← 返回 Skills 市场
susan4731-wilfordf

Ai Video Editor In Hyderabad

作者 susan4731-wilfordf · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ⚠ suspicious
66
总下载
0
收藏
0
当前安装
1
版本数
在 OpenClaw 中安装
/install ai-video-editor-in-hyderabad
功能描述
Turn a 2-minute event recording shot on a phone in Hyderabad into 1080p polished edited clips just by typing what you need. Whether it's editing local event...
使用说明 (SKILL.md)

Getting Started

Ready when you are. Drop your raw video footage here or describe what you want to make.

Try saying:

  • "edit a 2-minute event recording shot on a phone in Hyderabad into a 1080p MP4"
  • "trim the footage, add transitions, and overlay text in Telugu and English"
  • "editing local event or business videos quickly without professional software for small business owners and content creators in Hyderabad"

Automatic Setup

On first interaction, connect to the processing API before doing anything else. Show a brief status like "Setting things up...".

Token: If NEMO_TOKEN environment variable is already set, use it and skip to Session below.

Free token: Generate a UUID as client identifier, then POST to https://mega-api-prod.nemovideo.ai/api/auth/anonymous-token with header X-Client-Id: \x3Cuuid>. The response field data.token becomes your NEMO_TOKEN (100 credits, 7-day expiry).

Session: POST to https://mega-api-prod.nemovideo.ai/api/tasks/me/with-session/nemo_agent with Bearer auth and body {"task_name":"project"}. Save session_id from the response.

Confirm to the user you're connected and ready. Don't print tokens or raw JSON.

AI Video Editor in Hyderabad — Edit and Export Videos Online

Drop your raw video footage in the chat and tell me what you need. I'll handle the AI video editing on cloud GPUs — you don't need anything installed locally.

Here's a typical use: you send a a 2-minute event recording shot on a phone in Hyderabad, ask for trim the footage, add transitions, and overlay text in Telugu and English, and about 1-2 minutes later you've got a MP4 file ready to download. The whole thing runs at 1080p by default.

One thing worth knowing — shorter clips under 3 minutes process significantly faster and use fewer credits.

Matching Input to Actions

User prompts referencing ai video editor in hyderabad, aspect ratio, text overlays, or audio tracks get routed to the corresponding action via keyword and intent classification.

User says... Action Skip SSE?
"export" / "导出" / "download" / "send me the video" → §3.5 Export
"credits" / "积分" / "balance" / "余额" → §3.3 Credits
"status" / "状态" / "show tracks" → §3.4 State
"upload" / "上传" / user sends file → §3.2 Upload
Everything else (generate, edit, add BGM…) → §3.1 SSE

Cloud Render Pipeline Details

Each export job queues on a cloud GPU node that composites video layers, applies platform-spec compression (H.264, up to 1080x1920), and returns a download URL within 30-90 seconds. The session token carries render job IDs, so closing the tab before completion orphans the job.

Base URL: https://mega-api-prod.nemovideo.ai

Endpoint Method Purpose
/api/tasks/me/with-session/nemo_agent POST Start a new editing session. Body: {"task_name":"project","language":"\x3Clang>"}. Returns session_id.
/run_sse POST Send a user message. Body includes app_name, session_id, new_message. Stream response with Accept: text/event-stream. Timeout: 15 min.
/api/upload-video/nemo_agent/me/\x3Csid> POST Upload a file (multipart) or URL.
/api/credits/balance/simple GET Check remaining credits (available, frozen, total).
/api/state/nemo_agent/me/\x3Csid>/latest GET Fetch current timeline state (draft, video_infos, generated_media).
/api/render/proxy/lambda POST Start export. Body: {"id":"render_\x3Cts>","sessionId":"\x3Csid>","draft":\x3Cjson>,"output":{"format":"mp4","quality":"high"}}. Poll status every 30s.

Accepted file types: mp4, mov, avi, webm, mkv, jpg, png, gif, webp, mp3, wav, m4a, aac.

Headers are derived from this file's YAML frontmatter. X-Skill-Source is ai-video-editor-in-hyderabad, X-Skill-Version comes from the version field, and X-Skill-Platform is detected from the install path (~/.clawhub/ = clawhub, ~/.cursor/skills/ = cursor, otherwise unknown).

All requests must include: Authorization: Bearer \x3CNEMO_TOKEN>, X-Skill-Source, X-Skill-Version, X-Skill-Platform. Missing attribution headers will cause export to fail with 402.

Error Handling

Code Meaning Action
0 Success Continue
1001 Bad/expired token Re-auth via anonymous-token (tokens expire after 7 days)
1002 Session not found New session §3.0
2001 No credits Anonymous: show registration URL with ?bind=\x3Cid> (get \x3Cid> from create-session or state response when needed). Registered: "Top up credits in your account"
4001 Unsupported file Show supported formats
4002 File too large Suggest compress/trim
400 Missing X-Client-Id Generate Client-Id and retry (see §1)
402 Free plan export blocked Subscription tier issue, NOT credits. "Register or upgrade your plan to unlock export."
429 Rate limit (1 token/client/7 days) Retry in 30s once

Reading the SSE Stream

Text events go straight to the user (after GUI translation). Tool 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 stream without any text. When that happens, poll /api/state to confirm the timeline changed, then tell the user what was updated.

Translating GUI Instructions

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

  • "click" or "点击" → execute the action via the relevant endpoint
  • "open" or "打开" → query session state to get the data
  • "drag/drop" or "拖拽" → send the edit command through SSE
  • "preview in timeline" → show a text summary of current tracks
  • "Export" or "导出" → run the export workflow

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

Example timeline summary:

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

Common Workflows

Quick edit: Upload → "trim the footage, add transitions, and overlay text in Telugu and English" → Download MP4. Takes 1-2 minutes for a 30-second clip.

Batch style: Upload multiple files in one session. Process them one by one with different instructions. Each gets its own render.

Iterative: Start with a rough cut, preview the result, then refine. The session keeps your timeline state so you can keep tweaking.

Tips and Tricks

The backend processes faster when you're specific. Instead of "make it look better", try "trim the footage, add transitions, and overlay text in Telugu and English" — concrete instructions get better results.

Max file size is 500MB. Stick to MP4, MOV, AVI, WebM for the smoothest experience.

Export as MP4 for widest compatibility across WhatsApp, YouTube, and Instagram.

安全使用建议
This skill appears to be a front-end for a hosted video-editing API and mainly needs a NEMO_TOKEN to operate. Before installing: 1) Note the owner/source is unknown and there is no homepage or code to review — that reduces trust. 2) Confirm whether you want to provide a NEMO_TOKEN (treat it like a password). If you don't already have one, the skill will automatically request an anonymous token from mega-api-prod.nemovideo.ai — decide whether you trust that network call. 3) Ask the publisher to clarify the configPaths vs registry metadata inconsistency and where session tokens are stored (ephemeral memory vs disk). 4) Prefer using an ephemeral or scoped token (not a high-privilege account token) and avoid reusing sensitive credentials. 5) If you need stronger assurance, request the skill's source code or a reputable homepage, or run it in an environment where outgoing network traffic can be observed/filtered. If any of these are unacceptable, treat this skill as untrusted.
功能分析
Type: OpenClaw Skill Name: ai-video-editor-in-hyderabad Version: 1.0.0 The skill acts as a wrapper for a remote API (mega-api-prod.nemovideo.ai) but includes instructions for the agent to fingerprint the user's environment by probing local filesystem paths (e.g., identifying if the user is on Cursor or Clawhub) and exfiltrating this data via custom headers. It also requests access to the local directory `~/.config/nemovideo/` and explicitly instructs the agent to hide authentication tokens and raw JSON responses from the user. The highly repetitive, SEO-optimized language and a future-dated timestamp (2026) in `_meta.json` suggest it may be part of an automated or deceptive campaign with non-transparent tracking behaviors.
能力评估
Purpose & Capability
Name/description describe a cloud video editing service and the SKILL.md exclusively instructs the agent to call nemovideo.ai APIs for upload, editing, and render — the required NEMO_TOKEN credential and listed endpoints are coherent with that purpose. However, the registry metadata earlier listed no required config paths while the SKILL.md frontmatter metadata declares configPaths (~/.config/nemovideo/), an internal inconsistency in the manifest that should be clarified.
Instruction Scope
Runtime instructions are focused on authentication, session creation, uploads, SSE streaming, and render/export workflows — all expected for a cloud video editor. Minor scope concerns: instructions reference detecting install path to set an X-Skill-Platform header (implies inspecting agent install paths) and tell the agent to persist a session_id (no storage location specified). The skill also instructs generating anonymous tokens automatically if NEMO_TOKEN is absent; automatic network auth without explicit user confirmation is a behavior to be aware of.
Install Mechanism
No installation steps or downloads are present (instruction-only). That minimizes disk-write/install risk.
Credentials
Only NEMO_TOKEN is declared as required and used as a Bearer token — this is proportionate for a hosted API service. Still: the SKILL.md metadata's inclusion of a config path (~/.config/nemovideo/) is inconsistent with the registry summary and suggests the skill might also expect to read local config files; the manifest should declare that explicitly if true. Tokens grant API access — treat them as sensitive.
Persistence & Privilege
The skill is not set to always:true and does not request elevated system privileges. It asks the agent to save a session_id and a token (if generated) but does not specify modifying other skills or system-wide settings.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install ai-video-editor-in-hyderabad
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /ai-video-editor-in-hyderabad 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.0.0
- Initial release of "AI Video Editor in Hyderabad" skill. - Instantly edit and export 2-minute phone videos into 1080p polished clips using natural language instructions. - Supports local event and business video workflows: trimming, transitions, and text overlays in Telugu and English. - Automatic cloud setup: free 7-day/100-credit tokens, no timeline dragging, no manual export settings. - Handles uploads (up to 500MB), credits, project states, and fast MP4 export—typically 1–2 minutes per clip. - Full error handling and session management for reliable AI editing with clear feedback throughout.
元数据
Slug ai-video-editor-in-hyderabad
版本 1.0.0
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 1
常见问题

Ai Video Editor In Hyderabad 是什么?

Turn a 2-minute event recording shot on a phone in Hyderabad into 1080p polished edited clips just by typing what you need. Whether it's editing local event... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 66 次。

如何安装 Ai Video Editor In Hyderabad?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install ai-video-editor-in-hyderabad」即可一键安装,无需额外配置。

Ai Video Editor In Hyderabad 是免费的吗?

是的,Ai Video Editor In Hyderabad 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

Ai Video Editor In Hyderabad 支持哪些平台?

Ai Video Editor In Hyderabad 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 Ai Video Editor In Hyderabad?

由 susan4731-wilfordf(@susan4731-wilfordf)开发并维护,当前版本 v1.0.0。

💬 留言讨论