute-tool [options] <host> ``` ## Options - `-n`: Show numeric addresses only (no DNS) - `-m N`: Set max hops (default: 30) - `-w N`: Set wait time per hop - `-q N`: Set queries per hop ## Examples ```bash traceroute-tool google.com traceroute-tool -n 8.8.8.8 traceroute-tool -m 20 example.com ```

path: scripts/traceroute.py
content: #!/usr/bin/env python3
"""Traceroute Tool - Trace route."""
import subprocess, sys
host = sys.argv[1] if len(sys.argv) > 1 else 'localhost'
subprocess.run(['traceroute', host])


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: freeter226/timestamp-toolkit (version 1.0.0)

--- name: timestamp-toolkit description: Timestamp conversion tool. Convert between Unix timestamp, datetime, and various date formats. metadata: { "openclaw": { "emoji": "🕐", "requires": { "bins": ["python3"] } } } --- # Timestamp Toolkit A simple timestamp conversion and formatting tool. ## Features - **Timestamp to DateTime** - Convert Unix timestamp to readable datetime - **DateTime to Timestamp** - Convert datetime string to Unix timestamp - **Format Conversion** - Convert between various date formats - **Current Time** - Get current timestamp in multiple formats - **Relative Time** - Calculate time differences ## Usage ```bash python3 skills/timestamp-toolkit/scripts/timestamp.py <action> [options] ``` ## Actions | Action | Description | |--------|-------------| | `now` | Get current timestamp | | `to-datetime` | Convert timestamp to datetime | | `to-timestamp` | Convert datetime to timestamp | | `format` | Format datetime string | | `diff` | Calculate time difference | ## Options | Option | Type | Default | Description | |--------|------|---------|-------------| | `--input` | string | - | Input timestamp or datetime | | `--format` | string | %Y-%m-%d %H:%M:%S | Output format | | `--tz` | string | local | Timezone (local, utc, or timezone name) | | `--unit` | string | s | Timestamp unit (s=seconds, ms=milliseconds) | ## Examples ```bash # Get current timestamp python3 skills/timestamp-toolkit/scripts/timestamp.py now # Convert timestamp to datetime python3 skills/timestamp-toolkit/scripts/timestamp.py to-datetime --input 1700000000 # Convert datetime to timestamp python3 skills/timestamp-toolkit/scripts/timestamp.py to-timestamp --input "2024-03-22 14:00:00" # Format datetime python3 skills/timestamp-toolkit/scripts/timestamp.py format --input "2024-03-22" --format "%Y年%m月%d日" # Calculate time difference python3 skills/timestamp-toolkit/scripts/timestamp.py diff --input "2024-01-01" --input2 "2024-03-22" ``` ## Supported Formats | Format | Example | |--------|---------| | ISO 8601 | 2024-03-22T14:00:00Z | | Standard | 2024-03-22 14:00:00 | | Chinese | 2024年3月22日 14:00:00 | | Custom | Any Python strftime format | ## Use Cases 1. **Log Analysis** - Convert timestamps in logs to readable format 2. **API Development** - Handle timestamp conversions 3. **Data Processing** - Normalize time formats 4. **Debugging** - Quick timestamp conversions ## Current Status In development.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: zhaozy/theme-core (version 1.0.0)

--- name: theme-core version: 1.0.0 description: Theme design for novels. Define the core question of the story and validate sustainability for 300k-8M words. Use when starting a new novel project or entering PLANNING phase. --- # Theme Core ## Core Question What question does this story ask? ## Quick Template ```json { "theme": { "core_question": "One sentence question", "scale": "micro/macro", "antithesis": "The opposite of this question", "statement": "Author's stance (answer at the end)" } } ``` ## Sustainability Check - 300k words: Can the theme stand alone? - 3M words: How to deepen? - 8M words: How many iterations needed? ## References - Full guide: See [references/full-guide.md](references/full-guide.md) - Prototypes: See [references/prototypes.md](references/prototypes.md)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: abdhilabs/config-guardian (version 1.0.1)

--- name: config-guardian description: Safe OpenClaw config updates with automatic backup, validation, and rollback. For agent use - prevents invalid config updates. --- # Config Guardian ## Overview **For Agent use only.** Safe config updates with automatic backup, validation, and rollback. Prevents the agent from updating non-existent keys or invalid values. ## When to Use Use this skill **every time** you need to update `openclaw.json`. Prevents: - Updating non-existent config keys - Using invalid values - Breaking the gateway with bad config ## Workflow: Atomic Apply (Default) For all config changes - handles everything in one command: ```bash ./scripts/atomic_apply.sh <config_path> <new_value> # Example: ./scripts/atomic_apply.sh "agents.defaults.model.primary" "minimax-portal/MiniMax-M2.5" ``` **What it does:** 1. Creates timestamped backup automatically 2. Applies change via `openclaw config set <path> <value>` 3. Validates with `openclaw doctor --non-interactive` 4. **Auto-rollback** if validation fails 5. Trap ensures rollback even on crash **Backup location:** ``` ~/.openclaw/config-guardian-backups/ ``` ## Guardrails - **Never** restart or apply config without explicit user approval - **Always** use `atomic_apply.sh` - If validation fails -> config auto-rolled back, don't force it ## Scripts | Script | Purpose | |--------|---------| | `atomic_apply.sh` | Default - all-in-one safe apply | | `validate_config.sh` | Validate via OpenClaw doctor | | `restore_config.sh` | Manual restore from backup |

path: scripts/restore_config.sh
content: #!/usr/bin/env bash
set -euo pipefail

BACKUP_PATH="${1:?Usage: restore_config.sh <backup_path> [config_path]}"
CONFIG_PATH="${2:-$HOME/.openclaw/openclaw.json}"

cp "$BACKUP_PATH" "$CONFIG_PATH"
echo "Restored $CONFIG_PATH from $BACKUP_PATH"


path: .clawhub/origin.json
content: {
  "version": 1,
  "registry": "https://clawhub.ai",
  "slug": "config-guardian",
  "installedVersion": "1.0.0",
  "installedAt": 1770171837891,
  "metadata": {
    "openclaw": {
      "requires": {
        "bins": ["openclaw", "python3"]
      }
    }
  }
}


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: terrycarter1985/terrycarter1985-gifgrep (version 1.0.0)

--- name: gifgrep description: Search, preview, download, and process GIFs from Tenor and Giphy author: steipete author-url: https://github.com/steipete/gifgrep version: 1.0.0 tags: ["gif", "media", "search", "images"] metadata: { "requires": { "bins": ["gifgrep"], "env": ["GIPHY_API_KEY", "TENOR_API_KEY"] }, "install": [ { "type": "homebrew", "package": "steipete/tap/gifgrep", "label": "Install GifGrep CLI (Homebrew)" }, { "type": "go", "package": "github.com/steipete/gifgrep/cmd/gifgrep@latest", "label": "Install GifGrep CLI (Go)" } ] } --- # GifGrep GIF Search Skill ## Overview Search, preview, download, and process GIFs from Tenor and Giphy providers. Supports CLI output, interactive TUI with previews, static frame extraction, and frame sheet generation. ## Configuration | Environment Variable | Description | |----------------------|-------------| | `GIPHY_API_KEY` | Optional for Giphy provider (uses demo key if not set) | | `TENOR_API_KEY` | Optional for Tenor provider (uses demo key if not set) | ## Usage Examples ### Search for GIFs ```json { "tool": "gifgrep_search", "parameters": { "query": "excited reaction", "limit": 5, "format": "url" } } ``` ### Search and download top GIF ```json { "tool": "gifgrep_search", "parameters": { "query": "happy birthday", "limit": 1, "download": true, "output_path": "/workspace/downloads/birthday.gif" } } ``` ### Extract static frame from GIF ```json { "tool": "gifgrep_extract_frame", "parameters": { "input_path": "/workspace/downloads/birthday.gif", "time": "1.5s", "output_path": "/workspace/output/frame.png" } } ``` ### Generate frame sheet ```json { "tool": "gifgrep_generate_sheet", "parameters": { "input_path": "/workspace/downloads/birthday.gif", "frames": 9, "columns": 3, "output_path": "/workspace/output/sheet.png" } } ``` ## Supported Providers - `auto` (default): Uses Giphy if API key is set, otherwise Tenor - `tenor`: Tenor GIF provider - `giphy`: Giphy GIF provider (requires API key for higher rate limits)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: notabhay/grok-search (version 0.2.1)

--- name: grok-search description: Search the web or X/Twitter using xAI Grok server-side tools (web_search, x_search) via the xAI Responses API. Use when you need tweets/threads/users from X, want Grok as an alternative to Brave, or you need structured JSON + citations. homepage: https://docs.x.ai/docs/guides/tools/search-tools triggers: ["grok", "xai", "search x", "search twitter", "find tweets", "x search", "twitter search", "web_search", "x_search"] metadata: {"clawdbot":{"emoji":"🔎","requires":{"bins":["node"],"env":["XAI_API_KEY"]},"primaryEnv":"XAI_API_KEY"}} --- Run xAI Grok locally via bundled scripts (search + chat + model listing). Default output for search is *pretty JSON* (agent-friendly) with citations. ## API key The script looks for an xAI API key in this order: - `XAI_API_KEY` env var - `~/.clawdbot/clawdbot.json` → `env.XAI_API_KEY` - `~/.clawdbot/clawdbot.json` → `skills.entries["grok-search"].apiKey` - fallback: `skills.entries["search-x"].apiKey` or `skills.entries.xai.apiKey` ## Run Use `{baseDir}` so the command works regardless of workspace layout. ### Search - Web search (JSON): - `node {baseDir}/scripts/grok_search.mjs "<query>" --web` - X/Twitter search (JSON): - `node {baseDir}/scripts/grok_search.mjs "<query>" --x` ### Chat - Chat (text): - `node {baseDir}/scripts/chat.mjs "<prompt>"` - Chat (vision): - `node {baseDir}/scripts/chat.mjs --image /path/to/image.jpg "<prompt>"` ### Models - List models: - `node {baseDir}/scripts/models.mjs` ## Useful flags Output: - `--links-only` print just citation URLs - `--text` hide the citations section in pretty output - `--raw` include the raw Responses API payload on stderr (debug) Common: - `--max <n>` limit results (default 8) - `--model <id>` (default `grok-4-1-fast`) X-only filters (server-side via x_search tool params): - `--days <n>` (e.g. 7) - `--from YYYY-MM-DD` / `--to YYYY-MM-DD` - `--handles @a,@b` (limit to these handles) - `--exclude @bots,@spam` (exclude handles) ## Output shape (JSON) ```json { "query": "...", "mode": "web" | "x", "results": [ { "title": "...", "url": "...", "snippet": "...", "author": "...", "posted_at": "..." } ], "citations": ["https://..."] } ``` ## Notes - `citations` are merged/validated from xAI response annotations where possible (more reliable than trusting the model’s JSON blindly). - Prefer `--x` for tweets/threads, `--web` for general research.


Security scan verdicts — ClawScan: clean | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: wirec-yzx/private-knowledge-base (version 1.0.0)

--- name: private-knowledge-base description: Personal knowledge base for PDFs, papers, and documents with cross-document Q&A and concept retrieval. Use when: (1) User asks questions about stored documents ("which doc mentions X?"), (2) Need to summarize concepts across multiple PDFs, (3) User wants to ingest new PDFs/papers into knowledge base, (4) Cross-document linking and association is needed, (5) Fast semantic search over personal document collection. version: 1.0.0 --- # Private Knowledge Base Personal document storage and retrieval system for PDFs, papers, and research documents. ## Quick Start ### Ingest Documents ```bash # Add PDF to knowledge base ./scripts/ingest.sh ~/path/to/document.pdf # Process entire folder ./scripts/ingest-folder.sh ~/papers/ ``` ### Query Knowledge Base ```bash # Search for concept across all documents ./scripts/search.sh "transformer architecture" # Get summary of concept from relevant docs ./scripts/summarize.sh "attention mechanism" ``` ## Core Workflows ### 1. Document Ingestion When user provides new PDFs or papers: 1. Create document entry in `kb/index.json` 2. Extract text and metadata 3. Generate embeddings for semantic search 4. Store in `kb/docs/` with normalized name ### 2. Cross-Document Q&A When user asks "which document mentions X?" or "summarize X from my docs": 1. Search embeddings for relevant passages 2. Retrieve source documents 3. Synthesize answer across documents 4. Cite sources with document names and page numbers ### 3. Concept Linking Build associations between documents: - Shared concepts - Citation relationships - Topic clusters ## File Structure ``` private-knowledge-base/ ├── SKILL.md ├── scripts/ │ ├── ingest.sh # Single document ingestion │ ├── ingest-folder.sh # Batch ingestion │ ├── search.sh # Semantic search │ └── summarize.sh # Cross-document summary ├── references/ │ └── schema.md # KB index schema └── kb/ # Created at runtime ├── index.json ├── embeddings/ └── docs/ ``` ## Usage Examples **User**: "我之前存的文档里，哪篇提到了 transformer?" → Run `./scripts/search.sh "transformer"` **User**: "总结一下我文档里关于 attention 的内容" → Run `./scripts/summarize.sh "attention"` **User**: "把这篇 PDF 加到知识库" → Run `./scripts/ingest.sh <pdf-path>` ## Configuration Set knowledge base location: ```bash export KB_ROOT=~/.openclaw/workspace/kb ``` Default: `~/kb` if not set.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: steipete/qmd (version 1.0.0)

--- name: qmd description: Local search/indexing CLI (BM25 + vectors + rerank) with MCP mode. homepage: https://tobi.lutke.com metadata: {"clawdbot":{"emoji":"📝","requires":{"bins":["qmd"]},"install":[{"id":"node","kind":"node","package":"https://github.com/tobi/qmd","bins":["qmd"],"label":"Install qmd (node)"}]}} --- # qmd Use `qmd` to index local files and search them. Indexing - Add collection: `qmd collection add /path --name docs --mask "**/*.md"` - Update index: `qmd update` - Status: `qmd status` Search - BM25: `qmd search "query"` - Vector: `qmd vsearch "query"` - Hybrid: `qmd query "query"` - Get doc: `qmd get docs/path.md:10 -l 40` Notes - Embeddings/rerank use Ollama at `OLLAMA_URL` (default `http://localhost:11434`). - Index lives under `~/.cache/qmd` by default. - MCP mode: `qmd mcp`.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: zhj7860/ppt (version 1.0.0)

--- name: ppt-generator description: 将用户讲稿一键生成乔布斯风极简科技感竖屏HTML演示稿。当用户需要生成PPT、演示文稿、Slides、幻灯片，或要求科技风/极简风/乔布斯风格的演示时触发此技能。输出为单个可直接运行的HTML文件。 --- # PPT Generator 将讲稿转换为乔布斯风极简科技感竖屏HTML演示稿。 ## 设计哲学 - **极简主义** - 一屏只讲一件事 - **强视觉对比** - 深色背景 + 白色文字 - **高留白** - 禁止密集排版 - **强节奏感** - 让观众想继续看 ## 生成流程（必须严格遵循） ### Step 1: 读取讲稿 读取用户原始讲稿，不修改原稿内容。 ### Step 2: 生成提炼版讲稿 将内容精简、增强冲击力、适配演示场景，输出 Markdown 格式。 ### Step 3: 生成乔布斯风标题 为每个章节生成标题，必须满足： - ≤12 字 - 采用以下形式之一：对比式、问题式、断言式、数字式、比喻式 - 自检：是否让人想继续听？ ### Step 4: 设计幻灯片结构 规划页面顺序和类型，参考 [references/slide-types.md](references/slide-types.md)。 ### Step 5: 生成HTML 使用 [assets/template.html](assets/template.html) 作为基础模板生成完整HTML。 ### Step 6: 填充内容 添加动态背景光斑、交互逻辑、平滑切换动画。 ## 输出顺序（必须依次输出） 1. **提炼后的讲稿**（Markdown） 2. **幻灯片结构大纲** 3. **完整HTML代码** ## 视觉规范速查 | 项目 | 规范 | |------|------| | 比例 | 9:16 竖屏 | | 背景 | #000000 或 #0a0a0a + 模糊光斑动画 | | 主文字 | #ffffff | | 辅助文字 | #9ca3af | | 中文字体 | HarmonyOS Sans SC / 思源黑体 | | 英文字体 | Inter / Roboto | | 标题字重 | font-black / font-bold | | 正文字重 | font-light / font-normal | 详细规范见 [references/design-spec.md](references/design-spec.md)。 ## 交互要求 - 键盘 ← → 翻页 - 底部进度导航条 - 平滑切换动画 ## 技术栈 - TailwindCSS（国内CDN） - 复杂页面使用 Vue3（CDN） - 单个HTML文件，可直接打开运行 ## 严禁行为 - 堆字 / 密集排版 - 花哨配色 - 复杂图表 - 横屏比例 - 偏离极简科技风 ## 默认规则 - 未指定页数：自动生成 8~20 页 - 未指定风格：默认乔布斯风


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