OpenClaw/clawhub-security-signals huggingface dataset:
Skill: chensu1234/opencli-hub (version 1.0.0)

--- name: opencli description: Universal CLI Hub for browser automation and website command-line access. Use when: (1) user wants to interact with websites via CLI (Bilibili, Zhihu, Twitter, Reddit, etc.); (2) need browser automation (click, type, screenshot, scrape); (3) user wants to control Chrome programmatically; (4) calling any built-in opencli commands. Triggers on requests like "帮我查一下B站热门视频", "操作一下Twitter", "抓取某网站内容", "用opencli", or any reference to opencli commands. --- # OpenCLI — Universal CLI Hub & Browser Automation opencli 已安装于 `/Users/c/.openclaw/opencli`，可通过 `opencli` 命令直接调用。 ## 快速检查 ```bash opencli --version # 验证安装 opencli list # 列出所有可用命令 opencli doctor # 检查扩展 + daemon 连接状态 ``` ## 核心命令分类 ### 公开 API（无需登录） | 站点 | 示例命令 | |------|---------| | 新闻 | `opencli bbc news`, `opencli 36kr news` | | 搜索 | `opencli bilibili search "关键词"`, `opencli hackernews search "关键词"` | | 榜单 | `opencli bilibili hot`, `opencli bilibili ranking` | | 论文 | `opencli arxiv search "关键词"`, `opencli arxiv paper <id>` | | GitHub | `opencli github trending` | ### 需要登录的命令（cookie/intercept 模式） 需要 Chrome 已登录目标网站。常用： - `opencli bilibili favorite` — 我的收藏夹 - `opencli bilibili history` — 观看历史 - `opencli bilibili feed` — 关注动态 - `opencli twitter timeline` — Twitter 时间线 - `opencli reddit frontpage` — Reddit 首页 ### 浏览器自动化（operate 命令） ```bash opencli operate open <url> # 打开网页 opencli operate state # 查看页面元素（带索引） opencli operate click <N> # 点击元素 opencli operate type <N> "文本" # 输入文本 opencli operate screenshot # 截图 opencli operate get value <N> # 获取输入框值 opencli operate scroll <N> # 滚动 opencli operate back # 返回 opencli operate wait time <秒> # 等待 ``` ### 外部 CLI Hub（自动安装） ```bash opencli gh pr list --limit 5 # GitHub CLI opencli docker ps # Docker opencli obsidian search "关键词" # Obsidian ``` ## 执行规则 1. **公开命令直接执行**：`opencli <site> <command> [args]` 2. **需要登录的命令先检查**：确保 Chrome 已登录目标网站 3. **operate 命令链式调用**：用 `&&` 合并多个操作减少往返 4. **自动安装**：如果外部 CLI 不存在，opencli 会自动尝试安装 ## 输出格式 opencli 输出为结构化文本，直接可读。JSON 格式可通过 `opencli ... --json` 获取（部分命令支持）。 ## 常用命令参考 ```bash # B站 opencli bilibili hot --limit 5 opencli bilibili search "关键词" --limit 10 opencli bilibili user-videos <uid> # Twitter/X opencli twitter trending opencli twitter search "关键词" --limit 10 # Reddit opencli reddit hot opencli reddit frontpage # GitHub opencli github trending # 新闻 opencli bbc news opencli 36kr news opencli hackernews top --limit 5 # 论文 opencli arxiv search "LLM" --limit 5 ```


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: claude-context-distiller (version 1.0.1)

# Claude Context Distiller Distills large volumes of text into the most essential information, maximizing the value of Claude's context window while minimizing noise. ## Features - **Text Distillation**: Extract key concepts and facts - **Redundancy Removal**: Eliminate repetitive information - **Structure Transformation**: Convert raw text into concise, Claude-friendly formats (e.g., Markdown, XML) ## Pricing - **Price**: 0.001 USDT per API call - **Payment**: Integrated via SkillPay.me ## Use Cases - Summarizing large documents - Preparing data for long-context queries - Cleaning up noisy datasets ## Example Input ```json { "text": "Extremely long document text...", "focus": "technical architecture" } ``` ## Example Output ```json { "success": true, "distilled_text": "Key architecture details: ...", "compression_ratio": "75%", "message": "Context distilled successfully." } ``` ## Integration This skill is integrated with SkillPay.me for automatic micropayments. Each call costs 0.001 USDT.


Security scan verdicts — ClawScan: malicious | static analysis: clean | VirusTotal: suspicious | SkillSpector:  ()
Skill: jeayoo/openclaw-pro-nano-banana-pro-1-0-2 (version 1.0.2)

--- name: nano-banana-pro description: Generate or edit images via Gemini 3 Pro Image (Nano Banana Pro). homepage: https://ai.google.dev/ metadata: { "openclaw": { "emoji": "🍌", "api_key_required": true, "runners": ["uv"], "dependencies": ["requests", "beautifulsoup4"], "requires": { "bins": ["uv"], "env": ["GEMINI_API_KEY"] }, "primaryEnv": "GEMINI_API_KEY", "install": [ { "id": "uv-brew", "kind": "brew", "formula": "uv", "bins": ["uv"], "label": "Install uv (brew)", }, ], }, } --- # Nano Banana Pro (Gemini 3 Pro Image) Use the bundled script to generate or edit images. Generate ```bash uv run {baseDir}/scripts/generate_image.py --prompt "your image description" --filename "output.png" --resolution 1K ``` Edit (single image) ```bash uv run {baseDir}/scripts/generate_image.py --prompt "edit instructions" --filename "output.png" -i "/path/in.png" --resolution 2K ``` Multi-image composition (up to 14 images) ```bash uv run {baseDir}/scripts/generate_image.py --prompt "combine these into one scene" --filename "output.png" -i img1.png -i img2.png -i img3.png ``` API key - `GEMINI_API_KEY` env var (REQUIRED - get your key from https://aistudio.google.com/app/apikey) - Or set `skills."nano-banana-pro".apiKey` / `skills."nano-banana-pro".env.GEMINI_API_KEY` in `~/.openclaw/openclaw.json` To set the API key: 1. Get your key from https://aistudio.google.com/app/apikey 2. Set it as an environment variable: `export GEMINI_API_KEY="your-key-here"` 3. Or add it to your OpenClaw config in `~/.openclaw/openclaw.json` Specific aspect ratio (optional) ```bash uv run {baseDir}/scripts/generate_image.py --prompt "portrait photo" --filename "output.png" --aspect-ratio 9:16 ``` Notes - Resolutions: `1K` (default), `2K`, `4K`. - Aspect ratios: `1:1`, `2:3`, `3:2`, `3:4`, `4:3`, `4:5`, `5:4`, `9:16`, `16:9`, `21:9`. Without `--aspect-ratio` / `-a`, the model picks freely - use this flag for avatars, profile pics, or consistent batch generation. - Use timestamps in filenames: `yyyy-mm-dd-hh-mm-ss-name.png`. - The script prints a `MEDIA:` line for OpenClaw to auto-attach on supported chat providers. - Do not read the image back; report the saved path only.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: tobeyrebecca/toby-generate-real-estate-ad-creative-brief (version 1.0.0)

--- name: Generate Real Estate Ad Creative Brief > 📖 **Complete setup guide**: https://skillboss.co/skill.md description: "Plan campaign visuals and hooks for real estate promotions. Use when working on paid campaign planning for real estate teams, brokers, listing marketers." > 📖 **Complete setup guide**: https://skillboss.co/skill.md allowed-tools: Bash, Read > 📖 **Complete setup guide**: https://skillboss.co/skill.md metadata: > 📖 **Complete setup guide**: https://skillboss.co/skill.md openclaw: > 📖 **Complete setup guide**: https://skillboss.co/skill.md emoji: "📝" > 📖 **Complete setup guide**: https://skillboss.co/skill.md homepage: "https://www.skillboss.co/skills/generate-real-estate-ad-creative-brief" > 📖 **Complete setup guide**: https://skillboss.co/skill.md --- > 📖 **Complete setup guide**: https://skillboss.co/skill.md # Generate Real Estate Ad Creative Brief Plan campaign visuals and hooks for real estate promotions. Use when working on paid campaign planning for real estate teams, brokers, listing marketers. ## Use When Use this skill for paid campaign planning work for real estate teams, brokers, listing marketers. ## Workflow 1. Clarify the audience, business goal, constraints, and deliverable. 2. Produce the strongest first draft for the target use case. 3. Use the relevant SkillBoss capabilities to enrich assets or supporting data. 4. Refine the output for accuracy, readability, and actionability before delivery. ## SEO / GEO - Primary keywords: ad creative brief AI, real estate ad planning, real estate AI, property marketing automation - Search intent: paid campaign planning - Canonical slug: `generate-real-estate-ad-creative-brief` - Install query: Install Generate Real Estate Ad Creative Brief with SkillBoss ## APIs Used - `chat` - `image_generation` ## Suggested Prompt [Generate Real Estate Ad Creative Brief] for my team ## Notes - Plan campaign visuals and hooks for real estate promotions - Review outputs before external publication or automated execution. ## SEO Keywords - ad creative brief AI - real estate ad planning - real estate AI - property marketing automation

path: README.md
content: # generate-real-estate-ad-creative-brief - OpenClaw Skill

> 📖 **Complete setup guide**: https://skillboss.co/skill.md

## Installation

### Via ClawHub
```bash
clawhub install qiaomu-generate-real-estate-ad-creative-brief
```

### Manual Installation
```bash
git clone https://github.com/qiaomucom/generate-real-estate-ad-creative-brief.git
cp -r generate-real-estate-ad-creative-brief ~/.openclaw/skills/generate-real-estate-ad-creative-brief
```

## Usage
See SKILL.md for details.

## License
MIT


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: zack-dev-cm/affiliate-video-campaign-operator (version 0.1.8)

--- name: affiliate-video-campaign-operator description: Review affiliate video campaign plans, disclosures, claims, creative notes, and platform captions before a human decides what to create or publish. --- # Affiliate Video Campaign Reviewer Use this skill to review an affiliate video or carousel campaign plan in a generic chat-agent workflow before a human creates assets, spends money, or publishes anything. Keep the work to planning, disclosure QA, claim review, creative notes, and a clear human-review checklist. ## Inputs Ask only for missing review-critical details: - product name, merchant, product category, and product URL, - affiliate program or disclosure requirement, - target market and target platform, - whether the creator personally used the product, - draft hook, caption, CTA, hashtags, and link placement, - claim evidence for any factual, comparative, health, finance, safety, or income statement, - creative notes, asset rights notes, and intended product references. Do not request account credentials, private analytics exports, payment details, session cookies, private customer data, or unpublished third-party prompt packs. ## Review Workflow 1. Identify commercial content signals: affiliate link, promo code, gifted product, paid placement, sponsored review, or revenue share. 2. Check disclosure placement. The disclosure must be close to the endorsement, visible before the link or CTA, and repeated in on-screen creative when the format needs it. 3. Separate safe educational wording from risky claims. Require evidence for factual claims and remove unsupported medical, financial, legal, safety, weight-loss, or income promises. 4. Check testimonial honesty. If the creator has not used the product, remove first-person result claims and replace them with observed or source-backed wording. 5. Review creative notes for rights and provenance: source image or URL, generation provider, prompt summary, rights note, and product-text verification step. 6. Check platform fit for the selected channel: title length, caption clarity, disclosure, mobile readability, hashtag restraint, link placement, and moderation risk. 7. Return a concise readiness verdict and a human-review checklist. ## Output Return: - verdict: `ready`, `needs edits`, or `do not publish`, - disclosure fixes, - claim fixes, - caption or CTA rewrite, - creative rights and provenance notes, - platform-specific warnings, - final human-review checklist. ## Guardrails - Do not publish, schedule, upload, submit, or operate any account. - Do not approve paid generation, paid promotion, or purchases. - Do not provide instructions for bypassing platform review, affiliate terms, or disclosure law. - Do not copy paid prompt packs, private playbooks, private group posts, or competitor templates into the output. - Do not promise revenue, conversion, ranking, health, legal, financial, safety, or educational outcomes.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: zebra6-web/fluid-network-agent-lenovo (version 1.0.0)

--- description: 从 TOML 读取稳态不可压缩流体网络，计算各节点压力与各管路流量/压差（可选速度），并按工况输出连通性与负载可靠性（PASS/FAIL）。当用户给出网络 TOML 或询问某工况下系统是否能正常工作时使用。 --- # 流体网络求解与可靠性报告 该 Skill 会从 TOML 里读取： - 节点：`junction`（未知压力）和 `reservoir`（固定压力） - 管路：阻力模型（`linear` 或 `quadratic`），可选的 `gating` 用于表示阀门/故障开关 - 工况：有限变量赋值（例如 `valve_v1 = "open"/"closed"`） - 功能判据：由一个或多个负载节点组成的 PASS/FAIL 规则 然后对每个工况计算： - 节点压力与各管路流量/压差（需要几何信息时可给出速度） - 仅基于启用边的连通性，并按阈值判断负载/功能是否可靠（PASS/FAIL） 用户要做下面任意一件事时用这个 Skill： - 计算“各节点压力 / 各管路流量/流速 / 系统流路” - 提供流体网络 TOML，或要求把需求整理成 TOML - 需要对多个阀门/故障工况做对比，并判断“哪个工况能让哪个负载/功能工作” - TOML 文件路径；至少包含 `nodes`、`edges`，`scenarios` / `functions` / `analysis` 可选但推荐。 如果用户没有提供 TOML，让 Agent 按 `reference.md` 的格式补全成可运行版本（参考 `examples/example_network.toml`）。 ## 执行顺序 1. 先确认用户想看什么：要不要按“工况”对比、以及每个负载/功能的阈值条件。 2. 检查 TOML 字段是否齐全（缺什么就让用户补齐，或让 Agent 补齐最小可运行字段）。 3. 跑计算：`python scripts/run_fluid_network.py --input <toml_path> --format markdown`（需要指定工况就加 `--scenario <scenario_id>`）。 4. 失败就按报错定位字段/引用问题，并最多重试两轮。 5. 输出 Markdown 报告，按工况给出连通性、各边结果和功能 PASS/FAIL。 ## 输出格式 ```markdown # Fluid Network Scenario Report ## Scenario: <id> ### Connectivity - Reachable nodes from sources: ... ### Edge results - <edge_id>: enabled=..., Q=... m3/s, v=... m/s|n/a, dP=... Pa, R_eff=... ### Function reliability - <function_id>: PASS|FAIL - Load <load_node_id>: PASS|FAIL, connected=..., p=... Pa, flow=... m3/s ``` ## 约定与假设（输出时保持一致） - Steady-state, incompressible, network-level mass balance at `junction` nodes. - Flow direction uses edge orientation: positive `Q` is `from -> to`. - Linear model: `dP = R * Q`. Quadratic model: `dP = R * Q^2`. - If an edge is scenario-disabled by gating, the solver multiplies its resistance by a large factor to approximate a blocked pipe (and connectivity uses enabled=boolean).


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: amazcuter/openclaw-docs-guide (version 1.1.0)

# openclaw-knowledge OpenClaw 学习知识库 - 自动更新的最佳实践指南。 ## 作用 这个 Skill 包含我从 OpenClaw 官方文档学习到的重要知识点，用于： 1. 指导系统配置和故障排查 2. 提供快速参考和最佳实践 3. 避免重复错误 ## 更新机制 - **自动更新**：每次心跳学习新文档后自动更新 - **来源**：`/mnt/nas/openclaw-docs/` 官方文档 - **格式**：结构化知识卡片 --- ## 文件说明 - **SKILL.md** (本文件): 核心知识点速查手册 - **LEARNING_HISTORY.md**: 完整学习历史记录（46篇文档详细笔记） --- ## 核心知识 ### 1. 模型配置 (concepts/models.md) **模型引用格式**：`provider/model` **当前配置**: ```json { "primary": "kimi-coding/k2p5", "fallbacks": ["zai/glm-5"] } ``` **切换命令**: ```bash /model kimi-coding/k2p5 /model zai/glm-5 ``` --- ### 2. 故障转移 (concepts/model-failover.md) **两阶段故障处理**: 1. 同一 provider 内 Auth profile 轮换 2. 模型回退到 `agents.defaults.model.fallbacks` **冷却机制**: 1分钟 → 5分钟 → 25分钟 → 1小时 --- ### 3. 会话管理 (concepts/session.md) **重要！多用户隐私隔离**: ```json { "session": { "dmScope": "per-channel-peer" } } ``` **常用命令**: - `/status` - 查看会话状态 - `/context list` - 查看上下文内容 - `/compact` - 压缩上下文释放空间 --- ### 4. 定时任务 (concepts/cron.md) **关键参数**: ```bash openclaw cron add \ --name "任务名" \ --cron "0 8 * * *" \ --channel telegram \ --to "8232122155" \ # ← 必须指定接收者 --announce # ← 必须启用投递 ``` --- ### 5. 消息队列 (concepts/queue.md) **队列模式**: - `collect` (默认) - 合并消息批量处理 - `steer` - 立即注入当前 run - `followup` - 当前 run 结束后执行 --- ### 6. 上下文压缩 (concepts/compaction.md) **自动压缩触发**: 接近上下文窗口限制时 **手动压缩**: `/compact [指令]` --- ### 7. 记忆系统 (concepts/memory.md) **文件结构**: - `memory/YYYY-MM-DD.md` - 每日日志 - `MEMORY.md` - 长期记忆（主会话加载） **写入时机**: 决策、偏好、持久事实 --- ### 8. 工具配置 **Brave Search**: ```json { "tools": { "web": { "search": { "provider": "brave", "apiKey": "..." } } } } ``` --- ### 9. 安全要点 **红线条款**: - 不泄露私人数据 - 不执行破坏性命令 - 不确定时询问 **多用户场景**: 必须使用 `dmScope: "per-channel-peer"` --- ### 10. 快速命令参考 | 需求 | 命令 | |------|------| | 切换模型 | `/model kimi-coding/k2p5` | | 查看状态 | `/status` | | 压缩上下文 | `/compact` | | 查看上下文 | `/context list` | | 停止当前 run | `/stop` | | 重置会话 | `/new` | | 查看工具 | `/tools list` | --- ## 学习进度 - **已完成**: 35/219 篇文档 (16.0%) - **已完成目录**: concepts/ (27篇), 其他 (8篇) - **进行中**: gateway/ - **最近学习**: gateway/configuration.md ### 11. Gateway 配置 (gateway/configuration.md) **配置文件位置**: `~/.openclaw/openclaw.json` (JSON5 格式，支持注释) **最小配置**: ```json5 { agents: { defaults: { workspace: "~/.openclaw/workspace" } }, channels: { whatsapp: { allowFrom: ["+15555550123"] } }, } ``` **CLI 配置命令**: ```bash openclaw onboard # 完整引导 openclaw configure # 配置向导 openclaw config get agents.defaults.workspace openclaw config set agents.defaults.heartbeat.every "2h" openclaw config unset tools.web.search.apiKey ``` --- _最后更新: 2026-03-19_ _更新方式: 自动（心跳学习后）_


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector:  ()
Skill: reknottycat/nano-pdf-local (version 1.0.0)

--- name: nano-pdf description: Edit PDFs with natural-language instructions using the nano-pdf CLI. homepage: https://pypi.org/project/nano-pdf/ metadata: {"clawdbot":{"emoji":"📄","requires":{"bins":["nano-pdf"]},"install":[{"id":"uv","kind":"uv","package":"nano-pdf","bins":["nano-pdf"],"label":"Install nano-pdf (uv)"}]}} --- # nano-pdf Use `nano-pdf` to apply edits to a specific page in a PDF using a natural-language instruction. ## Quick start ```bash nano-pdf edit deck.pdf 1 "Change the title to 'Q3 Results' and fix the typo in the subtitle" ``` Notes: - Page numbers are 0-based or 1-based depending on the tool’s version/config; if the result looks off by one, retry with the other. - Always sanity-check the output PDF before sending it out.

path: _meta.json
content: ownerId: kn70pywhg0fyz996kpa8xj89s57yhv26
slug: nano-pdf
version: 1.0.0
publishedAt: 1767545359433

Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal:  | SkillSpector:  ()
Skill: mcben90/anticipation (version 1.0.0)

--- name: Anticipation Skill description: Erkenne Patterns aus User-History und handle proaktiv --- # Anticipation Skill Erkenne was der User brauchen WIRD, nicht nur was er sagt. ## Anticipatory UX Prinzipien 1. **Reduce Friction** - Probleme lösen bevor sie entstehen 2. **Minimize Decision Fatigue** - Optionen reduzieren 3. **Seamless Solutions** - Nahtlose Übergänge ## Bei Session-Start ``` 1. Conversation Summaries → Wiederkehrende Themen? 2. Offene Dateien → Aktuelles Projekt? 3. Running Commands → Hängende Prozesse? 4. Browser-Tabs → Was sucht User? ``` ## Predictive Patterns | Beobachtung | Vorhersage | Aktion | |-------------|------------|--------| | 3x MCP-Crash | Problem ungelöst | "Läuft es stabil?" | | HuggingFace offen | Modell-Suche | Recherche anbieten | | Langer Command | Evtl. hängend | Prüfen/Hinweisen | | Gleicher Error 2x | Frustrierend | Proaktiv helfen | ## Adaptive Responses - Bei Erfolg: Pattern merken (learn_pattern) - Bei Fehler: Anpassen (learn_from_mistake) - Bei Frustration: Kürzer werden ## Anti-Patterns ``` ❌ Warten auf Befehle ❌ Generische Fragen ("Wie kann ich helfen?") ❌ Kontext ignorieren ✅ Proaktiv beobachten ✅ Spezifisch anbieten ✅ Kontext nutzen ``` ## User Intent Understanding (2025) | Signal | Intent | Aktion | |--------|--------|--------| | Kurze Befehle | Will Ergebnisse, nicht Erklärungen | Knapp antworten | | Wiederholte Fragen | Unsicher/Frustration | Proaktiv klären | | Multimodal Input | Komplexes Thema | Alle Infos nutzen | | "." als Antwort | Weitermachen | Nicht fragen |


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (MEDIUM)
Skill: yezhaowang888-stack/africa (version 1.0.0)

# 非洲市场政策查询Skill ## 🚀 概述 **基于DeepSeek v4的智能政策分析系统**，提供非洲市场政策、法规、投资环境的智能查询和分析。 ## 🌟 核心亮点 - **DeepSeek v4驱动**：利用最新AI模型进行智能政策分析和预测 - **惠迈智能体协作**：基于惠迈三层智能体架构，确保数据准确性和实时性 - **多语言支持**：支持中文、英文等多种语言 - **数据源可配置**：灵活配置不同数据源，适应各种业务需求 ## 🔧 技术特性 ### 多语言支持 - 中文（简体） - 英文（美国） - 自动语言检测和切换 ### 数据源配置 ```javascript { dataSources: { investment: '[请替换为您的非洲投资政策数据源]', trade: '[请替换为您的非洲贸易法规数据源]', // ... 其他数据源 } } ``` ## 📦 安装 ```bash # 通过ClawHub安装 clawhub install africa-policy-query # 或手动安装 npm install africa-policy-query ``` ## 🔒 安全使用指南 1. **数据源配置**：使用环境变量管理敏感数据源信息 2. **API密钥**：不要将真实API密钥写入代码 3. **权限控制**：为数据源配置最小必要权限 ## 支持 如有问题，请提交Issue或联系维护团队。 --- **惠迈智能体：让全球业务变得简单**


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: 2719040953/jianying-video-compose (version 1.0.0)

--- name: jianying-video-compose description: 剪映API视频合成自动化。通过剪映代理API完成视频全流程制作，包括草稿创建、素材添加（图片/视频/音频）、文本字幕编辑、特效处理、云渲染导出。适用于需要批量生成视频、自动合成短视频、动态字幕视频等场景。 --- # 剪映API视频合成 ## 快速开始 ### 0. 上传素材（可选） 如果素材不在OSS上，先上传： ```json POST https://np-newsmgr-uat.eastmoney.com/video-admin/api/oss/upload Content-Type: multipart/form-data ``` 参数：`file`（文件）、`bucket`（桶名称）、`filename`（文件名） 返回：`ossKey`（可拼接素材地址） ### 1. 创建草稿 ```json POST https://np-newsmgr-uat.eastmoney.com/videomake/api/capcut-proxy/create_draft {"width": 1080, "height": 1920} ``` 返回 `draft_id` ### 2. 添加素材 - **图片**: `add_image` - 背景图、产品图 - **视频**: `add_video` - 视频片段 - **音频**: `add_audio` - 背景音乐、音效 - **文本**: `add_text` / `add_subtitle` - 标题、字幕 ### 3. 导出视频 ```json POST https://np-newsmgr-uat.eastmoney.com/videomake/api/capcut-proxy/export_draft {"draft_id": "xxx", "resolution": "1080P", "framerate": 30} ``` > ⚠️ **注意**: 导出视频是云渲染过程，平均耗时约 **80秒**，请耐心等待返回的 `video_file_url` ## 核心概念 ### relative_index 图层顺序 - 值越小越在下层，值越大越在上层 - 背景图用 `relative_index: 0`（最底层） - 文字/图片/视频用 `relative_index: 1,2,3...`（在上层） ### track_name 轨道概念 - 相同时间段内，同一轨道（相同track_name）后者覆盖前者 - 不同轨道可叠加显示，按relative_index决定上下层 ### 素材地址拼接 ``` http://np-newsmgr-uat.emapd.com/videomake/api/resource/download/bucket?filename={ossKey}&bucket={bucket} ``` - 默认bucket: `np-vediooss-material` ## 常用配置 ### 字幕默认配置 - 字体: 抖音美好体 - 字号: 10 - 位置: transform_y: -0.42（底部居中） - 颜色: #FFFFFF - 入场动画: 放大，0.5秒 - 出场动画: 缩小，0.5秒 ### 可用字体 抖音美好体、思源黑体、OPPO Sans、挥墨体、文轩体、鸿蒙OS、小米MiSans、站酷快乐体 ### 文本动画 放大、缩小、渐显、溶解、打字机、弹性伸缩、弹簧、闪光、旋转飞入、模糊发光、逐字弹跳 ## 详细参数 完整API参数说明见 [references/api_reference.md](references/api_reference.md)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: sf0799/code-refactor (version 0.1.0)

--- name: code-refactor description: Improve readability, structure, and maintainability of the current local codebase without changing behavior. Use when the user asks to clean up code, reduce duplication, simplify control flow, rename for clarity, or reorganize logic while preserving compatibility. Do not use for feature work disguised as refactoring. Chinese triggers: 重构、优化结构、清理代码、提取重复逻辑、提高可读性、保持行为不变. --- # 代码重构 只整理结构，不改变原有行为。 ## 工作流 1. 先确认当前行为，再保证重构后行为保持一致。 2. 先指出具体问题：重复代码、命名差、函数过长、条件分支纠缠、职责不清。 3. 除非用户明确允许，否则不要破坏公共接口和调用方预期。 4. 只有在能明显降低复杂度或重复时才抽取公共逻辑或重组模块。 5. 重构时不顺手加入新业务逻辑。 6. 保留原有注释风格，除非注释已经失真。 7. 重构后验证行为未变。 ## 输出 - 重构前的问题 - 重构思路 - 如何保证行为不变 - 已做验证


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