Trimmer In

v1.0.0

Turn a 3-minute interview recording with long pauses into 1080p trimmed video clips just by typing what you need. Whether it's cutting unwanted sections from...

⭐ 0· 52·0 current·0 all-time

by@mhogan2013-9

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-in.

Previewing Install & Setup.

Prompt PreviewInstall & Setup

Install the skill "Trimmer In" (mhogan2013-9/trimmer-in) from ClawHub.
Skill page: https://clawhub.ai/mhogan2013-9/trimmer-in
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-in

ClawHub CLI

Package manager switcher

npx clawhub@latest install trimmer-in

Security Scan

VirusTotal

Pending

View report →

OpenClaw

Suspicious

medium confidence

ℹ

Purpose & Capability

The skill claims to send user video to a cloud rendering backend (nemovideo.ai) and the SKILL.md contains the exact endpoints and flows to do that, so required env var NEMO_TOKEN is appropriate. However the SKILL.md frontmatter lists a required config path (~/.config/nemovideo/) while the registry metadata lists no required config paths — this mismatch is unexplained.

ℹ

Instruction Scope

Instructions are explicit about token acquisition, session creation, SSE streaming, uploads and polls — all consistent with a cloud trimming service. They also instruct reading the skill's YAML frontmatter and detecting the agent install path (e.g., ~/.clawhub/, ~/.cursor/skills/) to create attribution headers. Probing install paths and reading frontmatter are out-of-band filesystem accesses relative to the stated task and should be validated.

✓

Install Mechanism

This is an instruction-only skill with no install spec or code to write to disk, which is the lowest install risk.

ℹ

Credentials

Requesting a single NEMO_TOKEN is proportionate for a cloud service. The skill also implements an anonymous-token flow (POST to mega-api-prod.nemovideo.ai) if NEMO_TOKEN isn't present; that behavior is reasonable but means the skill can obtain a token itself. The earlier configPath inconsistency is a minor concern.

✓

Persistence & Privilege

The skill does not request always:true, does not modify other skills, and only maintains an ephemeral session token for render jobs. Autonomous invocation is allowed by default (normal) but does increase blast radius if the backend or skill were malicious.

What to consider before installing

This skill uploads your raw video/audio to an external service (mega-api-prod.nemovideo.ai). Before installing or using it: (1) confirm the service owner and read a privacy/security policy — there is no homepage or source link provided; (2) decide whether you trust a third party with your footage (sensitive content should not be uploaded until you verify policies); (3) ask the publisher to explain the configPath discrepancy (~/.config/nemovideo/ present in SKILL.md but not in registry metadata) and why the skill probes install paths; (4) if you require transparency, request the skill's source code or a known release host for the backend. If you proceed, prefer supplying your own NEMO_TOKEN (vs. allowing anonymous-token issuance) and avoid uploading highly sensitive materials until the backend identity and policies are confirmed.

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

Runtime requirements

✂️ Clawdis

EnvNEMO_TOKEN

Primary envNEMO_TOKEN

latestvk970jk0qve9wbykegbyqwxp4n1852vwt

52downloads

0stars

1versions

Updated 1w ago

v1.0.0

MIT-0

Getting Started

Got raw video footage 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 3-minute interview recording with long pauses into a 1080p MP4"
"trim the intro silence and cut out the filler sections in the middle"
"cutting unwanted sections from video recordings for content creators"

Quick Start Setup

This skill connects to a cloud processing backend. On first use, set up the connection automatically and let the user know ("Connecting...").

Token check: Look for NEMO_TOKEN in the environment. If found, skip to session creation. Otherwise:

Generate a UUID as client identifier
POST https://mega-api-prod.nemovideo.ai/api/auth/anonymous-token with X-Client-Id header
Extract data.token from the response — this is your NEMO_TOKEN (100 free credits, 7-day expiry)

Session: POST https://mega-api-prod.nemovideo.ai/api/tasks/me/with-session/nemo_agent with Bearer auth and body {"task_name":"project"}. Keep the returned session_id for all operations.

Let the user know with a brief "Ready!" when setup is complete. Don't expose tokens or raw API output.

Trimmer In — Trim and Export Clean Videos

Drop your raw video footage 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 3-minute interview recording with long pauses, ask for trim the intro silence and cut out the filler sections in the middle, 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 source clips process faster and give more precise trim results.

Matching Input to Actions

User prompts referencing trimmer in, 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":"<lang>"}`. 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/<sid>`	POST	Upload a file (multipart) or URL.
`/api/credits/balance/simple`	GET	Check remaining credits (`available`, `frozen`, `total`).
`/api/state/nemo_agent/me/<sid>/latest`	GET	Fetch current timeline state (`draft`, `video_infos`, `generated_media`).
`/api/render/proxy/lambda`	POST	Start export. Body: `{"id":"render_<ts>","sessionId":"<sid>","draft":<json>,"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.

Skill attribution — read from this file's YAML frontmatter at runtime:

X-Skill-Source: trimmer-in
X-Skill-Version: from frontmatter version
X-Skill-Platform: detect from install path (~/.clawhub/ → clawhub, ~/.cursor/skills/ → cursor, else unknown)

Include Authorization: Bearer <NEMO_TOKEN> and all attribution headers on every request — omitting them triggers a 402 on export.

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=<id>` (get `<id>` 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.

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 field mapping: t=tracks, tt=track type (0=video, 1=audio, 7=text), sg=segments, d=duration(ms), m=metadata.

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 intro silence and cut out the filler sections in the middle" → 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.

Tips and Tricks

The backend processes faster when you're specific. Instead of "make it look better", try "trim the intro silence and cut out the filler sections in the middle" — 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.

Comments

Loading comments...