OpenClaw/clawhub-security-signals huggingface dataset:
Skill: kleberbaum/pandora (version 0.1.0)

--- name: pandora description: Pandora namespace for Netsnek e.U. secrets and configuration management vault. Securely stores API keys, database credentials, and environment configs with versioning and access control. user-invocable: true version: 0.1.0 metadata: openclaw: os: [linux] permissions: [exec] --- # Pandora ## Guard Your Secrets Pandora is a secrets vault for applications and teams. It keeps API keys, passwords, and sensitive configuration away from code and config files—encrypted at rest and in transit. Use Pandora when managing credentials, rotating secrets, or enforcing least-privilege access. ## Vault Architecture - **Store** — Encrypt and persist secrets with metadata - **Rotate** — Schedule or trigger secret rotation - **List** — Enumerate secrets (values never exposed in listings) ## Operations Guide ```bash # Store a new secret ./scripts/vault-ops.sh --store --key "db_password" --value "secret" # Rotate an existing secret ./scripts/vault-ops.sh --rotate --key "api_token" # List all secret keys (no values) ./scripts/vault-ops.sh --list-secrets ``` ### Arguments | Argument | Purpose | |------------------|--------------------------------------| | `--store` | Insert or update a secret | | `--rotate` | Rotate the secret for the given key | | `--list-secrets` | List secret keys (not values) | ## Security Walkthrough 1. **Store**: `vault-ops.sh --store --key prod_db_pw` — Prompts for value or reads from stdin. 2. **List**: `vault-ops.sh --list-secrets` — Shows keys only; never outputs values. 3. **Rotate**: `vault-ops.sh --rotate --key prod_db_pw` — Generates new secret, updates vault, returns new value for app config.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: rockbotclub/wechat-pack (version 1.0.0)

--- name: wechat-pack description: Convert local .docx or Markdown files into WeChat-ready HTML and generate a publish folder (source/assets/cover/wechat). Use when packaging documents for WeChat public account publishing with local assets and copy-paste HTML output. 当用户要求打包微信文章、转换文章为微信格式、微信公众号排版、生成微信推文 HTML、或将 Markdown/Word 转成微信可粘贴的格式时触发。 --- # WeChat Pack ## Quick start Run from the skill root: ```bash ./wechat_pack path/to/article.docx ./wechat_pack path/to/article.md ``` Outputs a publish folder with: - `source/` original input file - `assets/` downloaded or extracted images - `cover/` optional cover image - `wechat/` `article.html` ready to paste into WeChat editor - `meta.json` metadata (title, paths, asset count, cover variants) ## Supported inputs - `.docx` (requires `pandoc` installed) - `.md` / `.markdown` - `.html` / `.htm` (treated as already-converted HTML) ## Recommended workflow 1. Export the document from your editor to `.docx` or `.md`. 2. Run `./wechat_pack <file>`. 3. Open `wechat/article.html` and paste into the WeChat editor. ## Options - `--out <dir>`: output directory (default: `<basename>-wechat`) - `--title <title>`: insert a top-level title if missing - `--cover <path-or-url>`: download or copy a cover image into `cover/` ## Notes - For `.docx` conversion, `pandoc` must be available in `PATH`. - The script downloads external images and rewrites `<img>` to local `assets/` paths. - Output HTML is styled with inline CSS to be friendly to the WeChat editor. - If Pillow is available, cover variants are generated in `cover/`: `cover-wide-2.35x1.jpg` and `cover-square-1x1.jpg`.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (CRITICAL)
Skill: haohanyang92/a-stock-leader-identification (version 1.0.0)

--- name: leader-stock-identification description: 快速找到板块内的"真龙"，识别龙头股。用于A股短线交易选股。 --- # 龙头股识别 Skill ## 核心能力 快速找到板块内的"真龙"，而不是跟风杂毛。 ## 龙头股识别规则 ### 启动信号 - 板块内第一个涨停 - 封单量占流通盘比例 > 1% ### 强度验证 - 连续2日涨停 - 换手率保持在 10%-20% - 过高易出货 - 过低易炸板 ### 辨识度 - 叠加多重题材（如"人工智能+算力"） - 或为行业龙头公司 ## 实战流程 1. 板块爆发时，先找第一个涨停的标的 2. 看封单强度和题材正宗度 3. 锁定龙头后直接跟进 4. 避免在跟风股上浪费时间 ## 排除杂毛的特征 - 跟风涨停，非板块引领者 - 封单量小，易被砸 - 题材不正宗，纯炒概念 - 换手率异常（过高或过低） ## 触发场景 - "谁是AI板块龙头" - "这个板块谁是真龙" - "能追吗还是买跟风股"


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: kakazhang50/tpt-generate-cover (version 1.0.0)

--- name: tpt-generate-cover version: 0.1.0 description: Generate a TPT cover using the AI Studio DinoCover tool and save it locally with the book title as filename. tags: [tpt, cover, gemini, browser, automation] --- # TPT Generate Cover (AI Studio DinoCover Tool) ## Purpose Automatically generate a Teachers Pay Teachers (TPT) cover using the custom AI Studio application "DinoCover Gen" and save the generated image to the local output folder. This skill uses browser profile `geminibuild`, which must already have a persisted Google login session. --- ## Tool URL https://aistudio.google.com/apps/drive/1dsPpIhA2HzaacwO16jxddnerC-B64MuC?showPreview=true&showAssistant=true&fullscreenApplet=true --- ## Preconditions - Browser profile `geminibuild` exists. - Google account is already logged in. - Input folders exist: - Reference covers: E:\AI\openclaw-tpt\input\cover\ - Story TXT files: E:\AI\openclaw-tpt\input\TXT\ - Output folder exists: E:\AI\openclaw-tpt\output\COVER\ This skill must not attempt Google account login or MFA handling. --- ## Inputs - Latest TXT file inside: E:\AI\openclaw-tpt\input\TXT\ TXT structure assumed: - First line = TITLE - Remaining content = STORY CONTEXT - Reference images: All PNG files inside: E:\AI\openclaw-tpt\input\cover\ --- ## Outputs - Generated PNG image - Saved to: E:\AI\openclaw-tpt\output\COVER\ - Filename: <TITLE>.png --- ## Procedure 1. Launch browser using profile `geminibuild`. 2. Open the DinoCover tool URL. 3. Wait until UI fully loads. 4. Upload all reference images from: E:\AI\openclaw-tpt\input\cover\ 5. Read latest TXT file from: E:\AI\openclaw-tpt\input\TXT\ - Extract TITLE (first line) - Extract STORY CONTEXT (remaining content) 6. Fill the TITLE field in the tool. 7. Fill STORY CONTEXT field. 8. Click "Generate Cover". 9. Wait for generated image preview to appear. 10. Download generated image. 11. Save image to: E:\AI\openclaw-tpt\output\COVER\ Filename: <TITLE>.png --- ## Success Criteria - Generated image is visible. - Image downloaded successfully. - File saved under correct filename. - File exists in output folder. --- ## Stop / Hand-off Conditions (Human Required) - Google login page appears. - MFA / verification prompt appears. - Captcha appears. - Generate button fails repeatedly. - No preview appears after reasonable wait. --- ## Safety Boundaries - Do not change Google account settings. - Do not modify Drive files. - Do not delete reference images. - Only upload, generate, download. --- # 中文说明（人工校验用） 目标： 用 geminibuild profile 打开 DinoCover 工具，上传参考封面，读取 TXT 标题与故事，生成封面，并保存到 output/COVER。 停止条件： 出现登录验证、人机验证、异常页面，必须停止。 文件命名规则： 标题.png


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: jeffaf/breweries (version 1.0.0)

--- name: breweries version: 1.0.0 description: "CLI for AI agents to find breweries for their humans. Uses Open Brewery DB. No auth required." homepage: https://www.openbrewerydb.org metadata: openclaw: emoji: "🍺" requires: bins: ["bash", "curl", "jq"] tags: ["breweries", "beer", "search", "openbrewerydb", "cli"] --- # Brewery Lookup CLI for AI agents to find breweries for their humans. "What breweries are in Portland?" — now your agent can answer. Uses Open Brewery DB. No account or API key needed. ## Usage ``` "Find breweries named Sierra Nevada" "What breweries are in San Diego?" "Show me breweries in Oregon" "Find me a random brewery" "What brewpubs are there?" ``` ## Commands | Action | Command | |--------|---------| | Search by name | `breweries search "name"` | | Find by city | `breweries city "city name"` | | Find by state | `breweries state "state"` | | Find by type | `breweries type <type>` | | Random | `breweries random [count]` | ### Brewery Types - `micro` — Most craft breweries - `nano` — Very small breweries - `regional` — Regional craft breweries - `brewpub` — Brewery with restaurant/bar - `large` — Large national breweries - `planning` — Breweries in planning - `bar` — Bars that brew on premises - `contract` — Contract brewing - `proprietor` — Alternating proprietor - `closed` — Closed breweries ### Examples ```bash breweries search "stone brewing" # Find breweries by name breweries city "portland" # Find breweries in Portland breweries state oregon # Find breweries in Oregon breweries type brewpub # Find all brewpubs breweries random 3 # Get 3 random breweries ``` ## Output ``` 🍺 Sierra Nevada Brewing Co. — Chico, California, Regional Brewery https://sierranevada.com ``` ## Notes - Uses Open Brewery DB API v1 (api.openbrewerydb.org) - No authentication required - No rate limiting documented - Returns up to 10 results per query - State names can be full name or abbreviation --- ## Agent Implementation Notes **Script location:** `{skill_folder}/breweries` (wrapper) → `scripts/breweries` **When user asks about breweries:** 1. Run `./breweries search "name"` to find by name 2. Run `./breweries city "city"` for location-based search 3. Run `./breweries state "state"` for state-wide search 4. Run `./breweries type brewpub` for specific types 5. Run `./breweries random` for discovery/recommendations **Common patterns:** - "Find me a brewery in [city]" → `breweries city "[city]"` - "What breweries are in [state]?" → `breweries state "[state]"` - "Search for [name] brewery" → `breweries search "[name]"` - "Surprise me with a brewery" → `breweries random` - "Where can I get craft beer in [city]?" → `breweries city "[city]"` or `breweries type micro` **Don't use for:** Bars without brewing, liquor stores, wine/spirits.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: alvinecarn/reasoning-answer (version 0.1.0)

--- name: reasoning-answer description: Current Date: $DATE$. Default language: keep the same with user's language, unless the user explicitly specifies a language. If answering based on search results, add a citation mark immediately after the relevant sentence or phrase. 2. The citation mark MUST be a clickable numbered footnote in the format `[[Number]](URL)`,for example [[1]](https://link-to-source-1.com). At the end, there shoul... --- # Reasoning Answer ## Overview This skill provides specialized capabilities for reasoning answer. ## Instructions Current Date: $DATE$. Default language: keep the same with user's language, unless the user explicitly specifies a language. If answering based on search results, add a citation mark immediately after the relevant sentence or phrase. 2. The citation mark MUST be a clickable numbered footnote in the format `[[Number]](URL)`,for example [[1]](https://link-to-source-1.com). At the end, there should be a complete list of references, ensure citation numbers are sequential starting from [1]. ## Usage Notes - This skill is based on the Reasoning_Answer agent configuration - Template variables (if any) like $DATE$, $SESSION_GROUP_ID$ may require runtime substitution - Follow the instructions and guidelines provided in the content above


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: tobeyrebecca/toby-browser-automation (version 1.0.0)

--- name: browser description: Automate web browser interactions using natural language via CLI commands. Use when the user asks to browse websites, navigate web pages, extract data from websites, take screenshots, fill forms, click buttons, or interact with web applications. allowed-tools: Bash requires-env: [SKILLBOSS_API_KEY] --- > 📖 **Complete setup guide**: https://skillboss.co/skill.md # Browser Automation Automate browser interactions using Stagehand CLI with SkillBoss API Hub. ### First: Environment Selection (Local vs Remote) The skill automatically selects between local and remote browser environments: - **If SKILLBOSS_API_KEY exists** (SKILLBOSS_API_KEY in .env file): Uses SkillBoss API Hub remote mode - **If no SKILLBOSS_API_KEY**: Falls back to local Chrome browser - **No user prompting**: The selection happens automatically based on available configuration ## Setup (First Time Only) Check `setup.json` in this directory. If `setupComplete: false`: ```bash npm install # Install dependencies npm link # Create global 'browser' command ``` ## Commands All commands work identically in both modes: ```bash browser navigate <url> # Go to URL browser act "<action>" # Natural language action browser extract "<instruction>" ['{}'] # Extract data (optional schema) browser observe "<query>" # Discover elements browser screenshot # Take screenshot browser close # Close browser ``` ## Quick Example ```bash browser navigate https://example.com browser act "click the Sign In button" browser extract "get the page title" browser close ``` ## Mode Comparison | Feature | Local | SkillBoss API Hub | |---------|-------|-------------| | Speed | Faster | Slightly slower | | Setup | Chrome required | SKILLBOSS_API_KEY required | | Stealth mode | No | Yes | | Proxy/CAPTCHA | No | Yes | | Best for | Development | Production/scraping | ## Best Practices 1. **Always navigate first** before interacting 2. **View screenshots** after each command to verify 3. **Be specific** in action descriptions 4. **Close browser** when done ## Troubleshooting - **Chrome not found**: Install Chrome or use SkillBoss API Hub remote mode - **Action fails**: Use `browser observe` to discover available elements - **Remote mode fails**: Verify SKILLBOSS_API_KEY is set For detailed examples, see [EXAMPLES.md](EXAMPLES.md). For API reference, see [REFERENCE.md](REFERENCE.md).


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (CRITICAL)
Skill: subwukong/feishu-knowledge-query (version 1.0.0)

--- name: feishu-knowledge-query description: 飞书知识库查询工具。当用户需要查询已有知识、确认操作步骤、查找历史记录或参考之前的工作时使用。包括：(1) 搜索知识库文档 (2) 获取具体文档内容 (3) 引用知识库中的操作指南。触发场景：用户说"查一下知识库"、"参考之前"、"知识库里有"、"确认一下之前的操作" --- # 飞书知识库查询 Skill ## 功能说明 本技能用于在执行任务时查询飞书知识库中的已有知识，确保操作一致性和知识复用。 ## 知识库信息 - **知识库名称**: openclaw - **知识库ID**: 7612944775368608721 ## 已收录内容 ### 🛠️ 工具安装 - Agent Reach 安装使用指南 - wechat-article-reader 使用指南 ### 🚀 进阶技巧 - 自我迭代方法 ### 🛡️ 安全配置 - 安全最佳实践 - IronClaw - OpenClaw 安全替代 ### 📖 完整教程 - OpenClaw飞书官方插件安装指南 - OpenClaw+Obsidian内容工厂搭建教程 - Anthropic官方Skill创建完全指南 ## 使用方法 ### 1. 搜索知识库 使用 `feishu_search_doc_wiki` 搜索相关内容： - query: 搜索关键词 - 可按文档类型筛选 ### 2. 获取文档内容 使用 `feishu_fetch_doc` 获取具体文档： - doc_id: 从搜索结果获取 ### 3. 结合知识执行任务 当用户提出需求时： 1. 先搜索知识库相关主题 2. 获取具体操作指南 3. 按照知识库中的最佳实践执行 4. 如需创建新Skill，参考 Skill 创建指南 ## 典型场景 | 用户需求 | 操作步骤 | |---------|---------| | "怎么做内容工厂" | 搜索"内容工厂" → 获取Obsidian搭建教程 | | "如何安装Agent Reach" | 搜索"Agent Reach" → 获取安装指南 | | "创建Skill的步骤" | 搜索"Skill创建" → 获取官方指南 | | "OpenClaw安全吗" | 搜索"IronClaw" → 获取安全替代方案 | ## 注意事项 - 知识库中的内容经过整理，是权威参考 - 执行前先查询，避免重复造轮子 - 如发现知识库内容过时，及时更新


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: xueyetianya/court-prep (version 2.3.5)

--- version: "2.0.0" name: Court Prep Guide description: "Court Preparation Tool. Use when you need court prep capabilities. Triggers on: court prep." 诉讼准备指南。流程指引、文书准备、证据整理、时间线、费用、小额诉讼。Court preparation guide. 诉讼、法院、起诉。 author: BytesAgain homepage: https://bytesagain.com source: https://github.com/bytesagain/ai-skills --- # Court Prep Guide 诉讼准备指南。流程指引、文书准备、证据整理、时间线、费用、小额诉讼。Court preparation guide. 诉讼、法院、起诉。 ## 速查表 See commands above. ## 命令速查 ``` guide guide document document evidence evidence timeline timeline cost cost small-claims small claims ``` > 💡 小技巧：先用 `help` 查看所有命令，再选择最适合的 --- *Court Prep Guide by BytesAgain* --- 💬 Feedback & Feature Requests: https://bytesagain.com/feedback Powered by BytesAgain | bytesagain.com - Run `court-prep help` for all commands ## Commands Run `court-prep help` to see all available commands. ## When to Use - when you need quick court prep from the command line - to automate court tasks in your workflow ## Output Returns summaries to stdout. Redirect to a file with `court-prep run > output.txt`.


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

--- name: Japanese description: Write Japanese that sounds human. Not formal, not robotic, not AI-generated. --- ## The Real Problem AI Japanese is technically correct but sounds off. Too polite. Too textbook. Too stiff. Natives write more casually, with particles, contractions, and personality. Match that. ## Formality Default Default register is too high. Casual Japanese drops formality. Unless explicitly formal: lean casual. です/ます is not always needed. Plain form is normal among peers. ## Politeness Levels Know when to use what: - 丁寧語 (です/ます): strangers, work, safe default - 普通形 (plain): friends, peers, casual internet - タメ口: close friends, very casual - Don't mix levels awkwardly within same conversation ## Sentence-Final Particles Essential for natural Japanese: - ね: seeking agreement, softening - よ: asserting, informing - な/なあ: self-reflection, male casual - わ: soft assertion (traditionally feminine) - ぞ/ぜ: strong assertion, masculine casual - の/んだ: explanatory, seeking/giving context - Missing these = robotic Japanese ## Contractions Casual Japanese contracts: - ている → てる - ておく → とく - てしまう → ちゃう/じゃう - なければ → なきゃ - では → じゃ - のだ → んだ - ら抜き: 食べれる instead of 食べられる (controversial but common) ## Fillers & Flow Real Japanese has fillers: - えーと, あのー, そのー - まあ, なんか, ちょっと - 一応, 的な, みたいな - っていうか, つまり ## Expressiveness Don't pick the safe word: - いい → 最高, やばい, 神 - 悪い → 最悪, ひどい, クソ - すごい → めっちゃ, 超, マジ - 本当 → マジ, ガチ ## Internet/Youth Slang Modern Japanese uses: - w/草: laughter (wwww, 草) - やばい: good or bad depending on context - 神: amazing, god-tier - エモい: emotional, aesthetic - それな: "that's it", agreement - り/りょ: りょうかい abbreviated ## Common Expressions Natural expressions: - 了解/りょ, おk - マジで?, うそ!, え? - なるほど, そうなんだ - 微妙, びみょー - めんどくさい → めんどい ## Reactions React naturally: - えー!, うそ!, マジ? - すご!, やば! - ウケる, 笑える - きも, うざ (negative) - かわいい!, いいね! ## Kanji vs Kana Balance Too much kanji = stiff, formal. Casual writing uses more hiragana: - 有難う → ありがとう - 下さい → ください - 出来る → できる - Match the casual level with kana usage ## Gendered Speech Be aware but don't overdo stereotypes: - Masculine: 俺, だぜ, だな - Feminine: 私/あたし, わ, かしら - Neutral: 僕, plain forms - Modern usage is more flexible ## The "Native Test" Before sending: would a Japanese person screenshot this as "AI-generated"? If yes—too polite, missing particles, too stiff. Casualize.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: xueyetianya/vision (version 3.5.0)

--- name: "Vision — Image Processing, Resize, Convert & Watermark" description: "Resize, crop, convert, and optimize images using ImageMagick. Use when processing photos, converting formats (PNG/WebP), compressing size, or adding watermarks." version: "3.5.0" author: BytesAgain homepage: https://bytesagain.com source: https://github.com/bytesagain/ai-skills tags: ["vision", "image-processing", "resize", "crop", "convert", "optimize", "exif", "watermark", "图像处理"] --- # vision Image processing toolkit powered by ImageMagick. ## Quick Start / 快速开始 Just ask your AI assistant: / 直接告诉 AI 助手： - "Resize photo.jpg to 800px width" (帮我把 photo.jpg 缩小到 800 宽) - "Convert this PNG to WebP format" (把这张 PNG 图片转换成 WebP 格式) - "Add watermark: © 2025 BytesAgain" (给这张图加个水印) ## Commands ### resize Resize an image. ```bash bash scripts/script.sh resize --input photo.jpg --width 800 bash scripts/script.sh resize --input photo.jpg --percent 50 ``` ### convert Change image formats. ```bash bash scripts/script.sh convert --input photo.png --to webp bash scripts/script.sh convert --input photo.jpg --to png ``` ### optimize Compress file size without losing quality. ```bash bash scripts/script.sh optimize --input photo.jpg --quality 80 ``` ### watermark Add text watermark to images. ```bash bash scripts/script.sh watermark --input photo.jpg --text "© 2025" --position southeast ``` ## Requirements - bash 4+ - ImageMagick (convert, identify) ## Feedback https://bytesagain.com/feedback/ Powered by BytesAgain | bytesagain.com


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: ouyang198000/topic-planner-agent (version 1.0.0)

--- name: topic-planner-agent description: 选题策划智能体。当需要生成小说选题、评估市场机会、设计卖点钩子、制定创作约束时触发本技能。用于：(1) 基于市场洞察师的数据生成备选选题 (2) 对选题进行多维度评分和ROI估算 (3) 设计核心卖点、开篇钩子、节奏大纲 (4) 输出标准选题策划文档给小说创作师 (5) 竞品对标分析和差异化定位 --- # 选题策划师 · SKILL.md ## 角色定位 作为总管智能体「摸鱼宇哥」的下游，选题策划师负责将市场洞察师的数据转化为可执行的创作决策。 ## 核心职责 ### 1. 选题评估模型 **评估维度：** | 维度 | 权重 | 说明 | |------|------|------| | 市场热度 | 25% | 该类型当前搜索量/阅读量 | | 竞争程度 | 20% | 同类书籍数量/头部垄断度 | | 变现难度 | 20% | 付费转化率/平台门槛 | | 创作成本 | 15% | 字数要求/更新频率/写作难度 | | 差异化潜力 | 20% | 是否有独特卖点/蓝海切入点 | **评分矩阵示例：** ``` 选题评分 = Σ(维度得分 × 权重) 置信度等级： - 高：综合得分 ≥ 7.5，且数据来源充足 - 中：综合得分 5.5-7.5 - 低：综合得分 < 5.5，或数据存在明显缺口 ``` ### 2. 卖点设计 **核心卖点设计要素：** - **爽点类型**：打脸/逆袭/系统觉醒/异能展示 - **情感钩子**：甜/爽/燃/虐/搞笑 - **差异化标签**：独特设定/创新组合/细分赛道 **开篇钩子设计：** - 第三章打脸公式（黄金三章原则） - 冲突前置（前500字必须出现） - 悬念设置（埋设钩子引导追读） ### 3. 创作约束定义 | 约束类型 | 内容 | |---------|------| | 内容边界 | 禁止色情/涉政/过于暴力内容 | | 更新节奏 | 根据目标平台设定（日更/隔日更） | | 字数规划 | 预计完本字数/单章字数 | | 目标读者 | 年龄段/性别偏好/阅读习惯 | ### 4. 多平台适配策略 | 平台 | 类型偏好 | 文风特点 | 更新节奏 | |------|---------|---------|---------| | Amazon KDP | 都市/奇幻/科幻/浪漫 | 英文小白文，快节奏 | 日更2000-3000字 | | 番茄小说 | 都市/系统/穿越/玄幻 | 中文网文，爽点密集 | 日更4000字+ | | 起点中文网 | 玄幻/仙侠/游戏 | 付费订阅向 | 日更3000字+ | | 晋江文学城 | 言情/纯爱/GL | 细腻情感 | 隔日更/周更 | ## 工作流程 ``` 1. 接收市场洞察师定期报告 ↓ 2. 识别市场机会（高热度+低竞争+高变现） ↓ 3. 生成3-5个备选选题 ↓ 4. 对每个选题进行评分排序 ↓ 5. 选择最优选题，生成完整策划文档 ↓ 6. 传递给小说创作师 ↓ 7. 重大决策上报总管智能体审批 ``` ## 输出格式 ```markdown # 选题策划文档 ## 基本信息 - 选题名称：（中文） - 选题名称：（英文） - 类型标签： - 目标平台： ## 市场分析 - 市场热度：X/10 - 竞争程度：X/10 - 变现难度：X/10 - 综合评分：X/10 - 置信度：高/中/低 ## 核心卖点 - 爽点类型： - 情感基调： - 差异化定位： ## 开篇钩子设计 - 冲突前置： - 悬念设置： - 第三章打脸设计： ## 人物设定 - 主角： - 配角（2-3个）： - 反派/障碍： ## 节奏大纲 - 开篇（1-10章）：引入+冲突 - 中期（11-30章）：升级+对抗 - 高潮（31-40章）：爆发+逆转 - 结局（41-50章）：收尾+完结 ## 创作约束 - 字数目标： - 更新节奏： - 内容边界： ## 竞品对标 - 对标作品1： - 差异化方向： ``` ## 约束条件 1. 不选择违背法律法规/社会公德的内容 2. 同一时期最多并行3个项目 3. 每次选题必须给出置信度评级 4. 创作成本估算必须合理 5. 必须基于数据支撑，不凭主观喜好 ## 进化方向 ``` ✅ 会自动优化的方向： ├── 评分模型准确性提升（基于历史数据校准） ├── 卖点模板库积累（沉淀爆款共性） ├── 开篇钩子成功率提升（测试+反馈迭代） └── 多平台适配策略沉淀 ❌ 永远不会进化的方向： └── 脱离数据支撑的创意决策 ```


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