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francemichaell-15

Video Filters

by francemichaell-15 · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ✓ Security Clean
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Install in OpenClaw
/install video-filters
Description
Skip the learning curve of professional editing software. Describe what you want — apply a warm cinematic color filter to my video — and get filtered video c...
README (SKILL.md)

Getting Started

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

Try saying:

  • "add a 60-second lifestyle video clip into a 1080p MP4"
  • "apply a warm cinematic color filter to my video"
  • "applying color grades and visual effects to videos for TikTok 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.

Video Filters — Apply Filters and Export Videos

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

Say you have a 60-second lifestyle video clip and want to apply a warm cinematic color filter to my video — the backend processes it in about 20-40 seconds and hands you a 1080p MP4.

Tip: shorter clips under 30 seconds process significantly faster.

Matching Input to Actions

User prompts referencing video filters, 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.

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.

Headers are derived from this file's YAML frontmatter. X-Skill-Source is video-filters, 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).

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

Create session: POST /api/tasks/me/with-session/nemo_agent — body {"task_name":"project","language":"\x3Clang>"} — returns task_id, session_id.

Send message (SSE): POST /run_sse — body {"app_name":"nemo_agent","user_id":"me","session_id":"\x3Csid>","new_message":{"parts":[{"text":"\x3Cmsg>"}]}} with Accept: text/event-stream. Max timeout: 15 minutes.

Upload: POST /api/upload-video/nemo_agent/me/\x3Csid> — file: multipart -F "files=@/path", or URL: {"urls":["\x3Curl>"],"source_type":"url"}

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

Session state: GET /api/state/nemo_agent/me/\x3Csid>/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_\x3Cts>","sessionId":"\x3Csid>","draft":\x3Cjson>,"output":{"format":"mp4","quality":"high"}}. Poll GET /api/render/proxy/lambda/\x3Cid> 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.

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=\x3Cid>, 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

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

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.

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 → "apply a warm cinematic color filter to my video" → 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 "apply a warm cinematic color filter to my video" — 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.

Usage Guidance
This skill appears purpose-aligned and instruction-only. Before installing, confirm you are comfortable sending videos, audio, images, and prompts to the NemoVideo cloud API, and keep the NEMO_TOKEN private.
Capability Analysis
Type: OpenClaw Skill Name: video-filters Version: 1.0.0 The video-filters skill (SKILL.md) is a legitimate integration for the nemovideo.ai cloud service, enabling AI-driven video editing and filter application. It features a well-defined workflow for anonymous authentication, file uploads, and asynchronous processing via SSE. While it instructs the agent to inspect local filesystem paths (e.g., ~/.cursor/skills/) to determine the host platform for API attribution, this behavior is transparently documented and directly supports the skill's functionality without evidence of malicious intent or unauthorized data exfiltration.
Capability Assessment
Purpose & Capability
The described capabilities—uploading media, applying filters, rendering, exporting, checking status, and checking credits—fit the stated video-filtering purpose.
Instruction Scope
The routing instructions appear bounded to the documented NemoVideo upload/edit/status/export workflow, with no evidence of unrelated local commands or hidden behavior.
Install Mechanism
No install spec or code files are present; this is an instruction-only skill, and the static scanner reported no findings.
Credentials
The skill’s cloud workflow requires sending user media and prompts to an external API, which is expected for cloud video rendering but important for users with private footage.
Persistence & Privilege
The skill uses a NEMO_TOKEN bearer credential and session_id for cloud operations. This is purpose-aligned, and the artifact instructs not to expose tokens.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install video-filters
  3. After installation, invoke the skill by name or use /video-filters
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
video-filters 1.0.0 — Initial Release - Introduces cloud-based AI video filter application and export for MP4, MOV, AVI, WebM files up to 500MB. - Simple prompt-based workflow: upload videos, describe your filter or effect, receive processed video in 20–40 seconds. - Automatic setup and session creation using NEMO_TOKEN for 100 free credits and 7-day access. - Covers TikTok-friendly exports, color grading, visual effects, BGM, text overlays, and aspect ratio adjustments. - Includes comprehensive error handling, credits checking, and timeline/track management. - Optimized for creators who want easy, fast, stylized results without manual editing.
Metadata
Slug video-filters
Version 1.0.0
License MIT-0
All-time Installs 0
Active Installs 0
Total Versions 1
Frequently Asked Questions

What is Video Filters?

Skip the learning curve of professional editing software. Describe what you want — apply a warm cinematic color filter to my video — and get filtered video c... It is an AI Agent Skill for Claude Code / OpenClaw, with 54 downloads so far.

How do I install Video Filters?

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

Is Video Filters free?

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

Which platforms does Video Filters support?

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

Who created Video Filters?

It is built and maintained by francemichaell-15 (@francemichaell-15); the current version is v1.0.0.

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