Avatar content engine

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

---

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.

---

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.

---

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.

---

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.

---

3. Script generation

Output contract

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

{
  "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.

---

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.

---

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.

---

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.

---

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.

---

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.