Ai Text To Best

Skip the learning curve of professional editing software. Describe what you want — turn this text into a short video with visuals and voiceover — and get AI-...

Security Scan

The skill's stated purpose (cloud AI video generation) matches the network calls and single credential it requests, but metadata references a local config path that isn't explained in the runtime instructions — ask for clarification before installing.

Purpose & Capability

The name/description align with a cloud video-rendering backend and the SKILL.md consistently calls out the nemovideo.ai API and a NEMO_TOKEN bearer credential — this is coherent. However, the metadata lists a required config path (~/.config/nemovideo/) that is not referenced in the runtime instructions; that suggests the skill may read or write a local config directory (e.g., to store tokens) but the behavior is not documented.

Instruction Scope

The SKILL.md limits actions to creating/using a session on the nemovideo.ai API, uploading user-supplied files, streaming SSE messages, polling render status, and returning download URLs. It does not instruct the agent to read arbitrary system files or other credentials. It explicitly instructs not to expose tokens or raw API output.

Install Mechanism

There is no install spec or code to run; this is an instruction-only skill. That minimizes the risk of arbitrary code being written to disk.

Credentials

Only one env var (NEMO_TOKEN) is declared as required and is the expected credential for the described API. This is proportionate. Minor concerns: metadata also lists a config path (implying access to ~/.config/nemovideo/) and the skill asks the agent to 'auto-detect' an install path for X-Skill-Platform header—both imply additional filesystem inspection beyond just reading a single env var and should be documented/justified.

Persistence & Privilege

The skill is not always-enabled and does not request elevated platform privileges. Still, the declared config path suggests it might read or write under the user's home config directory; the SKILL.md does not explain whether session tokens or client IDs are persisted to disk, so verify persistence behavior before granting file access.

Assessment

This skill appears to be what it claims: a thin integration that calls a nemovideo.ai backend using a single NEMO_TOKEN credential. Before installing, ask the publisher to confirm: (1) why ~/.config/nemovideo/ is declared in metadata and whether the skill will read or write files there (and what it stores), (2) whether the anonymous token endpoint will ever cause persistent credentials to be written to disk, and (3) whether any uploaded files are forwarded to third parties beyond nemovideo.ai. If you plan to upload sensitive content, avoid using this skill until you confirm the provider's privacy/storage policy. Also consider running it in an environment that restricts filesystem access if you want to limit potential token persistence.

Getting Started

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

Try saying:

• "convert my text script"
• "export 1080p MP4"
• "turn this text into a short"

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.

AI Text to Video — Convert Text into Shareable Videos

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

Here's a typical use: you send a a 150-word product description or blog paragraph, ask for turn this text into a short video with visuals and voiceover, 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, cleaner text with one clear topic produces the most focused video output.

Matching Input to Actions

User prompts referencing ai text to best, 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 <NEMO_TOKEN>, X-Skill-Source, X-Skill-Version, X-Skill-Platform. Missing attribution headers will cause export to fail with 402.

Three attribution headers are required on every request and must match this file's frontmatter:

Header	Value
X-Skill-Source	ai-text-to-best
X-Skill-Version	frontmatter version
X-Skill-Platform	auto-detect: clawhub / cursor / unknown from install path

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.

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

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

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.

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)

Tips and Tricks

The backend processes faster when you're specific. Instead of "make it look better", try "turn this text into a short video with visuals and voiceover" — concrete instructions get better results.

Max file size is 500MB. Stick to TXT, DOCX, PDF, copied text for the smoothest experience.

Export as MP4 for widest compatibility across social platforms and email.

Common Workflows

Quick edit: Upload → "turn this text into a short video with visuals and voiceover" → 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.