Avatar Content Engine UGC - OpenClaw

Generate emotionally-driven, platform-native 16-second AI-avatar video scripts, images, and clips for TikTok, Reels, Shorts, and Facebook focused on niche or...

Avatar Content Engine

runtime (OpenClaw, Claude Code, and others), and works on OpenClaw, and also through PaioClaw.ai. fill the [BRAND] slots with your own product. Runs on any AgentSkills-compatible

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UAIGC Avatar Engine- Create Viral Content (avatar-content-engine)

• Operate an AI-avatar short-form content engine — script, image, and video production for social-video avatars across TikTok, Reels, Shorts, and Facebook.
• Use for avatar scripts, hooks, micro-moments, emotional arcs, brand vs niche content modes, avatar portrait generation, 2×2 reference grids, and Seedance / Kling / Wan lip-sync clips.
• Trigger even when the request only names an avatar, a TikTok/Reel, a portrait, or a content pipeline — do not fall back to generic content advice when this pipeline applies.

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A production system for generating emotionally-driven, platform-native short-form video featuring AI avatars. It exists to solve one bottleneck: creative supply. The value is velocity of distinct, high-quality creative — the raw material that ad and recommendation algorithms need to optimize against — not a standalone acquisition channel. Anchor every decision here.

The engine spans three stages:

1. Script generation — structured 16s two-clip scripts.
2. Image generation — consistent avatar portraits and reference grids.
3. Video generation — clip production, voice, and multi-clip continuity.

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1. Core content philosophy

These principles override generic "best practice." Content that violates them reads as hollow even when technically correct.

• Emotion first, information last. The goal is to mirror the viewer's exact internal state, not to inform them. A script should make a real person think "this is me" before it makes them think "I learned something." Push back on safe, fact-heavy drafts.
• Channel division over repetition. Voice, on-screen text, and visuals must each carry a different layer — emotion / the concrete point / the proof. When all three repeat the same thin message, the content feels empty. Specificity across channels is what makes sub-30s video feel substantial.
• Specificity is the substance. "A show" is dead; a named episode is alive. "A game" is dead; "your ping showing 20ms but the server feels like 300ms" is alive. Never generalize a micro-moment.
• Payload discipline inside the format. Each 8s clip carries roughly 18–22 words of dialogue and must land one concrete payload — a specific point, image, or proof — not filler. Word count is a symptom; the real fix is a defined payload per clip.

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2. The avatars (example roster — customize)

Define a small roster of fixed personas. Each needs a locked persona, voice, target audience, brand slot, and two content banks (a niche bank for no-brand content, a brand bank for product-integrated content). Respect each avatar's voice and audience exactly — voice drift is a known failure mode.

Avatar	Brand slot	Persona	Voice (niche)	Core audience
Nova	[BRAND]	Gaming	Deadpan, self-aware Gen-Z gamer; dry humor; never tries too hard	Gamers 16–28, gym-gaming crossover, duo-queue culture
Remy	[BRAND]	Streaming	Enthusiastic, opinionated superfan; always has the hottest take	Binge-watchers, fandom communities, streaming obsessives
Kai	[BRAND]	AI Tech	Curious, slightly conspiratorial; "nobody talks about this" discoverer	Builders, solopreneurs, tech-curious 22–35, productivity obsessives
Sable	[BRAND]	Privacy / Security	Calm, precise, quietly alarming auditor; never conspiratorial; always cites specifics	Privacy-conscious individuals, compliance-aware businesses

Visual anchor: give each avatar a fixed, describable look so generations stay consistent — e.g. for Sable: 30s, glasses, dark crewneck, dual-monitor desk setup.

Niche-mode rules (never mention the product in niche mode):

• Gaming — name a specific game/mode/mechanic; use gamer vocabulary naturally.
• Streaming — name at least one real show, character, or actor; never "a show."
• AI Tech — reference a real tool by name (ChatGPT, Claude, Cursor, Notion AI, Make, Zapier, Perplexity); the moment must make a builder think "I did this yesterday."
• Privacy/Security — cite a concrete specific (a real breach pattern, a data-broker behavior, a permission most people never check); auditor energy, never fear-mongering.

Trust-category caution: trust-heavy categories (privacy, security, finance, health) carry higher brand risk with AI-avatar content. Lead validation tests with lighter categories (gaming, streaming) before exposing trust-heavy personas.

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3. Script generation

Output contract

Every script is a single JSON object. Never wrap it in markdown. Structure:

json

{
  "hook_concept": "", "trend_used": "", "emotion_used": "", "micro_moment": "",
  "brand_integrated": false, "hook_score": 0, "tension_score": 0,
  "clip1": {"time":"0-8s","visual_direction":"","on_screen_text":"","dialogue":"","gesture":""},
  "clip2": {"time":"8-16s","visual_direction":"","on_screen_text":"","dialogue":"","gesture":""},
  "caption": "", "hashtags": ["","","","",""], "comment_cta": "", "why_it_works": ""
}

Optional blocks appear only when the matching system is on: alt_hooks (Hook A/B, 3 variants), arc_posts (5-post series). In silent/reaction mode both dialogue fields are "[silent]" and the story is carried entirely by on_screen_text + gesture.

Hard structural rules

1. Hook stops the scroll in ≤2 seconds — emotion leads.
2. Every script is unique — a different micro-moment, opening line, or entry than any prior generation for that avatar.
3. Exactly one comment/tag mechanic is baked in.
4. Two clips × 8s = 16s total.
5. visual_direction ≤ 15 words and always includes clothing + camera angle + body position + one environment detail.

Brand mode vs. niche mode

• Niche mode (default): no product mention. Script stays entirely in the avatar's world. Uses the niche bank.
• Brand mode: four integration styles —
  • soft — clip 1 pure pain; the product appears once, casually, at the end.
  • story — clip 1 frustration → clip 2 the product is the resolution.
  • demo — one feature solving the core pain, proven inside 16s.
  • cta — hook + problem → product value + direct CTA (link in bio).

The 10 virality systems

Toggle these per generation. Sensible defaults on: emotion, moment, tension, drift, duo, hooks, reformat.

1. Emotion — filter all output through one selected emotion (Rage, Nostalgia, Embarrassment, FOMO, Paranoia, Disbelief, Spite, Relief, Pride, Anxiety). Goal maps to an emotion set: awareness→relatable, engagement→argument-provoking, conversion→specific, viral→share-triggering.
2. Micro-moment — anchor the script to one hyper-specific moment pulled from the avatar's bank; avoid the last 5 used to prevent repetition.
3. Audience flip — write for the secondary audience (the partner watching the rager, the manager assigning manual work) in the same voice.
4. Tension — clip 1 must end unresolved so the viewer needs clip 2; tension_score ≥ 7 required, else flag for regeneration.
5. Drift guard — after every 5 generations for an avatar, deliberately shift register to avoid voice sameness.
6. Duo hook — imply an off-screen partner or bake "send this to your [specific person]" into clip 2.
7. Series arc — a 5-post narrative: establish pain → escalate → turning point → discovery → resolution. Advance one step per fresh generation.
8. Hook A/B — generate 3 clip-1 openers: emotion-led, curiosity-led, text-on-screen. Swappable without regenerating the body.
9. Platform reformat — adapt visual/on-screen/gesture (not the story) to another platform.
10. Reaction / silent mode — no dialogue; story told via on-screen text and gesture only.

Platform natives

• TikTok — deadpan face-to-cam, trending audio, exaggerated micro-expressions, quick cuts.
• Instagram Reels — POV text overlays, polished, stomp-to-reveal transitions.
• YouTube Shorts — curiosity-hook opener, educational delayed reveal.
• Facebook Reels — warmer, more personal, slightly longer emotional setup.

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4. Image generation (avatar portraits & reference grids)

Tool: a Gemini-based image model (e.g. Nano Banana Pro) works well here.

Photographic language beats "hyperrealistic"

The uncanny plastic/CGI look comes from quality-adjective stacking. Fixes, applied as rules:

• Blocklist these plastic-rendering words entirely: hyperrealistic, 8k, ultra-detailed, and similar over-sharpening / perfection cues.
• Anchor on real photography instead: name a camera body, a lens, an aperture, film grain, and visible imperfections (skin texture, asymmetry, uneven light).
• Avoid perfect symmetry and flat, even lighting — both read as synthetic.
• Express aspect ratio in natural language, not as flags — Midjourney-style --ar flags are silently ignored by some models and pollute the prompt.

2×2 reference grid is the consistency method

Generate a 2×2 character grid (1:1 aspect ratio) to get four consistent angles of the same face in a single credit-efficient generation. Prefer this over separate single-angle generations, which drift in identity. Always price the generation (a get_cost-style check) before committing.

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5. Video generation (clips, voice, continuity)

Tools: Seedance 2.0 (via a platform like Higgsfield) as primary; Kling 3.0; Wan 2.7 for lip-sync.

Separate voice from video — always

Embedding dialogue inline in the video model triggers per-generation TTS: no voice consistency, no prosody control, robotic output. Correct architecture:

1. Clone the avatar's voice once (a dedicated voice tool).
2. Generate the finished audio take.
3. Pass that audio into the video model as @Audio1 for lip-sync — do not let the video model synthesize speech.

Continuity across clips

• End-frame chaining — screenshot the last frame of a clip and upload it as the start image of the next generation to hold face continuity across a multi-clip sequence.
• Single continuous prompt, never shot labels — labeling parts "Shot 1 / Shot 2" makes the model treat each as an independent generation and breaks the face. Use one continuous prompt structured with SCENE: and MOTION: blocks.

Anti-static vs. lip-sync is a real conflict

Head-movement / anti-static instructions fight lip-sync during dialogue. Resolve it by locking head movement during dialogue clips and reserving motion instructions for silent / B-roll clips only. Surface this tension proactively when both are requested.

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6. Tooling & platform reference

Generation MCP patterns

If your generation platform exposes an MCP server (e.g. Higgsfield), common patterns:

• An import-by-URL call to turn a CDN output into a reusable media ID for chaining.
• A job-display call to retrieve a generation's CDN URL.
• A cost check before committing any paid generation.
• Note that server-side CDN downloads are often blocked — download via browser instead.

Script engine implementation

A common setup is a React artifact that calls a chat-completion endpoint to produce the JSON script contract above. When run inside a host that provides a built-in proxy, no API key is needed in that environment. Keep the generation and reformat prompts versioned.

Hosted UI routes

If the engine has a hosted UI, routes are rarely guessable — enumerate the real ones with document.querySelectorAll('a[href]') rather than guessing paths.

Browser automation

On a heavy, animated staging UI, get_page_text and javascript_tool tend to be more reliable than screenshot-based validation. Avoid navigate + screenshot batch combos — animated canvas rendering can cause timeouts.

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7. Known gotchas

Check these before assuming a clean run:

• Gateway timeouts on generation: long synchronous generation requests (~40s+) can exceed a gateway timeout. Use an async job model for generation endpoints.
• Stale trend data: trend/ranking data can go empty or stale until a fresh ingest is triggered.
• Persona-name drift: watch for the same persona appearing under slightly different names across surfaces; keep names canonical.
• Persona sprawl: near-duplicate personas accumulate over time — consolidate.

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8. Operating principles (how to work here)

• Give a brief heads-up on approach, then execute with full momentum — minimal mid-task check-ins.
• Think in reusable blocks: named templates, scored banks, unit economics (track cost-per-clip). Prefer systematized prompt engineering over ad hoc craft.
• Surface conflicts within the system proactively (anti-static vs. lip-sync is the model here) — they are welcome.
• Prefer emotionally triggering, scroll-stopping mechanics over safe, information-heavy content.
• Scale the roster gradually across verticals rather than all at once.