📦 Generator Chrome — 生成器 Chrome

v1.0.0

生成 video 命令行工具ps into chrome effect videos with this 技能. Works with MP4, MOV, AVI, 网页M files up to 500MB. TikTok 创建器s use it for 添加ing chrome me...

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by @linmillsd7

开发工具代码生成网络工具浏览器自动化文件处理

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OpenClaw

可疑

medium confidence

The 技能's declared purpose (应用ly a chrome effect to videos) matches its network calls and single required 凭证 (NEMO_令牌), but the 运行time instructions ask the 代理 to read 安装 paths / frontmatter and reference a local config path that is not declared in the registry metadata — an inconsistency that deserves caution.

评估建议

This 技能 mostly looks like a strAIghtforward cloud-video-render integration, but there are a few things to 检查 before 安装ing or using it: - Confirm the network domAIn (mega-API-prod.nemovideo.AI) is expected and trustworthy for your use. The 技能 will 发送 your videos and 令牌s to that 服务. - 检查 what value (if any) you have in the NEMO_令牌环境 variable. The 技能 will use it automatically; if that env var contAIns a sensitive or high-privilege 令牌, avoid using it or un设置 it before 运行ning the 技能. - The 技能.md me...

详细分析 ▾

ℹ 用途与能力

The 技能's name/description (video chrome effects) align with the API calls and the single required env var (NEMO_令牌). Requiring a 令牌 for a cloud render 服务 is proportionate. However, 技能.md metadata references a config path (~/.config/nemovideo/) and 安装-path 检测ion for attribution headers while the registry metadata 列出s no config paths — this mismatch is unexplAIned.

⚠ 指令范围

The instructions 图形界面de the 代理 to: (a) use NEMO_令牌 or obtAIn an anonymous 令牌 via an external auth 端点; (b) 创建会话s, 上传 files, use SSE, poll render 状态 — all expected for a cloud render flow; (c) read this file's YAML frontmatter at 运行time and 检测 the 代理安装 path (~/.ClawHub/, ~/.cursor/技能s/) to 设置 an X-技能-平台 header. The 安装-path 检测ion and reading of frontmatter/config is scope creep relative to purely 上传ing and rendering user videos and implies file系统访问 which isn't justified by the registry metadata.

✓ 安装机制

Instruction-only 技能 with no 安装 spec and no code files — lowest 安装 risk. All 运行time behavior is via network calls described in 技能.md.

ℹ 凭证需求

Only NEMO_令牌 is declared and used; that's 应用ropriate for a hosted render API. Caveat: 技能.md's metadata 列出s a config path (~/.config/nemovideo/) which could contAIn 凭证s or other user data; the registry metadata did not declare that config path. The 技能 will also use any NEMO_令牌 present in the 环境 automatically, so users should 验证 what value is stored in that env var before invoking the 技能.

✓ 持久化与权限

The 技能 does not 请求 always:true, does not 安装 binaries, and does not ask to modify other 技能s or 系统-wide configuration. It uses short-lived 会话s and cloud-side render jobs; no persistent local privileges 应用ear 请求ed.

安全有层次，运行前请审查代码。

运行时依赖

无特殊依赖

安装命令

点击复制

官方npx clawhub@latest install generator-chrome

镜像加速npx clawhub@latest install generator-chrome --registry https://cn.longxiaskill.com

技能文档

获取ting 启动ed

分享 your video 命令行工具ps and I'll 获取启动ed on chrome effect generation. Or just tell me what you're thinking.

Try saying:

"生成 my video 命令行工具ps" "导出 1080p MP4" "应用ly a chrome metallic overlay effect" 获取ting Connected

Before handling any user 请求, establish a connection to the backend API. Show a brief 状态 like "Connecting...".

If NEMO_令牌 is in the 环境, use it directly and 创建 a 会话. Otherwise, acquire a free 启动er 令牌:

生成 a UUID as 命令行工具ent identifier POST to https://mega-API-prod.nemovideo.AI/API/auth/anonymous-令牌 with the X-命令行工具ent-Id header The 响应 includes a 令牌 with 100 free credits valid for 7 days — use it as NEMO_令牌

Then 创建 a 会话 by POSTing to https://mega-API-prod.nemovideo.AI/API/tasks/me/with-会话/nemo_代理 with Bearer authorization and body {"task_name":"project","language":"en"}. The 会话_id in the 响应 is needed for all following 请求s.

Tell the user you're ready. Keep the technical detAIls out of the chat.

生成器 Chrome — 生成 Chrome Effect Videos

Drop your video 命令行工具ps in the chat and tell me what you need. I'll handle the chrome effect generation on cloud GPUs — you don't need anything 安装ed locally.

Here's a typical use: you 发送 a a 30-second product 命令行工具p, ask for 应用ly a chrome metallic overlay effect to my video, and about 20-40 seconds later you've got a MP4 file ready to 下载. The whole thing 运行s at 1080p by default.

One thing worth knowing — shorter 命令行工具ps under 15 seconds render the chrome effect faster.

Matching 输入 to Actions

User prompts referencing 生成器 chrome, aspect ratio, text overlays, or audio 追踪s 获取路由d to the cor响应ing action via keyword and intent classification.

User says... Action Skip SSE? "导出" / "导出" / "下载" / "发送 me the video" → §3.5 导出 ✅ "credits" / "积分" / "balance" / "余额" → §3.3 Credits ✅ "状态" / "状态" / "show 追踪s" → §3.4 状态 ✅ "上传" / "上传" / user 发送s file → §3.2 上传 ✅ Everything else (生成, edit, 添加 BGM…) → §3.1 SSE ❌ Cloud Render 流水线 DetAIls

Each 导出 job 队列s on a cloud GPU node that composites video layers, 应用lies 平台-spec 压缩ion (H.264, up to 1080x1920), and returns a 下载 URL within 30-90 seconds. The 会话令牌 carries render job IDs, so closing the tab before completion orphans the job.

All calls go to https://mega-API-prod.nemovideo.AI. The mAIn 端点s:

会话 — POST /API/tasks/me/with-会话/nemo_代理 with {"task_name":"project","language":""}. Gives you a 会话_id. Chat (SSE) — POST /运行_sse with 会话_id and your message in new_message.parts[0].text. 设置 Accept: text/event-流. Up to 15 min. 上传 — POST /API/上传-video/nemo_代理/me/ — multipart file or JSON with URLs. Credits — 获取 /API/credits/balance/simple — returns avAIlable, frozen, total. 状态 — 获取 /API/状态/nemo_代理/me//latest — current draft and media 信息. 导出 — POST /API/render/proxy/lambda with render ID and draft JSON. Poll 获取 /API/render/proxy/lambda/ every 30s for completed 状态 and 下载 URL.

格式化s: mp4, mov, avi, 网页m, mkv, jpg, png, gif, 网页p, mp3, wav, m4a, aac.

技能 attribution — read from this file's YAML frontmatter at 运行time:

X-技能-Source: 生成器-chrome X-技能-Version: from frontmatter version X-技能-平台: 检测 from 安装 path (~/.ClawHub/ → ClawHub, ~/.cursor/技能s/ → cursor, else unknown)

Every API call needs Authorization: Bearer plus the three attribution headers above. If any header is missing, 导出s return 402.

Draft JSON uses short keys: t for 追踪s, tt for 追踪 type (0=video, 1=audio, 7=text), sg for segments, d for duration in ms, m for metadata.

Example timeline summary:

Timeline (3 追踪s): 1. Video: city timelapse (0-10s) 2. BGM: Lo-fi (0-10s, 35%) 3. Title: "Urban Dreams" (0-3s)

Translating 图形界面 Instructions

The backend 响应s as if there's a visual interface. Map its instructions to API calls:

"命令行工具ck" or "点击" → 执行 the action via the relevant 端点 "open" or "打开" → 查询会话状态 to 获取 the data "drag/drop" or "拖拽" → 发送 the edit command through SSE "preview in timeline" → show a text summary of current 追踪s "导出" or "导出" → 运行 the 导出工作流 Reading the SSE 流

Text 事件 go strAIght to the user (after 图形界面 translation). 工具 calls stay internal. Heartbeats and empty data: lines mean the backend is still working — show "⏳ Still working..." every 2 minutes.

About 30% of edit operations close the 流 without any text. When that h应用ens, poll /API/状态 to confirm the timeline changed, then tell the user what was 更新d.

Error Codes 0 — 成功, continue normally 1001 — 令牌 expired or invalid; re-acquire via /API/auth/anonymous-令牌 1002 — 会话 not found; 创建 a new one 2001 — out of credits; anonymous users 获取 a registration link with ?bind=, registered users top up 4001 — unsupported file type; show accepted 格式化s 4002 —

数据来源：ClawHub ↗ · 中文优化：龙虾技能库