synthesia

The avatar-video tool skill — have a reason for an avatar, use consented likeness only, make the script spoken-word, assemble + localize, note the disclosure. The agent scripts and plans; the human approves every video; WoopSocial publishes. (Ships with tools/integrations/synthesia.md.)

The POV: the presenter is synthetic — the standards stay human

Synthesia is the enterprise avatar category leader: one locked script becomes a consistent presenter in 140+ languages with no re-shoots, which makes it unbeatable for training, onboarding, explainers, and localization at scale. The top-1% operator holds four lines. (1) The fit test comes first: avatars read polished-but-clinical — they lose to a real face for trust-led founder content and testimonials (route those to talking-head-and-piece-to-camera); the pro move is the hybrid — the founder films the trust layer, the avatar scales the informational layer. (2) Consent is the architecture, not friction: stock avatars are paid consenting actors; a personal avatar requires your live consent recording on an unspliced single-take source — and nobody gets an avatar of a competitor, celebrity, or anyone who hasn't verifiably consented. (3) The script is most of avatar quality — and it locks before render: spoken-word writing (short sentences, SSML, read aloud), because a comma-level edit forces a full ~8–12-minute re-generation off the minute cap. (4) Disclosure, always: a synthetic presenter is labeled — platform AI tags and the EU AI Act's synthetic-media obligations make undisclosed avatars a channel-level risk.

Read these first

1. The content skill (educational-content-and-how-to / short-form-video-script) — the material.
2. brand-profile + voice-builder (the script's voice) + design-and-templates (brand kit).

The framework: HUMAN

(Depth: references/the-human-framework.md.)

• H — Have a reason for an avatar: training/explainers/localization/faceless = yes; founder-trust/ testimonials/emotional persuasion = no (real face); regulated medical topics = caution (moderation delays).
• U — Use consented likeness only: live consent recording for personal avatars; consented stock actors; never impersonation or fake endorsements; governance on sharing (use ≠ edit; voice separate).
• M — Make the script spoken-word: short sentences, contractions, SSML, closer framing; lock copy before generating (re-render trap); read it aloud first.
• A — Assemble scenes + localize: brand kit + template once; AI Playground B-roll (use the Veo 3.1 lane — OpenAI ends the Sora API Sept 24 2026, so the Sora 2 lane is dying); 1-click translation/dubbing → native-speaker QA per language; one master, tracked variants.
• N — Note the disclosure + publish: platform AI labels + EU AI Act + C2PA; human approves; export → capcut if needed → scheduling-and-queue → WoopSocial.

The reality (verify-quarterly)

Synthesia 3.0 (Oct 2025): Express-2 engine (full-body, gestures, micro-expressions, 1080p/30fps, no length cap), Video Agents (real-time conversational; Enterprise), AI Playground (embedded B-roll — launched with Sora 2 + Veo 3.1; the Sora API sunsets Sept 24 2026, so treat Veo as the durable lane), Interactivity 2.0, AI Dubbing, Copilot, doc/PPT→video, SSML, ~real-time rendering, 39+ subtitle languages. Consent architecture (from Synthesia's docs): consented stock actors; personal avatars via live consent recording on a single-take source; deepfakes/impersonation prohibited; SOC 2 Type II + GDPR + ISO 42001/27701 + C2PA membership; moderation over-flags regulated content (12–24h reviews reported). Tiers ≈ Free 10 min/mo · Starter $18–29 (~120 min/yr) · Creator $64–89 (~360 min/yr, API, voice cloning) · Enterprise custom (unlimited, SCORM, 1-click translation, Video Agents); minutes don't roll over; non-refundable annual; custom avatars ≈ $1,000/yr; comma-level edits force full re-renders (~8–12 min). Honest boundary: clinical for emotional content; HeyGen reads more TikTok-native. Attribute all; verify-quarterly. Full detail: references/synthesia-2026-reality.md; the fit table, script pattern, localization chain, plan-math worksheet, and worked examples: references/fit-and-templates.md.

Honest scope (never violate)

• The agent runs the fit test, writes locked spoken-word scripts, plans scenes/localization/QA, and drives the API where connected (exact human steps otherwise; no unreviewed auto-publish); the human approves every video (no fabricated "that looks natural"; native-speaker QA per language); WoopSocial publishes the exports — it does not generate avatars; LMS/SCORM distribution is the human's.
• Consent spine: no avatar of anyone without verified consent; no celebrities/competitors; no fake endorsements. Disclosure always (platform labels; EU AI Act; C2PA). YMYL: avatar delivery doesn't lower the claim bar; regulated topics budget moderation-review time. Never fabricate tiers, gates, render times, or capabilities. (Full scope: references/scope-and-connections.md.)

Distinct from its siblings (route correctly)

synthesia (this) = the enterprise/L&D/localization avatar lane · heygen = the creator/social-native lane (test both; state trade-offs) · talking-head-and-piece-to-camera = the real human (trust content routes there; the hybrid is the pro move) · ai-video / luma / veo-3 / kling = cinematic footage, no presenter (the AI Playground embeds two of them for B-roll) · ai-voiceover / elevenlabs = voice-only · descript = editing recordings (this generates the presenter) · capcut = post-render captions/pace.

Where this connects

Reads first: the content skill + brand-profile + voice-builder + design-and-templates. Feeds: capcut, the platform publishing skills, email-and-newsletter (embedded explainers), lead-magnets-and-funnels (course video). Publishes via: export → scheduling-and-queue → WoopSocial (social); the LMS (training — human). Tool file: tools/integrations/synthesia.md. Measure with: native + analytics-and-reporting on completion/watch-through — never fabricated.

Definition of done

Avatar video that passed the fit test first (training/explainers/localization/faceless = avatar; trust-led founder content routed to a real face; the hybrid split applied where both exist), built on consented likeness only (stock actors or the owner's live-consent personal avatar; no impersonation or fake endorsements), scripted as locked spoken-word (short sentences, SSML, read aloud, signed off before a single render — no re-render burn), assembled on the brand kit with honest B-roll and localized through the chain (master → 1-click translation/dubbing → native-speaker QA per language → tracked variants), and published disclosed (platform AI labels; EU AI Act; C2PA) via WoopSocial after human approval, with plan math done honestly (minute caps, no rollover, Enterprise gates, $1,000/yr custom avatars, moderation-review buffer for regulated topics); no undisclosed synthetic presenters, no unconsented likeness, no fabricated tiers/capabilities; and correctly distinguished from heygen, talking-head-and-piece-to-camera, ai-video/luma, ai-voiceover, and descript.