Trimmer Online Free

v1.0.0

Get trimmed video clips ready to post, without touching a single slider. Upload your video clips (MP4, MOV, AVI, WebM, up to 500MB), say something like "trim...

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Install

OpenClaw Prompt Flow

Install with OpenClaw

Best for remote or guided setup. Copy the exact prompt, then paste it into OpenClaw for mhogan2013-9/trimmer-online-free.

Previewing Install & Setup.
Prompt PreviewInstall & Setup
Install the skill "Trimmer Online Free" (mhogan2013-9/trimmer-online-free) from ClawHub.
Skill page: https://clawhub.ai/mhogan2013-9/trimmer-online-free
Keep the work scoped to this skill only.
After install, inspect the skill metadata and help me finish setup.
Required env vars: NEMO_TOKEN
Use only the metadata you can verify from ClawHub; do not invent missing requirements.
Ask before making any broader environment changes.

Command Line

CLI Commands

Use the direct CLI path if you want to install manually and keep every step visible.

OpenClaw CLI

Bare skill slug

openclaw skills install trimmer-online-free

ClawHub CLI

Package manager switcher

npx clawhub@latest install trimmer-online-free
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OpenClawOpenClaw
Benign
medium confidence
Purpose & Capability
Name, description, and runtime behavior align: the skill uploads user video files to a cloud rendering API and manages sessions/exports. The single required credential (NEMO_TOKEN) is appropriate for a hosted service.
Instruction Scope
SKILL.md stays focused on upload/trim/export workflows and session handling. It instructs the agent to upload user-provided video files, create sessions, poll render status, and return download URLs. It also instructs generating and using an anonymous token if NEMO_TOKEN is absent. Two noteworthy points: (1) it instructs deriving X-Skill-Platform by inspecting install paths (e.g., ~/.clawhub/ or ~/.cursor/skills/) which requires reading the agent's filesystem/home paths, and (2) it instructs keeping technical details out of chat (operational). Both are explainable for attribution and UX, but the install-path probe is a broader file-system read than strictly required for trimming.
Install Mechanism
Instruction-only skill with no install spec and no code files — nothing is written to disk by an installer. This is the lowest-risk install model.
Credentials
Only NEMO_TOKEN is declared as required and used as the bearer token for the nemovideo API; that is proportionate. The SKILL.md metadata also references a config path (~/.config/nemovideo/) even though registry metadata listed no required config paths — minor inconsistency. The skill will access user-provided files for upload (expected) and may read the agent's home/install paths to set an attribution header (see instruction_scope).
Persistence & Privilege
always is false and the skill is user-invocable; it does not request permanent/system-wide presence or modification of other skills. Session tokens live on the backend (render jobs can be orphaned if the client disconnects) but there is no evidence the skill modifies agent configs or other skills.
Assessment
This skill appears to be what it says: a cloud-based video trimmer that needs a nemovideo token. Before installing or using it, consider: (1) Privacy — your videos will be uploaded to https://mega-api-prod.nemovideo.ai; do not upload sensitive material unless you trust that service. (2) Token handling — provide a dedicated NEMO_TOKEN for this skill (avoid reusing high-privilege keys). If you don't provide one, the skill requests an anonymous token from the vendor. (3) Filesystem reads — the skill may inspect install/home paths to set an attribution header; if you are uncomfortable with that, ask for that behavior to be removed. (4) Source verification — there is no homepage or known source listed; if provenance matters, try to obtain publisher details or a public repository before relying on it. Overall the footprint is proportionate to the stated purpose, but exercise caution around data privacy and provenance.

Like a lobster shell, security has layers — review code before you run it.

Runtime requirements

✂️ Clawdis
EnvNEMO_TOKEN
Primary envNEMO_TOKEN
latestvk970e086t8haba99r6kv19dvfx84xxm2
69downloads
0stars
1versions
Updated 1w ago
v1.0.0
MIT-0

Getting Started

Got video clips to work with? Send it over and tell me what you need — I'll take care of the AI video trimming.

Try saying:

  • "trim a 10-minute raw interview recording into a 1080p MP4"
  • "trim the first 2 minutes and cut the last 30 seconds of dead air"
  • "cutting unwanted parts from video recordings for content creators"

Getting Connected

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

If NEMO_TOKEN is in the environment, use it directly and create a session. Otherwise, acquire a free starter token:

  • Generate a UUID as client identifier
  • POST to https://mega-api-prod.nemovideo.ai/api/auth/anonymous-token with the X-Client-Id header
  • The response includes a token with 100 free credits valid for 7 days — use it as NEMO_TOKEN

Then create a session by POSTing to https://mega-api-prod.nemovideo.ai/api/tasks/me/with-session/nemo_agent with Bearer authorization and body {"task_name":"project","language":"en"}. The session_id in the response is needed for all following requests.

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

Trimmer Online Free — Trim and Export Video Clips

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

Here's a typical use: you send a a 10-minute raw interview recording, ask for trim the first 2 minutes and cut the last 30 seconds of dead air, and about 20-40 seconds later you've got a MP4 file ready to download. The whole thing runs at 1080p by default.

One thing worth knowing — shorter clips process faster, so split long videos before uploading.

Matching Input to Actions

User prompts referencing trimmer online free, aspect ratio, text overlays, or audio tracks get routed to the corresponding action via keyword and intent classification.

User says...ActionSkip 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.

Headers are derived from this file's YAML frontmatter. X-Skill-Source is trimmer-online-free, 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 <NEMO_TOKEN> plus the three attribution headers above. If any header is missing, exports return 402.

API base: https://mega-api-prod.nemovideo.ai

Create session: POST /api/tasks/me/with-session/nemo_agent — body {"task_name":"project","language":"<lang>"} — returns task_id, session_id.

Send message (SSE): POST /run_sse — body {"app_name":"nemo_agent","user_id":"me","session_id":"<sid>","new_message":{"parts":[{"text":"<msg>"}]}} with Accept: text/event-stream. Max timeout: 15 minutes.

Upload: POST /api/upload-video/nemo_agent/me/<sid> — file: multipart -F "files=@/path", or URL: {"urls":["<url>"],"source_type":"url"}

Credits: GET /api/credits/balance/simple — returns available, frozen, total

Session state: GET /api/state/nemo_agent/me/<sid>/latest — key fields: data.state.draft, data.state.video_infos, data.state.generated_media

Export (free, no credits): POST /api/render/proxy/lambda — body {"id":"render_<ts>","sessionId":"<sid>","draft":<json>,"output":{"format":"mp4","quality":"high"}}. Poll GET /api/render/proxy/lambda/<id> every 30s until status = completed. Download URL at output.url.

Supported formats: mp4, mov, avi, webm, mkv, jpg, png, gif, webp, mp3, wav, m4a, aac.

SSE Event Handling

EventAction
Text responseApply GUI translation (§4), present to user
Tool call/resultProcess internally, don't forward
heartbeat / empty data:Keep waiting. Every 2 min: "⏳ Still working..."
Stream closesProcess 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 saysYou 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)

Error Codes

  • 0 — success, continue normally
  • 1001 — token expired or invalid; re-acquire via /api/auth/anonymous-token
  • 1002 — session not found; create a new one
  • 2001 — out of credits; anonymous users get a registration link with ?bind=<id>, registered users top up
  • 4001 — unsupported file type; show accepted formats
  • 4002 — file too large; suggest compressing or trimming
  • 400 — missing X-Client-Id; generate one and retry
  • 402 — free plan export blocked; not a credit issue, subscription tier
  • 429 — rate limited; wait 30s and retry once

Tips and Tricks

The backend processes faster when you're specific. Instead of "make it look better", try "trim the first 2 minutes and cut the last 30 seconds of dead air" — 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 → "trim the first 2 minutes and cut the last 30 seconds of dead air" → Download MP4. Takes 20-40 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.

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