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.

Install

If wonda is not found on PATH, install it first:

# npm
npm i -g @degausai/wonda

# Homebrew
brew tap degausai/tap && brew install wonda

Setup

• Auth: wonda auth login (opens browser, recommended) or set WONDA_API_KEY env var
• Verify: wonda auth check

Organizations & spend context

Wondercat orgs are shared wallets with their own seats and billing. Members can spend from the org wallet (instead of their personal credits) by switching context:

• wonda organizations list (aliases: wonda orgs list, wonda org list) — see every org you belong to with your role and seat plan in each.
• wonda use --org <slug> — sticky org context for this machine. Sets

X-Wonda-Org on every request; holds, charges, and wonda balance route through the org wallet.

• wonda use --personal — back to personal.
• wonda usage — spend-only usage summary (total + per-model + per-project

breakdown) for a period (--month 2026-05, or --from/--to; defaults to the current month, UTC). --project <name> restricts the report to one project. In org context it reports org-wide usage including a per-member breakdown — admin/owner role required. Admins can also download a full Excel report from the org page on the web.

Projects (spend tagging)

Projects attribute spend to a named workstream for monitoring. Agents should check the active project at task start (wonda use prints it) and set one per task when the operator monitors spend by project:

• wonda use --project <name> — sticky: every subsequent charge carries

the project (in wonda usage, the API, and the org Excel report). wonda use --no-project stops tagging; switching org/personal context clears the project automatically (projects are per-scope).

• --project <name> on any command — one-off override for that invocation.
• wonda project list|create|delete — manage the registry in the active

scope. Org projects are created by org admins/owners only; personal projects are self-service. Tagging against a name that doesn't exist fails with unknown_project (no silent new buckets, so typos can't split the monitoring data).

wonda topup always tops up your personal wallet, regardless of context. Topping up the org wallet (and configuring auto top-up) is admin-only and happens on the web at /organizations/<slug>. If a member runs out of org credits, the error tells them to ask an admin or switch back to personal — they cannot top up the org wallet from CLI.

Roles inside an org are separate from the seat plan:

• Owner: the original creator. Cannot be demoted or kicked. Can transfer ownership to another member from the org page (rare).
• Admin: can invite (single or bulk via paste), kick, change roles, change seats, top up, configure auto top-up, change monthly limits.
• User: can only spend within the org wallet (subject to a per-member monthly limit if the admin set one).

A paid org seat (WONDA / WONDA_PREMIUM) grants the same paid feature access (skills, etc.) as a personal paid plan, but only while in org context. wonda use --personal falls back to the user's personal account plan.

Access tiers

Wonda is paid-only: every product surface (generation, media, publishing, scraping, analysis, skills, cloud twin) requires a paid plan. New accounts get no credits and no product access until they subscribe.

If a command returns a 403 (paid_plan_required), subscribe at https://wonda.sh/account.

Voice cloning

Clone a voice from a 10s+ audio clip and use it in TTS. Hard limit: 20 cloned voices per account. Cost: $1.50 per clone.

# Clone from a local file (auto-uploads to media library first)
wonda voice create "Andu" --file ./sample.mp3 --description "My voice"

# Clone from existing wonda media
wonda voice create "Brand" --media-id <uuid>

# Optional source-audio preprocessing
wonda voice create "Clean" --file ./raw.wav --noise-reduction --normalize-volume

# List cloned voices (each row reports isExpired and expiresInDays)
wonda voice list

# One voice
wonda voice get <voice-id>

# Rename / re-describe (local only, no provider call)
wonda voice update <voice-id> --name "New Name" --description "..."

# Delete
wonda voice delete <voice-id>

Use a cloned voice in TTS by passing the providerVoiceId from voice get as voiceId to /audio/speech:

wonda audio speech "Hello world" \
  --model minimax-speech-2-8-hd \
  --params '{"voiceId":"<providerVoiceId>"}'

7-day expiry: cloned voices that haven't been used in TTS within 7 days are automatically expired. Running TTS with a cloned voice automatically refreshes its expiry. Idle voices that lapse must be re-cloned ($1.50 again).

Credentials vault

Persist logins created on external platforms (Instagram, TikTok, Twitter, etc.) so they can be reused on the next run. Passwords are AES-256-GCM encrypted with a server-side key and only decrypted on get.

# Create
wonda credentials create --website instagram.com --username myhandle \
  --email [email protected] --password-stdin <<< "hunter2" \
  --metadata '{"signup_source":"wonda-email"}'

# List (passwords omitted)
wonda credentials list --website instagram.com

# Get full record including decrypted password
wonda credentials get <id>

# Update any field (use --password-stdin to rotate; --username "" to clear)
wonda credentials update <id> --username newhandle

# Delete
wonda credentials delete <id>

# Fetch + record why you're using it in one call — POST, not GET, because
# it writes a 'used' event with the reason. Prefer this over `get` whenever
# you can articulate the reason.
wonda credentials use <id> --reason "instagram signup flow"

# See recent events (created / used / rotated / updated) for audit
wonda credentials events <id>

Fields: website (required — typed input like insta is canonicalized to instagram.com), username, email, password (required), metadata (arbitrary JSON). At least one of username / email must be present. Multiple records per (website, username) are allowed — dedupe on your side if you need to.

Event log: every credentials get/use, create, password rotate, and other updates are recorded as events on the credential (actor: cli | web | system). Use credentials events <id> or the web UI's history icon to audit. The event log is append-only and cascades on credential delete.

Global output flags

All commands support these output control flags:

• --json — Force JSON output (auto-enabled when stdout is piped)
• --quiet — Only output the primary identifier (job ID, media ID, etc.) — ideal for scripting
• -o <path> — Download output to file (implies --wait)
• --fields status,outputs — Select specific JSON fields
• --jq '.outputs[0].media.url' — Filter JSON output with a jq expression

CLI announcements & deprecation warnings

On every command the CLI polls GET /api/v1/updates (anonymous, 1h cache in ~/.wonda/state.json) for active announcements: deprecation notices, incident heads-ups, upgrade prompts. Messages are printed to stderr only, so stdout/JSON stays clean for piping.

Per-request deprecation hints arrive as the standard Warning: 299 - "<message>" HTTP header and are surfaced to stderr by the CLI's HTTP client as [deprecated METHOD /path] <message>.

Silence both channels with WONDA_QUIET=1 (env var) or --quiet (flag). Disable just the network checks with WONDA_NO_UPDATE_CHECK=1.

WAB / Wonda Automation Browser (wonda wab)

The Wonda Automation Browser (WAB) is a premium stealth antidetect browser, hardened so platforms cannot fingerprint it as automation. wonda wab is the one command for the antidetect Chromium stack (an undetected Playwright fork). It has two faces:

• Authenticated sessions. One persistent headful Chromium per persona that holds signed-in sessions for LinkedIn, X, Reddit, and friends. The CLI spawns it on demand, lets it idle out, and routes platform reads/writes through it whenever a command runs --via wab. Cookies live in the persona's Chromium profile, not in ~/.wonda/config.json.
• Anonymous capture. wonda wab record <url> (and wonda brand extract) drive an ephemeral Chromium with a fresh fingerprint, no persona, no cookies. See the record block below.

The mental model: you have accounts (one identity per platform). Each platform command routes to that account's cookies via either the flat JSON store (--via cookies, fast, no Chromium) or the account's persona (--via wab, live antidetect Chromium). A persona is the Chromium envelope that can hold multiple accounts under one fingerprint. In almost every case the persona is auto-created on first --via wab use, named after the account, so you never type a persona name.

Native login is the default for a new persona. wonda wab login <persona> <platform> opens a headful WAB window and you log in there. The session is minted INSIDE the WAB, so it is independent (logging out of the same account in an unrelated Chrome cannot revoke it) and the cookies are born under the WAB's own fingerprint, so session and browser identity stay coherent. A brand-new persona auto-created on first --via wab use chains straight into this flow on a TTY. Pasting cookies from another browser (wonda linkedin auth set, wonda x auth set, ...) still works and is the explicit fallback, but a hand-pasted li_at on a novel WAB fingerprint is the highest-risk shape.

wonda wab install                             # one-time: npm install + stealth-browser Chromium (shared by sessions, record, brand extract)
wonda wab start [account]                     # spawn (offscreen by default; --visible to show)
wonda wab stop [account]                      # graceful shutdown
wonda wab show [account]                       # peek a background WAB on-screen to watch it (suspends the macOS focus guard); starts it offscreen first if needed
wonda wab hide [account]                       # send a surfaced WAB back offscreen, resume silent background operation
wonda wab menubar                              # macOS menu-bar control (🐱): click to Show/Hide running WABs; --stop to remove
# macOS Dock menu: right-click a running WAB's Dock tile (the 🐱) for "Show on screen" / "Send to background" (same as wab show/hide). Each running persona has its own Dock tile and its menu controls only that persona. Opt out with WAB_DOCK_MENU=0.
# macOS: a background WAB no longer steals focus or flashes the menu bar / Dock when it opens a new tab; the Dock tile stays, it just never comes to the foreground until you `wab show` it.
wonda wab status                              # list personas + last activity
wonda wab login <account> <linkedin|x|reddit|instagram> # RECOMMENDED for a new persona: open headful window, user logs in, session minted in-WAB (independent + fingerprint-coherent)
wonda wab check <account> <linkedin|x|reddit|instagram> # non-interactive session-alive probe
wonda wab bind <persona> --x <acct> --reddit <acct> --linkedin <acct>  # multi-account power-user path: bind N accounts to ONE persona
wonda wab record <url>                        # anonymous one-shot capture (no account, no cookies), see below
wonda wab sync-cookies [account]              # force wab → disk cookie sync now (don't wait for the 10-min timer)
wonda wab logs [account] --tail 100           # tail driver.log (--audit for structured per-command log)
wonda wab errors --tail 20 --since 24h        # tail the cross-persona action-failure log
wonda wab top-failures --since 7d             # rank local WAB failures by platform/action/reason, joined with DOM recovery stats
wonda wab top-failures --platform x --json    # machine-readable local failure ranking
wonda wab bundle-failures list                # recent action failure bundles (one per failed run: screenshot, dom, visible-elements, cookies-summary REDACTED)
wonda wab bundle-failures show <id>           # print manifest + file tree for a bundle (id = unix-ms-ts prefix)
wonda wab bundle-failures ship <id>           # zip to ~/Downloads/wonda-failure-<id>.zip for sharing
wonda wab bundle-failures prune               # remove bundles older than 30d (or --max-per-persona, --all)
# Telemetry: on every wab action failure we report (action, platform, reason, error-string, has_bundle, cli_version) as a wab_action_failed PostHog event so maintainers can spot platform rotations across users. NO bundle contents, NO cookies, NO DOM, NO screenshots leave the user's machine. Opt out: WONDA_TELEMETRY_DISABLED=1. For server-side breakdowns, group `wab_action_failed` by `platform`, `action`, `reason`, and `has_bundle` in PostHog. Locally, `wonda wab top-failures` reads only `~/.wonda/wab/errors.jsonl` and persona-local `dom-recoveries.jsonl`, then shows count, last seen, recovered rate, bundle count, and a sample bundle id.
wonda wab migrate-legacy                      # copy a legacy WAB-driver profile into a persona slot
wonda wab restore <persona> [timestamp]       # restore from an hourly snapshot (--list to enumerate)
wonda wab backup disable                      # opt out of auto-push (on by default; existing cloud backups untouched)
wonda wab backup enable                       # opt back in (auto-push synced cookie JSON to wondercat after every disk sync)
wonda wab backup status                       # show config + remote inventory
wonda wab backup push [account]               # one-shot manual push for all platform bindings
wonda wab backup pull [account]               # guarded restore to ~/.wonda/<platform>-cookies/<account>.json; refuses non-empty local unless --force
wonda wab backup pull [account] --dry-run     # preview restores without writing
wonda wab backup list                         # inventory of cloud backups, including device/provenance metadata when available
wonda wab backup delete <plat> <persona> [acct] # remove one backup
wonda wab cookies list                        # explicit cookie backup inventory, metadata only
wonda wab cookies status [account]            # local cookie files plus cloud backup rows
wonda wab cookies port <plat> <persona> [acct] --from-device <id|label> # safely port one selected cloud row to this machine
wonda wab config set <persona> <key> <value>  # persist per-persona spawn defaults (idle-timeout, locale, visible, interactive, proxy_url, timezone, geo_lat/lon)
wonda wab config get <persona>                # print a persona's persisted config

Local browser proxy (`proxy_url`). By default the local WAB dials direct (your own IP). Set wonda wab config set <persona> proxy_url managed to route the LOCAL browser through your account's minted twin proxy, so it shares the same egress as the cloud twin (useful for IP continuity or a VPN/office/CGNAT network). A literal socks5://…/https://… value is a manual override instead; unset clears it back to direct. The proxy is optional: if minting is disabled for the environment or unavailable, the browser falls back to a direct dial.

Lifecycle commands take an --account (e.g. wonda wab login <account> linkedin); the persona is auto-derived from the account name. wonda wab bind is the one place a persona is named explicitly: use it when one Chromium must host accounts that have different names per platform.

Anonymous capture (`record`). wonda wab record <url> records a URL to webm in an ephemeral Chromium (fresh fingerprint each call, no persona, no cookies). Use it for cookie-banner-gated pages (Notion public shares, pdf.js renders, any site where bare Playwright trips a bot check) and marketing demo capture.

wonda wab record https://example.notion.site/page \
  --output recording.webm \
  --duration 5 \
  --viewport 960x1080 \
  --inject-js scripts/page-script.mjs   # optional: runs after load, before timer starts

# Transcode webm to mp4 at 30 fps (the stealth browser records webm/VP8)
ffmpeg -y -i recording.webm -t 5 -r 30 -an \
  -c:v libx264 -pix_fmt yuv420p -crf 18 recording.mp4

The --inject-js file is wrapped in an async IIFE so top-level await works. It runs AFTER domcontentloaded + networkidle + 400 ms paint settle, BEFORE the duration timer starts. Any await inside counts against the recording window. Use it for dark-theme injection, cookie-banner removal, scroll animations, anything that needs to happen in page context.

Node.js requirement: wonda needs Node >= v20 on PATH. Brew users get it via the node dependency; npm users have it by definition; install.sh users may need brew install node (or any Node distribution). If Node is missing, wonda wab install fetches a private copy into ~/.wonda/node/.

Cookie cloud backup. On by default (opt out per machine with wonda wab backup disable). The WAB driver pushes the synced cookie JSON for each bound platform to the wondercat backend after every wab → disk sync and graceful shutdown; auto-push no-ops when no api_key is configured. Encrypted at rest server-side (AES-256-GCM) when SOCIAL_COOKIES_KEY is set, else plaintext jsonb; the wire payload is always plaintext because the server holds the key. Cookie values are never printed by list/status/port commands.

Recovery is guarded. wonda wab backup pull <account> and wonda wab cookies port <platform> <persona> [account] refuse to overwrite a non-empty or newer local cookie file unless --force is passed. Forced writes create a hidden .before-pull-* backup first. Use --dry-run to inspect planned writes. When the backend exposes multiple device rows for the same platform/persona/account, use wonda wab cookies port ... --from-device <id|label> so the source row is explicit.

Current backend compatibility: legacy servers still expose one last-write-wins row per (account, platform, persona, account_label). Newer servers may include device_id, device_label, source, status, generation, and provenance; the CLI displays those fields when present and shows legacy rows as device legacy.

Source lives at cli/wondercat/wab/. The driver is launch.mjs and per-platform action scripts under actions/<platform>/.

Per-command transport (`--via`). linkedin, x, and reddit commands take:

• --via cookies|wab: cookies reads the flat per-account JSON store (fast, no Chromium); wab routes through the account's persona Chromium (cookies + TLS fingerprint inherit from a real browser session). An unsupported value errors loudly rather than silently downgrading.
• --via public: paid public-data API where a command explicitly supports it. For LinkedIn this avoids logged-in cookies and WAB profile reads, and uses the public scrape task route for wonda linkedin profile and wonda linkedin enrich.
• --account <name>: which on-disk identity to use (cookie filename / persona). Persona resolution is implicit: the first --via wab use auto-creates a persona named after the account and (on a TTY) chains straight into login.

Defaults differ for reads vs writes. Read commands (profile, posts, search, timeline, etc.) default to cookies (direct API), because that path is fast and detection-safe. Write / engagement commands (post, comment, like, follow, connect, message, mute, repost, delete) default to wab, because the cookie-API path triggers anti-abuse heuristics on LinkedIn / X / Reddit at any meaningful volume. Pass --via cookies to a write command if you explicitly want the legacy API path (where the command supports it).

Commands that require `--via wab`. A few commands have no cookie path and only run through the Wonda Automation Browser: wonda linkedin comment, wonda linkedin reply-comment, wonda linkedin mute, wonda linkedin follow, wonda linkedin edit-post, wonda linkedin edit-comment, wonda linkedin delete-comment, wonda linkedin post --media, wonda x delete, wonda x reply --attach, wonda x dm send, wonda x dm accept, and wonda x dm start. On these, the default already resolves to wab (one stderr line noting it); passing --via cookies explicitly errors. Reddit's writes (vote, comment, subscribe, save, unsave, delete, and subreddit submit) are likewise wab-only.

Per-account credentials. Cookies live in per-account JSON files on disk:

• ~/.wonda/x-cookies/<account>.json
• ~/.wonda/reddit-cookies/<account>.json
• ~/.wonda/linkedin-cookies/<account>.json (auto-migrated from the legacy single-file format)

Pass --account <name> to auth set to keep multiple logins side-by-side. The binding is recorded against the account's persona in account-bindings.json and, if the persona's Chromium is running, the rotated cookies get pushed into the live context. The driver also syncs cookies back to disk every 10 minutes (and on graceful shutdown), so rotated cookies (ct0 cycles, token_v2 server-side refresh, etc.) flow back to the cookies path without manual re-paste.

Action rate limits

Every platform command (linkedin, x, reddit, instagram), reads AND writes, runs through a per-profile rate-limit guard so a burst doesn't trip a platform's shadow-ban / anti-abuse heuristics. Accounting is per (platform, account) in a rolling 24h window, logged per profile under ~/.wonda/wab/personas/<persona>/ (so a cloud twin's caps persist across runs).

• Reads are _paced_, never blocked: spacing is jittered to keep a profile under read_per_min (75/min default), holding across separate invocations.
• Writes are checked against per-bucket daily caps. The LinkedIn defaults (other platforms track + count toward the total/day but have no per-type write cap by default):

Caps are SOFT by default: an over-safe / over-max action prints a shadow-ban-risk warning to stderr and proceeds. Pass --hard (or set mode: hard in config) to make over-cap writes abort (exit 1) instead.

wonda actions is a JSON data query (not a dashboard) for reading a profile's rolling-24h usage vs caps on demand; the caps/pacing/warnings run silently in the live hook regardless.

wonda actions                        # rolling-24h usage per profile vs caps, as JSON
wonda actions --persona <persona>        # one profile
wonda actions --platform linkedin    # filter to one platform
wonda actions sync                   # flush local action/health events to your Wonda account
wonda actions sync --persona <persona>   # flush one profile's ledgers
wonda linkedin post "…" --hard       # enforce caps as hard limits for this command

When an API key is configured, the local ledgers (actions log, WAB audit/error logs, cookie provenance) also sync to your Wonda account-health record automatically in the background on every command: best-effort, batched, and idempotent (a stable client event id per record means retries never double count), so offline use keeps working and sync catches up later. wonda actions sync forces a full flush and prints the server's insert/dedup counts; without an API key it is a silent no-op. Only event metadata travels, never cookie values or failure bundles. WONDA_TELEMETRY_DISABLED=1 turns the background sync off.

Override / disable / hard-mode via ~/.wonda/config.json under action_limits (caps are clamped to safety floors/ceilings so an override can loosen but not silently disable the guard):

{
  "action_limits": {
    "mode": "hard",
    "read_per_min": 75,
    "total_per_day": 100,
    "buckets": { "linkedin": { "outreach": { "safe": 15, "max": 30 } } }
  }
}

Config keys

wonda config get|set|list keys:

• api-key: your wondercat API key.
• base-url: API base (defaults to prod, set to https://staging.api.wondercat.ai for staging).
• default-account: account used when a platform command doesn't pass --account.
• wab-backup-enabled: true/false for cookie cloud backup (same as wonda wab backup enable/disable). On by default; only an explicit false disables it.

Transport is NOT a config key. Each command picks it per kind (reads default to cookies, writes / engagement default to wab), identically on every platform. Override it per command with --via cookies|wab (where the platform supports it).

How to think about content creation

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

1. What product category? (beauty, food, tech, fashion, fitness, etc.)
2. What format performs for this category? (UGC memes for everyday products, cinematic for luxury, before/after for transformations, testimonial for services)
3. What's the hook? (relatable scenario, surprising twist, aspirational lifestyle, social proof)
4. 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 <brand-id>                                    # 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/<domain>/ dir to the server
wonda brand save --from ./output/stripe.com --make-active
wonda brand pull <brand-id>                                    # Download a saved brand back to ./output/<domain>/
wonda brand activate <brand-id>                                # Set as the active brand
wonda brand upload-logo <brand-id> https://acme.com/logo.svg   # Attach a logo by URL (--variant wordmark|icon|dark|light)
wonda brand upload-font <brand-id> https://acme.com/Geist.woff2 --weight 700
wonda brand delete <brand-id>
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 <slug>                          # Pull a skill's full step-by-step guide live to stdout

<!-- SKILLS_TABLE_START -->

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

video

image

social-research

strategy

utility

<!-- SKILLS_TABLE_END -->

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 <md> / stdin)
wonda skill diff <slug>                          # See what changed in the default since you forked it (drift)
wonda skill refactor <slug> --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 <mediaId> -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.

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 <number>               — 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 <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 <jobId>                        # 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 c