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susan4731-wilfordf

Free Video Learn

by susan4731-wilfordf · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ⚠ suspicious
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Install in OpenClaw
/install free-video-learn
Description
Get structured learning videos ready to post, without touching a single slider. Upload your video clips (MP4, MOV, AVI, WebM, up to 500MB), say something lik...
README (SKILL.md)

Getting Started

Share your video clips and I'll get started on AI video tutoring. Or just tell me what you're thinking.

Try saying:

  • "convert my video clips"
  • "export 1080p MP4"
  • "break this video into chapters with"

First-Time Connection

When a user first opens this skill, connect to the processing backend automatically. Briefly let them know (e.g. "Setting up...").

Authentication: Check if NEMO_TOKEN is set in the environment. If it is, skip to step 2.

  1. Obtain a free token: Generate a random UUID as client identifier. POST to https://mega-api-prod.nemovideo.ai/api/auth/anonymous-token with header X-Client-Id set to that UUID. The response data.token is your NEMO_TOKEN — 100 free credits, valid 7 days.
  2. Create a session: POST to https://mega-api-prod.nemovideo.ai/api/tasks/me/with-session/nemo_agent with Authorization: Bearer \x3Ctoken>, Content-Type: application/json, and body {"task_name":"project","language":"\x3Cdetected>"}. Store the returned session_id for all subsequent requests.

Keep setup communication brief. Don't display raw API responses or token values to the user.

Free Video Learn — Turn Videos Into Learning Lessons

This tool takes your video clips and runs AI video tutoring through a cloud rendering pipeline. You upload, describe what you want, and download the result.

Say you have a 3-minute tutorial screen recording and want to break this video into chapters with titles and a summary — the backend processes it in about 30-60 seconds and hands you a 1080p MP4.

Tip: shorter clips under 5 minutes produce the most accurate chapter breakdowns.

Matching Input to Actions

User prompts referencing free video learn, 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 free-video-learn, 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).

Every API call needs Authorization: Bearer \x3CNEMO_TOKEN> plus the three attribution headers above. If any header is missing, exports return 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

SSE Event Handling

Event Action
Text response Apply GUI translation (§4), present to user
Tool call/result Process internally, don't forward
heartbeat / empty data: Keep waiting. Every 2 min: "⏳ Still working..."
Stream closes Process final response

~30% of editing operations return no text in the SSE stream. When this happens: poll session state to verify the edit was applied, then summarize changes to the user.

Backend Response Translation

The backend assumes a GUI exists. Translate these into API actions:

Backend says You do
"click [button]" / "点击" Execute via API
"open [panel]" / "打开" Query session state
"drag/drop" / "拖拽" Send edit via SSE
"preview in timeline" Show track summary
"Export button" / "导出" Execute 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)

Tips and Tricks

The backend processes faster when you're specific. Instead of "make it look better", try "break this video into chapters with titles and a summary" — 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.

Common Workflows

Quick edit: Upload → "break this video into chapters with titles and a summary" → Download MP4. Takes 30-60 seconds 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.

Usage Guidance
This skill implements a cloud rendering pipeline: using it will upload any videos you provide to https://mega-api-prod.nemovideo.ai and will either use a NEMO_TOKEN you supply or automatically request an anonymous token on your behalf. Before installing or using: 1) Confirm you trust the remote service and are comfortable uploading the video content (do not send sensitive/confidential videos). 2) Note the SKILL.md mentions reading install paths and a local config (~/.config/nemovideo/); ask the publisher if the skill will read files from that location and why. 3) The package metadata is inconsistent (registry shows no config paths but SKILL.md lists one); prefer skills with clear provenance or source code you can inspect. 4) If you want tighter control, provide your own NEMO_TOKEN (rather than letting the skill auto-create one) and monitor network activity. If you cannot verify the backend or author, treat this as potentially privacy-sensitive and proceed cautiously.
Capability Analysis
Type: OpenClaw Skill Name: free-video-learn Version: 1.0.0 The 'free-video-learn' skill is a video processing utility designed to automate chaptering and editing via the 'nemovideo.ai' backend. The SKILL.md file provides clear, task-aligned instructions for the AI agent to manage authentication (using a NEMO_TOKEN), handle file uploads, and process streaming responses (SSE). It includes security-conscious instructions to avoid displaying raw tokens or API responses to the user and limits its operations to the specified service domain. No indicators of data exfiltration, malicious execution, or harmful prompt injection were found.
Capability Assessment
Purpose & Capability
The skill is a cloud-based video editing pipeline and asks only for a NEMO_TOKEN credential, which is consistent with using a third‑party API. However the SKILL.md YAML includes a configPaths entry (~/.config/nemovideo/) while the registry metadata shown earlier listed no required config paths — that mismatch is unexplained and could indicate stale or inconsistent packaging.
Instruction Scope
Instructions direct the agent to upload user video files to https://mega-api-prod.nemovideo.ai, obtain or reuse a bearer token, create sessions, stream SSE, and poll render status. Those network actions are consistent with the described purpose, but the skill also: (a) automatically generates anonymous tokens without explicit user consent when NEMO_TOKEN is absent, and (b) derives an X-Skill-Platform header from an install path (mentions ~/.clawhub/ and ~/.cursor/skills/) and includes a configPaths value, implying the agent may read local paths to determine installation context — behavior not justified by the description and potentially privacy-relevant.
Install Mechanism
No install spec or code files are present (instruction-only). That lowers risk because nothing is downloaded or written by the skill during install. All runtime behavior is network calls described in SKILL.md.
Credentials
Only NEMO_TOKEN is declared as required, which is proportionate for a hosted video API. But the skill simultaneously instructs the agent to auto-request an anonymous token if none is present (effectively creating credentials), and the SKILL.md metadata references a local config path (~/.config/nemovideo/) not declared elsewhere — together these raise ambiguity about whether the skill may read local config files or expect persistent local credentials beyond the single declared env var.
Persistence & Privilege
always is false and the skill does not request system-wide changes or higher privileges. It stores a session_id for requests (expected), and there is no install-time binary or persistent service implied by the package.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install free-video-learn
  3. After installation, invoke the skill by name or use /free-video-learn
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
Initial release of Free Video Learn — Turn Videos Into Learning Lessons. - Upload video (MP4, MOV, AVI, WebM) and get structured, chaptered learning videos in 1080p MP4 format. - Automatic session creation, cloud-based video editing, and export pipeline; no editing software needed. - Uses NEMO_TOKEN for authentication, with a simple process to get 100 free credits valid for 7 days. - Supports user prompts to break videos into chapters, add titles, summaries, and more. - Robust error and SSE event handling for smooth workflow and user-friendly feedback. - Clear documentation on common actions, supported formats, file limits, and tips for best results.
Metadata
Slug free-video-learn
Version 1.0.0
License MIT-0
All-time Installs 0
Active Installs 0
Total Versions 1
Frequently Asked Questions

What is Free Video Learn?

Get structured learning videos ready to post, without touching a single slider. Upload your video clips (MP4, MOV, AVI, WebM, up to 500MB), say something lik... It is an AI Agent Skill for Claude Code / OpenClaw, with 47 downloads so far.

How do I install Free Video Learn?

Run "/install free-video-learn" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is Free Video Learn free?

Yes, Free Video Learn is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does Free Video Learn support?

Free Video Learn is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created Free Video Learn?

It is built and maintained by susan4731-wilfordf (@susan4731-wilfordf); the current version is v1.0.0.

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