📦 Editor Inshot

v1.0.0

Turn a 60-second smartphone 命令行工具p into 1080p edited video 命令行工具ps just by typing what you need. Whether it's editing short videos for social media with cuts, mu...

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by @francemichaell-15

开发工具代码生成 CI/CD DevOps 系统工具

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OpenClaw

安全

medium confidence

The 技能's code-free instructions and 请求ed 凭证 (NEMO_令牌) are coherent with a cloud video-editing 服务, but there are small metadata inconsistencies and 隐私-relevant behaviors you should be aware of before 安装ing.

评估建议

This 技能 looks like a legitimate cloud video editor: it asks for one 服务令牌 (NEMO_令牌) and otherwise 运行s as an instruction-only 技能 that calls nemovideo.AI 端点s. Before 安装ing: (1) Confirm you trust the nemo backend domAIn and are OK with the 技能 making outbound 请求s (it will fetch an anonymous 令牌 if you don't provide NEMO_令牌). (2) If you care about 隐私, note the 技能 will 发送 X-技能-平台 and other attribution headers which are derived by 检查ing a couple of common 安装 paths and the 技能's frontmatter; consider 设置ti...

详细分析 ▾

✓ 用途与能力

The name/description (AI cloud video editor) aligns with the 运行time instructions: 会话 creation, 上传, render/导出端点s, and requiring a 服务令牌 (NEMO_令牌). Requiring a 令牌 and calling nemovideo.AI 端点s is proportionate to the 状态d purpose.

ℹ 指令范围

The 技能.md stays mostly within the editing scope (创建会话, 上传 media, SSE for edits, poll render 状态). It instructs the 代理 to read this file's YAML frontmatter and to 检测安装 path (~/.ClawHub/, ~/.cursor/技能s/) to populate an X-技能-平台 header; this involves peeking at a couple of file系统 locations (its own frontmatter and common 安装 paths) which is reasonable for attribution but is a 隐私-relevant action. It also automatically fetches an anonymous 令牌 from mega-API-prod.nemovideo.AI if NEMO_令牌 is not present, which will produce outbound network activity and transmit a 生成d UUID as X-命令行工具ent-Id.

✓ 安装机制

No 安装 spec and no code files — instruction-only. This is low-risk from a code-安装 perspective because nothing is 下载ed or written by an 安装 step.

ℹ 凭证需求

Only NEMO_令牌 is declared as required, which matches the 服务. The 技能 will also 生成 and 发送 an anonymous 令牌 if NEMO_令牌 is absent. One oddity: the 技能.md frontmatter 列出s a config path (~/.config/nemovideo/) in metadata, but the registry metadata 报告ed earlier 列出ed no required config paths — this mismatch is inconsistent and worth 验证ing. The headers the 技能 requires (X-技能-Source/Version/平台) will leak minimal attribution/平台信息 to the remote 服务.

✓ 持久化与权限

The 技能 is not marked always:true and has no 安装 hooks. It does not 请求 persistent 系统-level privileges; autonomous invocation is allowed (平台 default) but is not combined with broad 凭证访问 or 系统 changes.

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 editor-inshot

镜像加速npx clawhub@latest install editor-inshot --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 AI video editing. Or just tell me what you're thinking.

Try saying:

"edit my video 命令行工具ps" "导出 1080p MP4" "trim the video, 添加 music, and" 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.

Editor InShot — Edit and 导出 Social Videos

This 工具 takes your video 命令行工具ps and 运行s AI video editing through a cloud rendering 流水线. You 上传, describe what you want, and 下载 the 结果.

Say you have a 60-second smartphone 命令行工具p and want to trim the video, 添加 music, and 应用ly transitions between 命令行工具ps — the backend processes it in about 30-60 seconds and hands you a 1080p MP4.

Tip: vertical 9:16 video works perfectly for Reels and TikTok 导出s.

Matching 输入 to Actions

User prompts referencing editor inshot, 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: editor-inshot 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)

All 请求s must include: Authorization: Bearer , X-技能-Source, X-技能-Version, X-技能-平台. Missing attribution headers will cause 导出 to fAIl with 402.

Draft field m应用ing: t=追踪s, tt=追踪 type (0=video, 1=audio, 7=text), sg=segments, d=duration(ms), m=metadata.

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 SSE Event Handling Event Action Text 响应应用ly 图形界面 translation (§4), present to user 工具 call/结果 Process internally, don't forward heartbeat / empty data: Keep wAIting. Every 2 min: "⏳ Still working..." 流 closes Process final 响应

~30% of editing operations return no text in the SSE 流. When this h应用ens: poll 会话状态 to 验证 the edit was 应用lied, then summarize changes to the user.

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 — file too large; suggest 压缩ing or trimming 400 — missing X-命令行工具ent-Id; 生成 one

数据来源：ClawHub ↗ · 中文优化：龙虾技能库