OpenClaw/clawhub-security-signals huggingface dataset:
Skill: ivangdavila/career (version 1.0.2)

--- name: Career version: 1.0.2 description: "Learns your career context. Guides decisions on paths, transitions, negotiations, and growth based on what it discovers about you." changelog: "Restructured with auxiliary files for specific scenarios" --- ## Quick Reference | Scenario | File | Key Trap | |----------|------|----------| | Big career decisions | `decisions.md` | Answering without context | | Changing roles/industries | `transitions.md` | "Grass is greener" | | Salary & offer talks | `negotiations.md` | Accepting first offer | | Promotions & development | `growth.md` | Visibility blindness | | Building connections | `networking.md` | Transactional mindset | ## Adaptive Career Profile This skill evolves. Observe what matters, detect patterns, personalize guidance. **User profile in `~/career/memory.md`.** Create on first use: ```markdown ## Values <!-- Format: "value (level)" — e.g., autonomy over salary (confirmed) --> ## Strengths <!-- Format: "skill: level (source)" — e.g., writing: strong (observed) --> ## Constraints <!-- Format: "constraint (confirmed)" — e.g., location: Madrid only --> ## Context <!-- Format: "fact (stated)" — e.g., senior dev 4 years --> ## Goals <!-- Format: "goal (level)" — e.g., wants to lead team (exploring) --> ``` *Levels: stated → pattern → confirmed. Only store after explicit yes.* ## Core Loop 1. **Discover** — Extract career context from any question 2. **Pattern** — After 2+ signals, propose confirmation 3. **Store** — Only confirmed insights go to memory.md 4. **Apply** — Use profile to personalize all guidance ## Universal Traps - **Sunk cost** — "I've invested 5 years" ≠ reason to stay - **False binary** — "Stay or quit" ignores negotiate, pivot, transition - **Projection** — Their values, not yours or society's - **Timing blindness** — Market conditions matter; some moves have windows - **Prestige capture** — Optimizing for status vs actual satisfaction


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

--- name: pipeworx-citybikes description: Real-time bike-sharing station data for 600+ networks worldwide — Citi Bike, Velib, Nextbike, and more version: 1.0.0 metadata: openclaw: requires: bins: - curl emoji: "🚲" homepage: https://pipeworx.io/packs/citybikes --- # CityBikes Real-time availability data for bike-sharing networks around the world. Covers 600+ systems including Citi Bike (NYC), Velib (Paris), Nextbike (Berlin), and many more. Check station availability, search by city, or list every network on the planet. ## Tools | Tool | What it does | |------|-------------| | `list_networks` | Every bike-sharing network worldwide with city, country, and company info | | `get_network` | All stations for a specific network with real-time bike/slot availability | | `search_networks` | Find networks by city or country name (e.g., "New York", "France") | ## Ideal for - Travel apps showing nearest available bikes in a city - Urban mobility dashboards comparing bike-share systems - Answering "are there bikes available near me?" when given a city - Research on micro-mobility infrastructure across countries ## Example: Citi Bike NYC station availability ```bash curl -s -X POST https://gateway.pipeworx.io/citybikes/mcp \ -H "Content-Type: application/json" \ -d '{"jsonrpc":"2.0","id":1,"method":"tools/call","params":{"name":"get_network","arguments":{"id":"citi-bike-nyc"}}}' ``` Returns every station with: name, latitude, longitude, free bikes, empty slots, and timestamp. ## Common network IDs | City | Network ID | |------|-----------| | New York | `citi-bike-nyc` | | Paris | `velib` | | London | `santander-cycles` | | Berlin | `nextbike-berlin` | | Chicago | `divvy` | ## Setup ```json { "mcpServers": { "pipeworx-citybikes": { "command": "npx", "args": ["-y", "mcp-remote@latest", "https://gateway.pipeworx.io/citybikes/mcp"] } } } ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ckchzh/weathernow (version 2.0.2)

--- name: "weathernow" version: "2.0.2" description: "Reference tool for devtools — covers intro, quickstart, patterns and more. Quick lookup for Weathernow concepts, best practices, and implementation patterns." author: "BytesAgain" homepage: "https://bytesagain.com" source: "https://github.com/bytesagain/ai-skills" tags: [weathernow, reference] category: "devtools" --- # Weathernow Reference tool for devtools — covers intro, quickstart, patterns and more. Quick lookup for Weathernow concepts, best practices, and implementation patterns. No API keys or credentials required. ## Commands | Command | Description | |---------|-------------| | `intro` | intro reference | | `quickstart` | quickstart reference | | `patterns` | patterns reference | | `debugging` | debugging reference | | `performance` | performance reference | | `security` | security reference | | `migration` | migration reference | | `cheatsheet` | cheatsheet reference | ## Output Format All commands output plain-text reference documentation via heredoc. No external API calls, no credentials needed, no network access. --- *Powered by BytesAgain | bytesagain.com | [REDACTED_SECRET]*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: leooooooow/ecommerce-landing-page-conversion-audit (version 1.0.1)

--- name: ecommerce-landing-page-conversion-audit description: Audit an ecommerce landing page to identify the biggest conversion blockers across message clarity, proof, offer structure, friction, and CTA flow. Use when teams need a practical CRO review that leads to clear fixes. --- # Ecommerce Landing Page Conversion Audit A structured audit for ecommerce landing pages that need clearer messaging, stronger proof, lower friction, and better conversion flow. ## Browser-first guidance If a live page URL is available, prefer **OpenClaw managed browser** before generating the audit. Recommended order: 1. If the user already has page copy pasted, use it as a starting point. 2. If the live page is accessible, inspect it with **OpenClaw managed browser**. 3. Use Browser Relay only when the user explicitly wants to inspect their currently attached Chrome tab. ## Solves Many landing pages underperform because: - the headline is vague or generic; - the offer is not easy to understand; - proof is weak or badly placed; - the CTA appears before trust is built; - too many sections create friction instead of momentum. Goal: **Find the biggest conversion blockers on the page and turn them into a practical fix list.** ## Use when - A landing page is getting traffic but not converting well - The page feels busy or unclear - The offer may be good, but the page does not explain it well - Teams want a structured CRO review before scaling spend ## Inputs - Landing page URL or page copy - Target audience - Offer details - Traffic source or intent - Known constraints or compliance boundaries ## Workflow 1. Clarify the conversion goal. - purchase? - lead? - quiz start? - add to cart? 2. Review headline, subhead, and above-the-fold clarity. 3. Check page structure, proof placement, and CTA flow. 4. Identify friction points, confusion points, and weak sections. 5. Prioritize fixes based on likely conversion impact. 6. Suggest improved wording or structural changes where useful. ## Output 1. Conversion blockers 2. Priority-ranked fixes 3. Copy / structure recommendations 4. CTA and proof notes 5. Quick-win suggestions ## Quality bar - Focus on commercial clarity, not generic design commentary. - Recommendations should be specific enough to implement. - Separate major blockers from minor polish issues. - Keep the audit grounded in ecommerce conversion logic. - If the observed page surface is partial, label that clearly. ## Resource See `references/output-template.md`.

path: references/output-template.md
content: # Output Template

## 1) AI-tone issues
- 

## 2) Rewritten copy (main)

## 3) Alternative version

## 4) Risk-safe wording swaps
- 


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: nikhonit/zillow-zestimate (version 1.0.1)

--- name: zillow-zestimate version: 1.0.1 description: Zillow Zestimate (property valuation) and rent Zestimate lookups via Zillapi.com. One tool, minimum surface area. license: MIT-0 author: Zillapi homepage: https://zillapi.com repository: https://github.com/nikhonit/zillow-skills tags: - zillow - zestimate - real-estate - valuation - api - mcp metadata: openclaw: primaryEnv: ZILLAPI_KEY homepage: https://zillapi.com requires: env: - ZILLAPI_KEY --- # zillow-zestimate Focused valuation skill. Use only when the user **explicitly asks** for a property value — the Zestimate, rent Zestimate, tax-assessed value, or last sale price. ## When to use this skill **DO use when the user asks:** - "What's the Zestimate on 123 Main St?" - "How much is my house worth?" - "What's the rental Zestimate?" - "What did this house last sell for?" - "What's the tax-assessed value?" **Do NOT use when:** - An address appears incidentally in context (email signatures, unrelated documents) - The user mentions a property without asking for its value - The user has not signaled they want a valuation lookup If you also need photos, schools, agent contact, or price history, use [`zillow-full`](https://github.com/nikhonit/zillow-skills/tree/main/skills/zillow-full) instead — it bundles everything in one install. ## Tools ### `get_zestimate` — 1 credit Returns: ```json { "data": { "zestimate": 489500, "rent_zestimate": 2800, "tax_assessed_value": 410000, "last_sold_price": 375000, "currency": "USD" } } ``` Pass either `zpid` (preferred — cheaper) or `address`. If only `address` is given, the handler resolves the zpid first. ## Authentication Set `ZILLAPI_KEY` to your Zillapi API key (format `zk_...`). Free key with 100 credits at <https://zillapi.com/signup> — no card. ## Pricing | Plan | Price | Credits | Rate limit | Top-ups | |---|---|---|---|---| | Free | $0 | 100 (one-time) | 20/min | not available | | Monthly | $5/mo | 1,000/month | 200/min | $4 per 1,000 | | Annual | $54/yr | 12,000 upfront | 300/min | $3 per 1,000 | ## Trademark Zillapi is an independent service and is not affiliated with, endorsed by, or sponsored by Zillow Group, Inc. "Zillow" and "Zestimate" are registered trademarks of Zillow Group, Inc.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: builtbyecho/repo-agent-brief (version 0.4.1)

--- name: repo-agent-brief description: Generate concise, safety-aware repository orientation briefs with @builtbyecho/repo-agent-brief/agent-brief before coding-agent work, reviews, handoffs, PR analysis, unfamiliar repo edits, diff-aware branch handoffs, or when an agent needs stack/commands/context/risk signals before changing files. --- # Repo Agent Brief Skill Use `@builtbyecho/repo-agent-brief` to orient an agent before it edits or reviews a repository. It finds high-signal context files, infers stack/commands, builds a compact repo map, and flags obvious secret/risky-instruction patterns. ## Default workflow From the repository root: ```bash npx @builtbyecho/repo-agent-brief . > AGENT_BRIEF.md sed -n '1,220p' AGENT_BRIEF.md ``` For in-progress branches: ```bash npx @builtbyecho/repo-agent-brief . --diff origin/main > AGENT_HANDOFF.md sed -n '1,260p' AGENT_HANDOFF.md ``` For machine-readable automation: ```bash npx @builtbyecho/repo-agent-brief . --format json > agent-brief.json ``` For durable handoffs: ```bash npx @builtbyecho/repo-agent-brief . --diff HEAD --bundle sed -n '1,220p' .agent-brief/brief.md sed -n '1,160p' .agent-brief/verification.md ``` ## When to use - First pass in an unfamiliar repo. - Before delegating to a coding agent. - PR/branch handoff where changed files matter. - Safety preflight before touching CI, migrations, deploy scripts, auth, or config. ## Safety - This is not a full secret scanner. Use Gitleaks/TruffleHog for full audits. - If high-risk patterns are found, inspect before proceeding. - Use `--fail-on-high-risk` in CI or strict agent workflows. - Generated briefs may include snippets from repo context files; avoid posting publicly without review. ## Useful commands ```bash npx @builtbyecho/repo-agent-brief . npx @builtbyecho/repo-agent-brief . --diff HEAD npx @builtbyecho/repo-agent-brief . --diff HEAD --bundle npx @builtbyecho/repo-agent-brief . --diff origin/main --fail-on-high-risk npx @builtbyecho/repo-agent-brief . --no-snippets ```


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

--- name: voice-transcriber-toolkit description: Voice-to-Text Transcription Toolkit - 语音识别转文字，支持Whisper/Vosk引擎，批量处理，字幕导出 | Speech recognition & transcription with Whisper/Vosk engines, batch processing, subtitle export metadata: openclaw: requires: bins: ["python3", "ffmpeg"] install: - id: python-deps kind: python requirements: "requirements.txt" - id: ffmpeg kind: apt packages: ["ffmpeg"] --- # Voice Transcriber Toolkit ## 功能 - **Transcribe** — 单文件/批量音频转文字 (Whisper/Vosk) - **Convert** — 音频格式转换 (ffmpeg) - **Export** — 导出 SRT/VTT 字幕 - **Info** — 音频文件元信息提取 ## 使用 ```python from scripts.voice_transcriber import VoiceTranscriber, AudioConverter transcriber = VoiceTranscriber(engine="whisper", model_size="base") # 单文件转录 result = transcriber.transcribe("meeting.mp3", language="zh") print(result["text"]) # 批量转录 results = transcriber.transcribe_batch(["file1.mp3", "file2.wav"]) # 导出字幕 srt = transcriber.export_subtitles(result, "srt") # 音频转换 converter = AudioConverter() converter.convert_to_wav("input.m4a", "output.wav") ``` ## CLI ```bash python3 scripts/voice_transcriber.py ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: zhuchenggong19851114-design/zhuchenggong-xiaohongshu-post (version 1.0.0)

--- name: xiaohongshu-post description: 小红书发布技能（文字配图版）- 运营执行专员专用 --- # 小红书发布技能（文字配图版） ## 完整流程 一、打开发布页 二、选择「上传图文」 三、★ 点击「文字配图」按钮（不要点上传图片） 四、★ 粘贴文案到输入框 五、★ 点击「生成图片」按钮提交（不是回车） 六、等待图片生成完成 七、截图发给用户，**选择卡片风格**（基础/插图/备忘/边框/便签/简约/涂写/弥散/光影/科技） 八、用户选择后，点击「下一步」 九、填写标题（可选） 十、截图发给用户预览确认 十一、客户确认后，**问客户：发布还是暂存** 十二、客户选择后，点击「发布」或「暂存离开」 ## 关键规则 1. ★ 顺序不能错：上传图文 → 文字配图 → 粘贴文案 → 生成图片 2. 必须点击「文字配图」按钮，不是上传图片 3. 输入文案后要点「生成图片」按钮，不是回车 4. 等待图片完全生成后再截图 5. ★ 用户选择卡片风格（不是A/B/C/D，是基础/插图/备忘/边框/便签/简约/涂写/弥散/光影/科技） 6. 用户选择卡片风格后要点下一步 7. 发布前必须用户预览确认 8. 客户说可以才能点发布 ## 适用场景 - 用户选择「文字配图」发布 - 用户想要AI自动生成配图


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: mjrussell/fitbit (version 0.1.0)

--- name: fitbit description: Query Fitbit health data including sleep, heart rate, activity, SpO2, and breathing rate. Use when user asks about their fitness, sleep quality, steps, or health metrics. homepage: https://www.fitbit.com metadata: clawdbot: emoji: "💪" requires: bins: ["fitbit-cli"] --- # Fitbit CLI Query health and fitness data from Fitbit wearables. ## Commands ### Health Data ```bash # Sleep logs (deep, light, REM, awake times) fitbit-cli -s # today fitbit-cli -s yesterday # yesterday fitbit-cli -s last-week # last 7 days fitbit-cli -s 2026-01-01 # specific date # Heart rate time series fitbit-cli -e # today fitbit-cli -e last-week # last 7 days # Blood oxygen (SpO2) fitbit-cli -o # today fitbit-cli -o last-3-days # last 3 days # Active Zone Minutes fitbit-cli -a # today fitbit-cli -a last-month # last month # Breathing rate fitbit-cli -b # today # Daily activity (steps, calories, distance, floors) fitbit-cli -t # today fitbit-cli -t yesterday # yesterday ``` ### Account & Devices ```bash # User profile fitbit-cli -u # Connected devices (battery, sync status) fitbit-cli -d ``` ### Date Formats - No parameter: today - Specific date: `2026-01-05` - Date range: `2026-01-01,2026-01-05` - Relative: `yesterday`, `last-week`, `last-month` - Custom relative: `last-2-days`, `last-3-weeks`, `last-2-months` ## Usage Examples **User asks "How did I sleep last night?"** ```bash fitbit-cli -s yesterday ``` **User asks "What's my heart rate been like this week?"** ```bash fitbit-cli -e last-week ``` **User asks "How many steps today?"** ```bash fitbit-cli -t ``` **User asks "Show my SpO2 levels"** ```bash fitbit-cli -o ``` **User asks "Is my Fitbit synced?"** ```bash fitbit-cli -d ``` **User asks "How active was I last month?"** ```bash fitbit-cli -a last-month ``` ## Notes - Read-only access to Fitbit data - Tokens auto-refresh (expire after 8 hours) - Data may be delayed from device sync - First-time setup: `fitbit-cli --init-auth`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: johnnywang2001/csv-toolkit (version 1.0.0)

--- name: csv-toolkit description: Manipulate CSV files from the command line — view, filter, sort, select columns, convert CSV to/from JSON, compute statistics, deduplicate, and merge files. Use when the user needs to work with CSV data, inspect a spreadsheet export, clean up tabular data, convert formats, or get quick column stats. Zero external dependencies. --- # CSV Toolkit Command-line CSV manipulation — view, filter, sort, select, convert, stats, dedup, and merge. Pure Python, no dependencies. ## Quick Start ```bash # View as formatted table python3 scripts/csv_toolkit.py view data.csv python3 scripts/csv_toolkit.py view data.csv --head 10 # Filter rows python3 scripts/csv_toolkit.py filter data.csv -w 'age>30' 'city==Chicago' # Sort python3 scripts/csv_toolkit.py sort data.csv --by salary --desc # Select columns python3 scripts/csv_toolkit.py select data.csv -c "name,email,salary" # Convert to JSON python3 scripts/csv_toolkit.py to-json data.csv -o data.json # Convert JSON back to CSV python3 scripts/csv_toolkit.py from-json data.json -o data.csv # Column statistics python3 scripts/csv_toolkit.py stats data.csv -c "age,salary" # Remove duplicates python3 scripts/csv_toolkit.py dedup data.csv -o clean.csv # Merge files python3 scripts/csv_toolkit.py merge file1.csv file2.csv -o combined.csv ``` ## Commands | Command | Description | |---------|-------------| | `view` | Display CSV as a formatted table. Use `--head N` / `--tail N` to limit rows. | | `filter` | Filter rows with `-w` conditions: `col>val`, `col==val`, `col!=val`, `col<=val`. Multiple conditions = AND. | | `sort` | Sort by column with `--by col`. Add `--desc` for descending. Numeric-aware. | | `select` | Pick columns with `-c "col1,col2"`. | | `to-json` | Convert CSV to JSON array of objects. Auto-detects numbers. | | `from-json` | Convert a JSON array of objects to CSV. | | `stats` | Column statistics: count, unique values, min/max/mean/median for numeric columns. | | `dedup` | Remove duplicate rows. | | `merge` | Concatenate multiple CSV files (same headers). | ## Global Options | Flag | Description | |------|-------------| | `-d, --delimiter` | Field delimiter (default: `,`). Use `-d '\t'` for TSV. | | `--encoding` | File encoding (default: `utf-8`). | | `-o, --output` | Write result to file instead of stdout. |


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: dadaniya99/zenmux-image-generation (version 1.5.0)

--- name: zenmux-image-generation description: Generate images via ZenMux API (Pro/Elite). Supports Text-to-Image, Image-to-Image, and Multi-Image reference fusion. --- # ZenMux Image Generation Skill (Gemini 3 Pro) This skill uses the ZenMux API to generate high-fidelity images using the **Gemini 3 Pro Image (Nano Banana Pro)** model. ## Features * **Text-to-Image**: Generate visuals from descriptive prompts. * **Image-to-Image**: Modify an existing image based on a prompt. * **Multi-Image Fusion**: Combine elements or styles from multiple reference images (e.g., character from one image, costume/style from another). ## Usage To generate an image, execute the `scripts/generate.py` script. **IMPORTANT:** * You must set the `ZENMUX_API_KEY` environment variable. * **Model**: Defaults to [REDACTED_SECRET]. * **Provider**: ZenMux (Requires **Pro** or higher plan). ### 1. Text-to-Image ```bash ZENMUX_API_KEY="YOUR_KEY" python3 scripts/generate.py --prompt "a cybernetic lobster in space" ``` ### 2. Image-to-Image ```bash ZENMUX_API_KEY="YOUR_KEY" python3 scripts/generate.py --prompt "make it winter" --images "summer.png" ``` ### 3. Multi-Image Fusion (Advanced) ```bash ZENMUX_API_KEY="YOUR_KEY" python3 scripts/generate.py --prompt "put this child in this costume" --images "child.png" "costume.jpg" ``` ### Arguments * `--prompt` (required): Text description of the task. * `--images` (optional): Path(s) to one or more reference image files. * `--model` (optional): Defaults to [REDACTED_SECRET]. * `--output` (optional): Defaults to `generated_image.png`.

path: package.json
content: name: zenmux-image-generation
version: 1.0.0

Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: tobewin/china-poster-studio (version 1.1.1)

--- name: china-poster-studio description: 社交海报生成器。一键将文字/文章生成精美海报，支持朋友圈、小红书、抖音等平台。粘贴内容即可生成，无需设计技能。适合运营人员、自媒体、普通用户。 version: 1.1.0 license: MIT-0 metadata: {"openclaw": {"emoji": "🎨", "requires": {"bins": ["python3"], "env": []}}} --- # 社交海报生成器 一键将文字、文章、感悟生成精美海报，适合发朋友圈、小红书、抖音。 ## 功能特点 - 🎨 **一键生成**：粘贴内容即可生成精美海报 - 📱 **多平台适配**：朋友圈(1:1)、小红书(3:4)、抖音(9:16) - 🖼️ **精美背景**：内置10+高质量背景模板 - ✍️ **智能排版**：自动提取标题、要点、金句 - 🌏 **中文优化**：专为中文内容优化 - ⚡ **秒级生成**：无需等待，即刻出图 ## 适用场景 | 场景 | 示例 | |------|------| | 朋友圈分享 | 新闻观点、读书笔记、生活感悟 | | 小红书笔记 | 干货总结、产品推荐、经验分享 | | 抖音素材 | 知识卡片、观点输出 | | 公众号配图 | 文章摘要、金句海报 | ## 使用方式 ### 方式1：粘贴内容（推荐，最稳定） ``` 用户：[粘贴一篇文章或一段话] Agent：自动生成海报 ``` ### 方式2：简单描述 ``` 用户：帮我做一个关于"坚持学习"的励志海报 Agent：AI生成内容 + 生成海报 ``` ### 方式3：提供URL（可选） ``` 用户：https://mp.weixin.qq.com/s/xxx Agent：尝试抓取 → 生成海报 ``` --- ## 生成流程 ``` 用户输入 ↓ 识别输入类型（URL/长文本/短描述） ↓ 获取内容 ├─ URL → 尝试抓取（可能失败） ├─ 长文本 → 直接使用 └─ 短描述 → AI扩展生成 ↓ AI智能处理 ├─ 提取标题 ├─ 提炼3个核心要点 ├─ 提取金句 └─ 选择背景风格 ↓ 生成海报 ├─ 加载预制背景图 ├─ Pillow精准压字 └─ 导出PNG图片 ↓ 输出成品海报 ``` --- ## 背景模板 ### 内置模板 | 名称 | 风格 | 适用场景 | |------|------|----------| | 商务蓝 | 深蓝渐变 | 职场、商业 | | 极简白 | 纯白底 | 清爽、通用 | | 深色模式 | 深灰/黑 | 科技、高端 | | 暖色橙 | 橙色渐变 | 活力、生活 | | 渐变紫 | 紫色渐变 | 时尚、文艺 | | 清爽灰 | 浅灰底 | 简约、通用 | | 墨绿色 | 深绿渐变 | 自然、健康 | | 酒红色 | 深红渐变 | 正式、庄重 | ### 背景选择 ``` 用户可以指定： ├─ "用蓝色背景" ├─ "深色模式" ├─ "简洁白色" └─ 不指定 → AI根据内容自动选择 ``` --- ## 海报排版 ### 标准布局（1:1 朋友圈） ``` ┌─────────────────────────┐ │ │ │ [背景图/渐变] │ │ │ │ ┌───────────────────┐ │ │ │ │ │ │ │ 标题（大字） │ │ │ │ 金句/观点 │ │ │ │ │ │ │ └───────────────────┘ │ │ │ │ · 要点1 │ │ · 要点2 │ │ · 要点3 │ │ │ │ ─────────────────── │ │ 来源：xxx | 日期 │ └─────────────────────────┘ ``` ### 竖版布局（3:4 小红书） ``` ┌────────────────┐ │ │ │ [背景图] │ │ │ │ ┌──────────┐ │ │ │ 标题 │ │ │ │ 金句 │ │ │ └──────────┘ │ │ │ │ · 要点1 │ │ · 要点2 │ │ · 要点3 │ │ │ │ 来源 | 日期 │ └────────────────┘ ``` --- ## 使用示例 ### 示例1：新闻观点海报 ``` 用户：[粘贴一篇关于AI的新闻] 输出： ┌─────────────────────────┐ │ [深蓝色科技背景] │ │ │ │ AI正在改变世界 │ │ ───────────────── │ │ "未来已来，只是分布 │ │ 不均匀" - 威廉·吉布森 │ │ │ │ · 大模型能力持续突破 │ │ · Agent技术走向成熟 │ │ · 开源生态蓬勃发展 │ │ │ │ AI研究院 | 2026.03.25 │ └─────────────────────────┘ ``` ### 示例2：读书笔记海报 ``` 用户：读完《原则》，印象最深的是"痛苦+反思=进步" 输出： ┌─────────────────────────┐ │ [简洁白色背景] │ │ │ │ 读书笔记 │ │ 《原则》 │ │ ───────────────── │ │ "痛苦+反思=进步" │ │ │ │ · 拥抱现实 │ │ · 五步流程法 │ │ · 极度透明 │ │ │ │ 读书笔记 | 2026.03.25 │ └─────────────────────────┘ ``` --- ## Python代码示例 如需本地生成海报，请使用 Pillow 图像处理库。 **基本用法：** 1. 创建画布：设置尺寸（1080x1080 等） 2. 加载背景：使用预制模板或渐变色 3. 添加文字：标题、要点、金句 4. 导出 PNG **常用尺寸：** - 朋友圈：1080 x 1080 (1:1) - 小红书：1080 x 1440 (3:4) - 抖音：1080 x 1920 (9:16) **字体路径（中文）：** - macOS: `/System/Library/Fonts/PingFang.ttc` - Linux: `/usr/share/fonts/truetype/noto/NotoSansCJK-Bold.ttc` - Windows: `C:/Windows/Fonts/msyh.ttc` --- ## 注意事项 - 首次使用需要下载字体（约20MB） - 内置背景图为预制模板 - 文字精准，无错别字 - 支持中文排版 - **依赖**：本地海报生成需要 Pillow 库，用户需自行安装


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