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wonda-cli

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by degausai · part of degausai/wonda

Using the Wonda CLI to generate images, videos, music, and audio from the terminal — plus LinkedIn, Reddit, and X/Twitter research and automation

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🧰 Not standalone. This skill ships with degausai/wonda and only works together with that tool — install the tool first, then add this skill.

This is the playbook your agent receives when the skill activates — you don't need to read it to use the skill, but it's here to audit before installing.

by degausai

Using the Wonda CLI to generate images, videos, music, and audio from the terminal — plus LinkedIn, Reddit, and X/Twitter research and automation npx skills add https://github.com/degausai/wonda --skill wonda-cli Download ZIPGitHub133

Wonda CLI

Wonda CLI is a content creation toolkit for terminal-based agents. Use it to generate images, videos, music, and audio; edit and compose media; publish to social platforms; and research/automate across LinkedIn, Reddit, and X/Twitter.

How to think about content creation

You are a marketing director with access to a full production toolkit. Before touching any tool, think:

  • What product category? (beauty, food, tech, fashion, fitness, etc.)

  • What format performs for this category? (UGC memes for everyday products, cinematic for luxury, before/after for transformations, testimonial for services)

  • What's the hook? (relatable scenario, surprising twist, aspirational lifestyle, social proof)

  • What specific scene? (not "product on table" but "person discovering the product in a funny situation")

Decision flow

When asked to create content, follow this order:

Step 1: Gather context

wonda brand # Active brand: identity, colors, fonts, logos, products
wonda brand list # All brands owned by this account/org
wonda brand show # Specific brand
wonda brand extract https://stripe.com # Local-only: writes ./output/stripe.com/{DESIGN.md, tokens.json, assets/}
wonda brand extract https://stripe.com --save --make-active # Local + persist + activate (the common path)
wonda brand extract https://stripe.com --save --name "Stripe" # Persist with a custom name
wonda brand extract https://stripe.com --no-output --save # Don't write to disk, persist only
wonda brand save # Persist the most recent ./output/ / dir to the server
wonda brand save --from ./output/stripe.com --make-active
wonda brand pull # Download a saved brand back to ./output/ /
wonda brand activate # Set as the active brand
wonda brand upload-logo https://acme.com/logo.svg # Attach a logo by URL (--variant wordmark|icon|dark|light)
wonda brand upload-font https://acme.com/Geist.woff2 --weight 700
wonda brand delete 
wonda analytics instagram # What content performs well
wonda scrape social --handle @competitor --platform instagram --wait # Competitive research (if relevant)

# Cross-platform research (if relevant)
wonda x search "topic OR keyword" # Find conversations on X/Twitter
wonda x user-tweets @competitor # Competitor's recent tweets
wonda reddit search "topic" --sort top --time week # Reddit discussions
wonda reddit feed marketing --sort hot # Subreddit trends
wonda linkedin search "topic" --type COMPANIES # LinkedIn company/people research
wonda linkedin profile competitor-vanity-name # LinkedIn profile intel

Step 2: Check content skills

Content skills are step-by-step guides for common content types. Each skill tells you exactly which models, prompts, and editing operations to use — and in what order. ALWAYS check skills before building from scratch.

Skills are server-hosted, per-account, and editable (the same model as wonda brand save) — you don't download a folder of .md files you own. Wonda ships a canonical set of default skills, served read-only as the fallback. Pull them live for a task; fork a default into your own copy when you want to tweak it; Wonda keeps full version history. skill list shows your effective skills (defaults overlaid with your own edits), and flags any fork whose default has since changed.

wonda skill list # Browse your effective skills (defaults + your own); forks with a changed default are flagged
wonda skill get # Pull a skill's full step-by-step guide live to stdout

Default skill catalog (live source: wonda skill list, which also shows your own forks/edits and flags drift):

video

Slug What it does product-demo-video Premium ~15s multi-beat product demo, a dev hits a pain point, runs your tool, it does something real on screen, the payoff lands, then a branded CTA, built as one HTML composite captured per-frame and muxed with ffmpeg product-video Product/scene video from an image or from scratch split-screen-demo 5-second 16:9 LinkedIn loop, source doc to designed slides comparison ending on a CTA that crosses out the competitors tiktok-ugc-pipeline Reverse-engineer a viral reel, generate 5 variations, auto-post ugc-dance-motion Dance and motion transfer video from image + reference ugc-hook-brainstorm 25 graded scroll-stopping UGC hooks, hot casting, iPhone 16 aesthetic, psychological levers ugc-reaction-batch Batch produce TikTok-native UGC reaction videos ugc-talking Talking-head UGC ad, single clip, two-angle PIP, or long-form 20s+

image

Slug What it does creative-static-ads High-converting single-frame static ads, 6 conversion pillars, 8 format archetypes, 8 psychological hooks linkedin-media-premium-generation Render a single stop-the-scroll LinkedIn hero card (1920x1080) using a layered visual vocabulary: italic-serif headline, real product screenshot, paper grain, floating social-proof cards. Six layout patterns sharing the same DNA. Wonda CLI sourcing for real quotes from X / LinkedIn / Reddit. premium-static-ads Pixel-perfect HTML+Playwright static ads with brand extraction (wonda brand extract). Real fonts, exact tokens, reproducible templates tiktok-slideshow-carousel 3-5 slide TikTok carousel that looks organic but promotes your product, hook, bridge, reveal

social-research

Slug What it does analyze-reel Analyze a viral reel or TikTok, viral breakdown + 5 adapted content ideas linkedin-engager-intel Pull every commenter + reactor on a LinkedIn post with profile URLs for warm outreach linkedin-icp-qualify Enrich LinkedIn engagers with their current employer (industry, headcount, HQ, description) so you can filter for ICP fit linkedin-social-listening Paste any keyword, pull every recent post that mentions it, then enrich each post's engagers into an ICP-qualified outreach shortlist reddit-subreddit-intel Scrape top posts, analyze virality patterns, generate post ideas twitter-influencer-search Find micro-influencers and amplifiers for product launches

strategy

Slug What it does linkedin-post-system Turn a raw idea into a LinkedIn post that matches proven formats and the user's exact voice. Bootstraps the user's voice corpus via wonda linkedin profile/posts, maps the idea to one of 28 proven content formats, drafts 2 variants with anti-AI-slop guardrails. marketing-brain Strategy brain for hooks, visuals, ads, and competitive analysis

utility

Slug What it does extract-apply-style Extract a visual style from any image, then generate new subjects in that style ffmpeg Local deterministic media transforms, trim, replace audio, burn captions, social formatting, scene splitting, silence cut, frame extraction, analysis artifacts image-edit Edit existing images, img2img, background removal, crop, text overlay, vectorize slide-generation Generate branded slide decks from any content source, codebase, Notion notes, or Google Docs software-ui-mockups Render real software UIs, terminal/CLI TUIs, the Chrome browser window, the macOS desktop, pixel-accurately in HTML for demo videos, slides, screenshots and docs, from the program's real source of truth tiktok-caption-presets TikTok-style textOverlay and animatedCaptions presets applied via wonda edit --preset

Editing skills (optional). When a default doesn't quite fit, fork and edit it instead of working around it. Editing a default forks it into your account automatically:

wonda skill create my-ugc --from ugc-talking # Fork a default into your own editable copy
wonda skill edit my-ugc --editor # Record a new version (opens $EDITOR; or --file / stdin)
wonda skill diff # See what changed in the default since you forked it (drift)
wonda skill refactor --editor # Re-base your fork onto the updated default, clears the drift hint

If a skill matches → wonda skill get <slug>, read it, adapt to context, execute each step.

If no skill matches → build from scratch (Step 3).

Step 2.5: Decide whether finishing should be local

Not every media task should go back through Wonda editing. Use this routing rule:

  • Use wonda for AI generation, AI transcription/alignment, scraping, publishing, hosted transitions, and workflows that need media IDs or remote jobs.

  • Use local ffmpeg for deterministic transforms on files you already have or can download: trim, crop/scale/pad, concat (merging multiple clips), replace audio, extract audio/frame, reverse, normalize for delivery, burn captions, split scenes, cut silence, and build analysis artifacts. Always merge clips locally — server-side merge can hang for 30+ minutes once any input exceeds ~7MB.

When a task starts from a Wonda media ID but the actual edit is deterministic, move it to local files first:

wonda media download -o ./input.mp4

Before any local ffmpeg work:

which ffmpeg
which ffprobe
ffmpeg -version
ffprobe -v error -show_format -show_streams -of json ./input.mp4

Font rule for local caption/text work:

  • Prefer an explicit font file path over a family name.

  • Never assume a font exists. Check first with fc-match, fc-list, /System/Library/Fonts, /Library/Fonts, ~/Library/Fonts, or /usr/share/fonts.

  • If the task is mainly local finishing/captions/formatting/splitting/artifact extraction, check the ffmpeg skill before inventing commands.

  • wonda edit video runs a local ffmpeg for every editor op: trim, crop, volume, speed, reverseVideo, extractFrame, extractAudio, editAudio, imageCrop, imageToVideo, merge, overlay, splitScreen, splitScenes, skipSilence. The render runs on your machine via ffmpeg: no server-side editor_job and no credit hold for the render itself (inputs are downloaded and the result uploaded around it). textOverlay and animatedCaptions also run locally, via the bundled hyperframes (Chromium) renderer. ffmpeg must be on PATH (wonda doctor verifies). The public API /video/edit, /image/edit, /audio/edit are no longer used for these and return 410 Gone.

  • Always merge clips locally. Server-side merge can hang for 30+ minutes once any input exceeds ~7MB, and wonda edit video --operation merge now runs in local ffmpeg by default for the same reason.

  • Never mix per-clip audio then concat. Concat the video tracks first, then layer the full voiceover or music track once over the joined timeline. Per-clip audio bakes create cut-line collisions and silent gaps.

Default local export target unless the user asked otherwise:

-c:v libx264 -preset medium -crf 18 -pix_fmt yuv420p -movflags +faststart -c:a aac -b:a 192k

Always pass -y as the first flag so the command auto-overwrites the output. ffmpeg prompts interactively when the output path exists and agent shells hang on that prompt until timeout.

Step 2.6: Pick the right local tool

Editing maps to one of four tools. Pick the first row that matches.

Need Tool Why Primitive transform (trim, crop, speed, merge, overlay, ...) wonda edit video --operation <op> Wraps local ffmpeg. Free, deterministic, renders on your machine (no server render, no credits). Motion graphics, animated text, lower thirds, intro/outro wonda compose <kind> (hyperframes HTML compositions, local render) One-shot, no Lambda, no Node bundled into wonda. Requires Node >= 22 + ffmpeg. Kinetic captions, branded effects pipelines, scene FX wonda transitions run --preset <name> (miruna's transitions service) Hosted; richer effect library (SAM3 masking, scene transitions, caption presets). One-off raw transform not covered by a primitive Raw ffmpeg via Bash (see the ffmpeg skill) Faster than picking a wrong primitive; matches "deterministic transform on local files". Complex multi-step pipeline Chain the above (wonda edit ... → raw ffmpeg → wonda compose ...) Each step writes a local mp4; pass it as --input / --media to the next.

Run wonda doctor once on a new machine to confirm ffmpeg, node, and hyperframes are all available. Pass --warm-chrome to pre-fetch hyperframes' bundled Chromium (~150 MB) so the first clipping render doesn't pause to download it.

Examples:

Primitive trim and merge (wonda edit, local ffmpeg):

wonda edit video --operation trim --media $VID \
 --params '{"trimStartMs":3000,"trimEndMs":10000}' \
 --wait -o ./trimmed.mp4

wonda edit video --operation merge --media $A,$B,$C \
 --wait -o ./merged.mp4

Motion graphics intro (wonda compose, hyperframes):

wonda compose motion --template fade-in \
 --text "Q4 Recap" --subtitle "Wondercat" \
 --duration 4 --resolution portrait -o intro.mp4

wonda compose text --input ./clip.mp4 --text "NEW DROP" \
 --position bottom-center -o overlay.mp4

Kinetic captions on a finished clip (transitions service):

wonda transitions run --media $VID --preset caption_word_pop --wait -o final.mp4

Raw ffmpeg for an op no primitive covers (e.g. concat with audio fade out):

ffmpeg -y -f concat -safe 0 -i list.txt \
 -af "afade=out:st=29:d=1" \
 -c:v libx264 -crf 18 -pix_fmt yuv420p \
 -c:a aac -b:a 192k out.mp4

Multi-step pipeline (compose intro → wonda merge with main → transitions captions):

wonda compose motion --template scale-pop --text "Hello" --duration 3 -o intro.mp4
wonda edit video --operation merge --media $(wonda media upload intro.mp4 --quiet),$MAIN_VID \
 --wait -o merged.mp4
MERGED_ID=$(wonda media upload merged.mp4 --quiet)
wonda transitions run --media $MERGED_ID --preset caption_word_pop --wait -o final.mp4

Step 3: Build from scratch (chain endpoints)

When no skill matches, chain individual CLI commands. Each step produces an output that feeds into the next.

Single asset:

wonda generate image --model gpt-image-2 --prompt "..." --aspect-ratio 9:16 --wait -o out.png
# --params '{"quality":"high"}' — auto/low/medium/high (default auto)
# --negative-prompt "..." — override what to exclude (model-dependent)
# --seed — pin the seed for reproducible results (model-dependent)
wonda generate video --model seedance-2 --prompt "..." --duration 5 --params '{"quality":"high"}' --wait -o out.mp4
wonda generate text --model --prompt "..." --wait
wonda generate music --model suno-music --prompt "upbeat lo-fi" --wait -o music.mp3

Audio (speech, transcription, dialogue):

# List available voices (TTS + dialogue use the same set)
wonda audio voices

# Text-to-speech
wonda audio speech --model elevenlabs-tts --prompt "Your script here" \
 --params '{"voiceId":"hpp4J3VqNfWAUOO0d1Us"}' --wait -o speech.mp3
# elevenlabs-tts always requires a voiceId — pick one from `wonda audio voices`

# Transcribe audio/video to text
wonda audio transcribe --model elevenlabs-stt --attach $MEDIA --wait

# Multi-speaker dialogue (each speaker needs a voiceId from `wonda audio voices`)
wonda audio dialogue --model elevenlabs-dialogue \
 --prompt 'ALICE: Hi! BOB: Hello!' \
 --params '{"speakers":[{"label":"ALICE","voiceId":"hpp4J3VqNfWAUOO0d1Us"},{"label":"BOB","voiceId":"IKne3meq5aSn9XLyUdCD"}]}' \
 --wait -o dialogue.mp3

Audio AI operations (direct-inference, NOT editor ops):

# Denoise / dereverberate speech
wonda audio enhance --model replicate-resemble-enhance --attach $MEDIA \
 --params '{"denoise":true,"chunkSeconds":10}' --wait -o enhanced.wav

# Split a track into voice and instrumental stems
wonda audio extract-voice --model replicate-demucs --attach $MEDIA \
 --wait -o vocals.wav

Add animated captions to a video:

The animatedCaptions operation handles everything in one step — it extracts audio, transcribes for word-level timing, and renders animated word-by-word captions onto the video.

# Generate a video with speech audio
VID_JOB=$(wonda generate video --model seedance-2 --prompt "..." --duration 5 --aspect-ratio 9:16 --params '{"quality":"high"}' --wait --quiet)
VID_MEDIA=$(wonda jobs get inference $VID_JOB --jq '.outputs[0].media.mediaId')

# Add animated captions (single step)
wonda edit video --operation animatedCaptions --media $VID_MEDIA \
 --params '{"fontFamily":"TikTok Sans SemiCondensed","position":"bottom-center","sizePercent":80,"strokeWidth":2.5,"fontSizeScale":0.8,"highlightColor":"rgb(252, 61, 61)"}' \
 --wait -o final.mp4

The video's original audio is preserved. Do NOT replace the audio with TTS — Sora already generated the speech.

Transitions (effects pipelines on a single video):

wonda transitions presets # List built-in presets (JSON)
wonda transitions operations # Grouped by category (analysis/effect/...)
wonda transitions operations --json # Full per-param metadata
wonda transitions llms # Full reference (presets + ops + dependencies)
wonda transitions run --media $VID --preset flash_glow --wait -o out.mp4
# Or send an agent-generated timeline of clips (inline JSON):
wonda transitions run --media $VID \
 --clips '[{"layer_type":"video","start_frame":0,"end_frame":60}]' --wait -o out.mp4
# Or from a file (handy for long agent timelines):
wonda transitions run --media $VID --clips ./timeline.json --wait -o out.mp4
# To attach scene_transitions: pass an envelope (clips + scene_transitions)
# instead of a bare clip array — same file, both fields forwarded.
wonda transitions run --media $VID --clips ./timeline_with_transitions.json --wait -o out.mp4
# where timeline_with_transitions.json is:
# { "clips": [...],
# "scene_transitions": [{"name":"crossfade","params":{"duration":8},"boundaries":[60]}] }
wonda transitions job # Poll a transition job

Use exactly one of --preset or --clips. Requires a full (logged-in) account. Always read wonda transitions llms first when composing a clips timeline. It documents the detect/segment/effect dependencies, which ops need masks, and the full clip-spec shape (layer types, tracks, effects, transforms).

Preset variables (variables block). Each preset declares the template variables it accepts under variables in wonda transitions presets. Each entry has name, description, and required. Required variables MUST be supplied or the job is rejected with a 400 — no more silent skipping. Pass them with --var name=value (repeatable) or, for the common prompt case, the --prompt shortcut:

# flash_glow_prompted requires { prompt }
wonda transitions run --media $VID --preset flash_glow_prompted \
 --prompt "woman in white dress" --wait -o out.mp4

# text_behind_person requires { prompt, text }
wonda transitions run --media $VID --preset text_behind_person \
 --var prompt="the person" --var text="HELLO WORLD" --wait -o out.mp4

# Numeric-typed vars: bare digits are decoded as numbers, "true"/"false" as
# bools, everything else stays a string. Presets that compare frame indices
# numerically (border_frame, marquee_text, quick_motion_text, bg_remove_scale)
# need this — quoting an int turns it back into a string.
wonda transitions run --media $VID --preset border_frame \
 --var exit_start_frame=200 --var exit_end_frame=251 --wait -o out.mp4

The prompt variable is a detection text query describing which subject to mask, fed to SAM3 to produce per-frame segmentation masks. Not a content-generation prompt.

Building a custom --clips timeline that needs detection masks? Add a clip with layer_type: "video" and a mask: {layer_type: "mask", analysis_steps: [{name: segment, params: {prompt: "..."}}]}. SAM3 handles both detection and segmentation in one step from the prompt, so no separate detect step is needed.

Pre-warming masks before render (recommended)

For presets with mask:<label> variables, run wonda transitions ensure-masks first so the render starts with masks already prepared. The first call for a (media, label) pair takes 1-3 minutes; subsequent calls are near-instant.

# 1. Ensure masks are prepared for the labels you'll use, blocking until ready.
wonda transitions ensure-masks --media $VID --labels person,phone --wait

# 2. Run the render. Masks are already prepared.
wonda transitions run --media $VID --preset slide_reflect_background \
 --var "masks=mask:person+phone" --wait -o out.mp4

ensure-masks flags:

  • --media MEDIA_ID — required, the video the masks are for

  • --label NAME — repeatable, one label per call (--label person --label phone)

  • --labels NAME,NAME — comma-separated alternative (--labels person,phone)

  • --wait — block until every label is prepared

  • --timeout DUR — cap wait time when --wait is set (default 10m)

Multi-prompt syntax: mask:woman+phone in --var is split into separate masks (woman, phone) and unioned per-frame. Pass each sub-label separately to ensure-masks so all of them are pre-warmed.

When to skip ensure-masks:

  • Non-mask presets (no mask:<label> variables) — nothing to prepare

  • A previous render already used these (media, labels) — already prepared

When ensure-masks matters most:

  • First render of a new media with mask-based presets

  • Iterating params on a render — pre-warm once, then run as many times as you want without re-preparing

Multi-scene presets (requiresMultiScene: true). Some presets use scene-aware logic and expect a video with multiple cuts/scenes. Check requiresMultiScene in wonda transitions presets. If true, feeding a single continuous shot will produce only one scene and the effect may look underwhelming. Combine clips first or use a video with natural cuts.

Tweaking preset params. Every preset is clip-shape. Pull a single preset with wonda transitions preset <name> --json, read its clips: (single-track) or tracks: (multi-track) field, edit any clip param, and submit as --clips. For multi-track presets, flatten by giving each clip a track index drawn from the track it came from. If the preset declares sceneTransitions:, pass that array through unchanged on the request.

# Single-track preset (e.g. flash_glow_montage): copy clips: directly
wonda transitions preset flash_glow_montage --json | jq '.preset.clips' > clips.json
# edit clips.json
wonda transitions run --media $VID --clips "$(cat clips.json)" --wait -o out.mp4

Auto-repair safety net (--auto-repair, --face-bbox). For --clips renders the worker runs a deterministic repair pass on the submitted JSON before rendering, default on. Repairs: width-fit font clamp, descender clamp against canvas bottom, stack-spacing snap (ROW1_py from cap-height formula), keyframe-bound clamp to [0, source_duration], same-y-row caption overlap trim, mask full-duration extension, stroke-width zeroing, letter-spacing target snap per font, mask-cutout duration extension, negative-start clamp, and (with --face-bbox) face-overlap caption shift. Pass --auto-repair=false for strict validation; out-of-spec values then surface as render errors.

# Push body captions off the speaker's face. bbox is x1,y1,x2,y2 in canvas pixels (top-left origin).
wonda transitions run --media $VID --clips ./timeline.json \
 --face-bbox 200,160,520,520 --wait -o out.mp4

# Strict mode — disable auto-repair to see exactly which clips fail validation.
wonda transitions run --media $VID --clips ./timeline.json \
 --auto-repair=false --wait -o out.mp4

--face-bbox only shifts body captions. Decorative text you want behind the speaker still routes through an explicit mask_cutout {prompt: "person"} clip.

Output URL paths differ by job type:

  • Inference jobs (generate, audio): .outputs[0].media.url and .outputs[0].media.mediaId

  • Editor jobs (edit): .outputs[0].url and .outputs[0].mediaId

Model waterfall

Image

Default: gpt-image-2. OpenAI's flagship — strongest prompt adherence, best text-in-image, high-fidelity edits via reference images. Handles 1-4 reference images. Quality tiers: auto (default), low, medium, high — pass via --params '{"quality":"high"}'. Caps at 1536px output.

For img2img editing specifically (change, add/remove, restyle, bg-remove, crop, text overlay, vectorize), use wonda skill get image-edit — it has the full edit-specific decision tree.

Pick something else only when one of these applies:

  • User explicitly requests another model

  • More than 4 reference images → nano-banana-2 (gpt-image-2 caps at 4 refs; nano-banana-2 accepts up to 14). For 1-4 refs, stay on gpt-image-2.

  • Need vector output → runware-vectorize

  • Need background removal → birefnet-bg-removal

  • Cheapest possible / fastest drafts → z-image

  • Need >1536px / true 4K output → nano-banana-pro (1K/2K/4K) or nano-banana-2 (1K/2K/4K). gpt-image-2 caps at 1536px.

  • gpt-image-2 unavailable / OpenAI down → nano-banana-2 or seedream-4-5 or grok-imagine-pro

Video

Default: seedance-2 (duration 5/10/15s, default 5s, quality: high). Escalation:

  • Quality complaint or different style → sora2 or sora2pro

  • Max single-clip duration is 15s for Seedance 2, 20s for Sora → for longer content, stitch multiple clips via merge

  • Veo (veo3_1, veo3_1-fast) is available but NOT in the default waterfall. Only pick Veo when the user explicitly asks for Veo by name.

  • Gemini Omni (gemini-omni-video) is available but NOT in the default waterfall. Only pick it when the user asks for Gemini by name, or specifically needs multi-image reference T2V/I2V (up to 7 reference images) or 4K output.

Image-to-video routing (MANDATORY when attaching a reference image):

  • Person/face visible in the reference image → MUST use kling_3_pro (preserves identity better for faces)

  • No person in reference image → use seedance-2

  • Text-to-video (no reference image): Seedance 2 generates people fine. This rule ONLY applies when you --attach an image.

Kling model family:

  • kling_3_pro — Text-to-video and image-to-video, supports start/end images, custom elements (@Element1, @Element2), 3-15s duration, 16:9/9:16/1:1

  • kling_2_6_pro — General purpose, 5-10s, 16:9/9:16/1:1, text-to-video and image-to-video

  • kling_2_6_motion_control — Motion transfer: requires both a reference image AND a reference video, recreates the video's motion with the image's appearance

  • kling2_5-pro — Budget Kling option, 5-10s, supports first/last frame images

Kling prompt rules (important): Kling's prompt field caps at 2,500 characters and Kling responds poorly to Sora-style structured briefs (SCENE: / SUBJECT: / MOTION: / BANNED LOOK: section headers). In that format Kling latches onto atmosphere nouns and silently drops the central subject (verified empirically: the same 2,842-char Sora-style prompt that rendered correctly on Sora 2 Pro and Seedance 2 produced no phone at all on Kling — even when trimmed to 2,250 chars). When escalating Seedance → Kling, or targeting Kling directly, rewrite the prompt as short natural-language prose (~1,000–1,500 chars) and lead with the hero subject in the opening sentence rather than burying it inside a SUBJECT: block. Do NOT pass a Sora-formatted prompt through to Kling unchanged.

Other video models:

  • grok-imagine-video — xAI video generation, 5-15s, supports 7 aspect ratios including 4:3 and 3:2

  • gemini-omni-video: Google Gemini Omni. Text-to-video and image-to-video with up to 7 reference images (slots reference_image_1 through reference_image_7). Durations 4/6/8/10s, aspect ratios 9:16 and 16:9, resolutions 720p / 1080p / 4K. Pricing: $0.15 base + $0.075/s at 720p/1080p, $0.75 base + $0.075/s at 4K. No native audio (pair with a separate audio model if speech is needed).

  • topaz-video-upscale — Upscale video resolution (1-4x factor, supports fps conversion)

  • sync-lipsync-v2-pro — Legacy lipsync for user-supplied video + audio pairs. Inferior to native-audio generation and almost never the right choice for new content. See the "Lip sync" section for rules.

Seedance family (DEFAULT video model, watermarks automatically removed):

  • seedance-2 — Base Seedance 2.0 (T2V/I2V, 5-15s, high=standard/basic=fast)

  • seedance-2-omni — Multi-reference generation (images, audio refs)

  • seedance-2-video-edit — Edit existing video via text prompt

Video durations: Accepted --duration values vary by model. Check with wonda capabilities or wonda models info <slug>.

Audio

  • Music: suno-music (set --params '{"instrumental":true}' for no vocals)

  • Text-to-speech: elevenlabs-tts — only for explicit narrator/voice-over asks over silent footage. Do NOT use to "make a UGC character talk" — Sora / Sora 2 Pro / Veo 3.1 / Kling 3 / Seedance 2 generate native synced speech in any language, which looks and sounds far better. Always set voiceId in params. Default female voice: --params '{"voiceId":"21m00Tcm4TlvDq8ikWAM"}' (Rachel).

  • Transcription: elevenlabs-stt

  • Multi-speaker dialogue: elevenlabs-dialogue

  • Enhance audio (clean up noisy speech): replicate-resemble-enhance via wonda audio enhance — denoise + dereverberate. Use when a voice recording sounds muffled, echoey, or has background noise. NOT a general "sounds better" button; if the source is already clean this can soften it.

  • Extract voice (isolate vocals / split stems): replicate-demucs via wonda audio extract-voice — splits into voice and instrumental tracks. Use to pull a speaker or singer off a track, or to isolate the music behind a vocal.

Native synced speech (preferred over TTS + lipsync): Sora, Sora 2 Pro, Veo 3.1, Kling 3, and Seedance 2 all generate dialogue in any language directly inside the video, with mouth movements baked in. Put the line (and language) in the video model's --prompt. Never chain elevenlabs-tts → sync-lipsync-v2-pro to fake speech over a silent generation.

Characters

Characters are reusable saved combos (image + optional voice audio) you can mention in prompts with @name. The server auto-injects the image, optional face video, and audio into the right slots for the selected model. Works on Kling 3 Pro (start_image + element_1 + voice_audio) and Seedance 2 Omni (ref_image_1 + ref_video_1 + ref_audio_1). Name rules: must start with a letter, 1–31 chars, alphanumeric + _/-.

Provider gotchas (Seedance 2 Omni): when a character is mentioned, the API routes Seedance to MuAPI automatically. Replicate enforces a 15s ref_audio_1 cap and rejects famous-celebrity refs with E005 — input flagged as sensitive. MuAPI is the reliable path for character-driven jobs. Even on MuAPI, top-tier celebrity refs (think Sydney Sweeney, Leonardo DiCaprio) are blocked with "Face detected in uploaded image. Please use an image without real people." Non-celebrity faces and lesser-known public figures pass cleanly. If you see that error on a real-person ref, use Kling 3 Pro instead (its character pipeline runs voice cloning server-side, so the raw face audio never touches a moderation classifier).

From a Kling clip — extract a frame + voice from a generation you like:

VID=$(wonda generate video --model kling_3_pro --prompt "young man, grey tshirt, talking to camera" --wait --quiet)
VID_MEDIA=$(wonda jobs get inference $VID --jq '.outputs[0].media.mediaId')
wonda character from-media alex --source $VID_MEDIA --frame-ms 2500
wonda generate video --model kling_3_pro --prompt "@alex welcomes viewers to the channel" --wait -o alex-welcome.mp4

From scratch — generate a portrait and a TTS sample, then bind them:

IMG=$(wonda generate image --model nano-banana-2 --prompt "young woman, studio portrait" --wait --quiet)
IMG_MEDIA=$(wonda jobs get inference $IMG --jq '.outputs[0].media.mediaId')
AUD=$(wonda audio speech --model elevenlabs-tts --prompt "Hi, this is me" --params '{"voiceId":"21m00Tcm4TlvDq8ikWAM"}' --wait --quiet)
AUD_MEDIA=$(wonda jobs get inference $AUD --jq '.outputs[0].media.mediaId')
wonda character create maya --image $IMG_MEDIA --audio $AUD_MEDIA

List / inspect / update / delete: wonda character list, wonda character get <name>, wonda character update <name> --audio $NEW, wonda character delete <name>. Only one character with audio can be referenced per generation.

Prompt writing rules

Follow this waterfall top-to-bottom. Use the FIRST matching rule and stop.

PASSTHROUGH — If the user says "use my exact prompt" / "verbatim" / "no enhancements" → copy their words exactly. Zero modificatio