| name | avatar-gen |
| description | Avatar-gen is RRQ's presenter generation and rotation system. It manages a dynamic roster of 3-5 channel presenters, each with a permanent face (seed-locked FLUX portrait), evolving personality, and content-type ownership. FLUX.1 [dev] on EC2 g4dn.xlarge spot generates portraits once at channel onboarding and never again for that presenter. SkyReels V2 reuses the same reference.jpg every video — face consistency is guaranteed by SkyReels I2V architecture. Regum owns rotation logic. Zeus scores presenter performance. Oracle tracks presenter analytics and triggers evolution reviews. Read this skill when building presenter generation, roster management, rotation logic, personality evolution, or the human-in-loop approval gate.
|
Avatar-Gen — Presenter Roster & Generation System
What Avatar-Gen Is Not
NOT AVATAR-GEN'S JOB: Generating per-video talking head video → SkyReels (EC2)
NOT AVATAR-GEN'S JOB: Deciding which topic to cover → Rex + Regum
NOT AVATAR-GEN'S JOB: Writing the script → Qeon script step
NOT AVATAR-GEN'S JOB: Managing upload schedule → Regum
NOT AVATAR-GEN'S JOB: Tracking channel-level analytics → Zeus
What Avatar-Gen Owns
OWNS: Portrait generation (FLUX.1 [dev] on EC2 — once per presenter lifetime)
OWNS: character_config.json (personality, traits, content assignment, voice)
OWNS: Presenter roster state in DynamoDB avatar-profiles table
OWNS: Human-in-loop approval gate (optional — off by default)
OWNS: Personality evolution records (face never changes, personality deepens)
OWNS: Feeding presenter context into SkyReels job parameters
Presenter Roster Design
Gender Ratio — Default
RRQ default roster ratio: 3F:1M or 4F:1M
Rationale:
Majority female roster is a deliberate strategic decision.
Female presenters index higher on trust, warmth, and watch time
retention across the widest range of content categories.
Male presenter included for credibility-anchored content types
(finance deep dives, technical hardware analysis, geopolitical breakdowns).
Override: user may set ratio during onboarding. Roster must have
minimum 1 presenter before any video can be produced.
Maximum 5 presenters before Oracle triggers a review
of whether expansion adds value or dilutes audience recognition.
Presenter Archetype Table — Default 4-Presenter Roster
ID Gender Style Content Ownership
──────────────────────────────────────────────────────────────────
presenter_f1 Female Editorial power Breaking news, tech, AI,
(blazer, poised, finance, trending topics.
authority voice) High-stakes topics. Default
presenter when no match found.
presenter_f2 Female Smart-casual Explainer videos, how-to
(contemporary, series, deep dives, science,
warm, intelligent) education. Long-form anchor.
presenter_f3 Female Bold lifestyle Beauty, culture, lifestyle,
(expressive, entertainment, social trends,
charismatic, gaming culture. Shorts-first.
dynamic presence)
presenter_m1 Male Documentary Finance investigation,
authority geopolitics, investigative
(suit, credible, explainers, technical hardware,
measured delivery) serious analytical topics.
Expansion Roster (Slots 5+)
Triggered by Oracle Domain 10 when:
- Existing presenter content-type mismatch > 20% of videos for 30 days
- New content category added to channel niche that has no natural owner
- Performance gap: one presenter averaging 40% lower CTR than roster mean
New presenter generation follows same FLUX pipeline as initial onboarding.
Zeus logs roster expansion as EXPANSION_EVENT in oracle-updates table.
Portrait Generation — FLUX.1 [dev] on EC2
When FLUX Fires
TRIGGER 1: Channel onboarding — CHANNEL_MODE_SET EventBridge event
All presenters for the initial roster generated in one batch.
TRIGGER 2: Roster expansion event — Oracle Domain 10 recommendation
approved by Zeus. Only new presenter(s) generated.
TRIGGER 3: Manual regeneration — user hits REGENERATE in approval gate UI.
One presenter at a time. Previous seed discarded.
NEVER: Fires mid-pipeline. FLUX does not run per-video.
SkyReels reuses the reference.jpg stored in S3 forever.
EC2 Instance Specification
Instance: g4dn.xlarge spot
GPU: 1x NVIDIA T4 (16GB VRAM)
VRAM budget: FLUX.1 [dev] FP8 quantised = ~12GB VRAM
Disk: 200GB gp3 (model weights + output)
Spot price: ~$0.16/hr (us-east-1)
Runtime: ~4 min per portrait at 1024x1024
Batch: All roster portraits generated in one spot session
Termination: Self-terminates after batch upload to S3 confirmed
Cost: ~$0.02-0.04 for a full 4-presenter batch
Seed Locking — Permanent Face Consistency
function generatePresenterSeed(): number {
return Math.floor(Math.random() * 2 ** 32);
}
const fluxParams = {
prompt: presenter.base_prompt,
seed: presenter.seed,
num_steps: 50,
guidance_scale: 3.5,
width: 1024,
height: 1024,
output_format: "jpg",
output_quality: 95,
};
FLUX Prompt Templates
Female Presenter Prompts
// presenter_f1 — Editorial Power
base_prompt: "Professional female presenter, tailored power blazer in deep navy
or charcoal, clean structured collar, bold confident direct gaze into camera,
sharp strong features, elegant grooming, immaculate skin, hair styled away from
face or sleek blowout, modern dark studio background with subtle rim lighting,
cinematic portrait photography, photorealistic, 8k quality, Hasselblad medium
format aesthetic, no jewelry distraction, authority and composure, age 28-38"
// presenter_f2 — Smart Casual Intellectual
base_prompt: "Intelligent female presenter, contemporary smart-casual styling,
structured knit or refined blouse, warm expressive eyes conveying curiosity and
depth, relaxed yet composed posture, natural soft makeup, glossy healthy hair,
modern neutral background soft bokeh, cinematic portrait photography,
photorealistic, 8k quality, editorial magazine lighting, approachable and
credible, relatable warmth without losing authority, age 25-35"
// presenter_f3 — Bold Lifestyle
base_prompt: "Charismatic female lifestyle presenter, bold statement outfit in
rich jewel tone or confident colour, expressive animated face, radiant flawless
skin, statement hair styling, dynamic energy in posture, editorial beauty
lighting, warm skin tones, vibrant photorealistic portrait, 8k quality,
high-fashion editorial aesthetic, magnetic presence, engaging eyes, age 24-33"
// Generic female expansion slot
base_prompt_template: "Professional female presenter, {style_descriptor},
confident direct gaze, photorealistic portrait, cinematic studio lighting,
well-groomed, polished appearance, dark professional background, 8k quality,
{age_range}"
Male Presenter Prompts
// presenter_m1 — Documentary Authority
base_prompt: "Authoritative male documentary presenter, premium dark charcoal
suit, crisp white or light blue shirt, no tie or understated tie, strong jaw,
calm analytical expression, direct intelligent gaze, distinguished professional
appearance, dark studio background with subtle depth lighting, cinematic portrait
photography, photorealistic, 8k quality, BBC documentary presenter aesthetic,
trustworthy measured gravitas, age 32-45"
// Generic male expansion slot
base_prompt_template: "Professional male presenter, {style_descriptor},
confident composed expression, photorealistic portrait, cinematic studio
lighting, authoritative but approachable, dark professional background,
8k quality, {age_range}"
Diversity Instruction (Applied to All)
All portraits default to diverse ethnicity across the roster.
No two presenters in the same roster should share the same apparent ethnicity.
Regum sets ethnicity guidance during brief generation if niche warrants it
(e.g., SNIPER geo-strategy indicates primary market is South Asia — Regum
adjusts demographic composition of roster accordingly).
Diversity is a strategic decision, not a compliance checkbox.
Wider demographic representation = broader audience identification.
Character Config — Full Schema
DynamoDB Table: avatar-profiles
PK: channelId (string)
SK: presenterId (string — e.g., "presenter_f1")
TypeScript Interface
export interface AvatarProfile {
channelId: string;
presenterId: string;
displayName: string;
gender: "FEMALE" | "MALE" | "NEUTRAL";
archetype: string;
seed: number;
base_prompt: string;
s3_reference: string;
generated_at: string;
portrait_version: number;
voice_id: string;
voice_style: string;
edge_tts_fallback: string;
personality: PersonalityProfile;
expression_hints: ExpressionHint[];
content_assignment: ContentAssignment;
performance_scores: PresenterPerformanceScores;
use_count: number;
last_used: string;
version: number;
evolution_history: EvolutionRecord[];
approval_status: "PENDING_APPROVAL" | "APPROVED" | "AUTO_APPROVED";
approved_at?: string;
approved_by?: "HUMAN" | "AUTO_TIMEOUT";
}
export interface PersonalityProfile {
core_traits: string[];
delivery_style: string;
hook_style: string;
content_strengths: string[];
audience_rapport: string;
verbal_tics: string[];
avoid: string[];
}
export interface ExpressionHint {
content_type: string;
skyreels_params: {
expression: string;
intensity: number;
pacing: "FAST" | "MEASURED" | "SLOW";
energy: "HIGH" | "MEDIUM" | "LOW";
};
}
export interface ContentAssignment {
primary_types: string[];
secondary_types: string[];
excluded_types: string[];
niche_fit_score: Record<string, number>;
}
export interface PresenterPerformanceScores {
avg_ctr: number;
avg_retention: number;
avg_likes_ratio: number;
content_type_scores: Record<string, number>;
last_updated: string;
video_count: number;
}
export interface EvolutionRecord {
version: number;
evolved_at: string;
trigger: "ORACLE_REVIEW" | "ZEUS_DIRECTIVE" | "MANUAL";
changed_fields: string[];
reason: string;
performance_delta: number;
}
character_config.json — Example
{
"presenterId": "presenter_f1",
"displayName": "Zara",
"gender": "FEMALE",
"archetype": "editorial_power",
"seed": 2847361920,
"s3_reference": "avatars/dynamic/channel_abc123/presenter_f1/reference.jpg",
"voice_id": "21m00Tcm4TlvDq8ikWAM",
"voice_style": "authoritative",
"edge_tts_fallback": "en-US-AriaNeural",
"personality": {
"core_traits": ["analytical", "composed", "direct", "decisive"],
"delivery_style": "Measured authority with controlled urgency on breaking topics. Never breathless. Commands attention without raising her voice.",
"hook_style": "Opens with a statement most people assume is wrong, then immediately proves it. Cold opens lean factual, not emotional.",
"content_strengths": ["BREAKING_NEWS", "TECH_ANALYSIS", "FINANCE", "AI_DEVELOPMENTS"],
"audience_rapport": "Builds trust through precision. Viewers feel she has done the work so they don't have to.",
"verbal_tics": ["short declarative sentences", "rhetorical pause before key data points"],
"avoid": ["exclamation energy", "lifestyle framing", "pop culture references"]
},
"expression_hints": [
{
"content_type": "BREAKING_NEWS",
"skyreels_params": {
"expression": "concentrated_focus",
"intensity": 0.8,
"pacing": "MEASURED",
"energy": "HIGH"
}
},
{
"content_type": "EXPLAINER",
"skyreels_params": {
"expression": "engaged_clarity",
"intensity": 0.6,
"pacing": "MEASURED",
"energy": "MEDIUM"
}
}
],
"content_assignment": {
"primary_types": ["BREAKING_NEWS", "TECH_ANALYSIS", "FINANCE", "AI"],
"secondary_types": ["EXPLAINER", "GEOPOLITICS"],
"excluded_types": ["BEAUTY", "GAMING_CULTURE", "LIFESTYLE"],
"niche_fit_score": {
"tech": 95,
"finance": 92,
"ai": 98,
"science": 80,
"business": 85,
"beauty": 20,
"gaming": 35
}
},
"performance_scores": {
"avg_ctr": 0.068,
"avg_retention": 0.54,
"avg_likes_ratio": 0.042,
"content_type_scores": {
"BREAKING_NEWS": 88,
"TECH_ANALYSIS": 82,
"FINANCE": 79
},
"last_updated": "2026-03-14T09:00:00Z",
"video_count": 14
},
"version": 2,
"approval_status": "APPROVED",
"approved_by": "HUMAN"
}
Muse Character Brief — Generation & Format
When Muse Generates Character Briefs
Trigger: Channel onboarding CHANNEL_MODE_SET event fires.
Muse receives: channelNiche, channelMode, geoStrategy (from SNIPER),
rosterSize, genderRatio (from user onboarding settings).
Muse outputs: one CharacterBrief per presenter slot.
Regum receives CharacterBrief array → sets strategic content assignment.
Avatar-gen receives CharacterBrief + ContentAssignment → builds FLUX prompt + character_config.
CharacterBrief TypeScript Interface
export interface CharacterBrief {
slotId: string;
gender: "FEMALE" | "MALE" | "NEUTRAL";
ageRange: string;
archetype: string;
visualDirection: {
style: string;
colourPalette: string;
hairDirection: string;
makeupIntensity: "MINIMAL" | "NATURAL" | "POLISHED" | "BOLD";
backgroundNote: string;
};
personality: {
coreTraits: string[];
deliveryStyle: string;
hookStyle: string;
audienceRapport: string;
verbalTics: string[];
avoid: string[];
};
contentFit: string[];
voiceDirection: string;
strategicRationale: string;
}
Example Muse Character Brief Output
{
"slotId": "presenter_f2",
"gender": "FEMALE",
"ageRange": "26-34",
"archetype": "smart_casual",
"visualDirection": {
"style": "Contemporary smart-casual. Structured knit or refined silk blouse. Approachable without sacrificing sharpness.",
"colourPalette": "Warm neutrals, dusty rose, sage green. Nothing corporate grey.",
"hairDirection": "Natural waves or sleek bob. Feels real, not produced.",
"makeupIntensity": "NATURAL",
"backgroundNote": "Warm neutral with soft depth bokeh. Not a cold dark studio — warmer than presenter_f1."
},
"personality": {
"coreTraits": ["curious", "warm", "thorough", "unpretentious"],
"deliveryStyle": "Conversational authority. Explains complex ideas as if talking to a smart friend. Pacing is relaxed but never slow. Uses analogies freely.",
"hookStyle": "Opens with a relatable question the viewer has definitely asked themselves. Immediate recognition moment. Never stats-first.",
"audienceRapport": "Feels like the most knowledgeable person at the dinner table who actually wants to share what they know.",
"verbalTics": ["uses 'here's the thing'", "pauses before counterintuitive reveals", "occasionally self-corrects mid-explanation (sounds authentic)"],
"avoid": ["corporate register", "sounding like a press release", "overly dramatic hooks"]
},
"contentFit": ["EXPLAINER", "SCIENCE", "HEALTH", "EDUCATION", "DEEP_DIVE_SERIES"],
"voiceDirection": "Warm mid-range. Confident but not anchorial. Sounds like she genuinely finds this interesting.",
"strategicRationale": "Counterweight to presenter_f1's authority tone. Handles long-form content where trust-through-warmth outperforms trust-through-precision. Essential for health, science, and education niches where approachability drives completion rate."
}
Rotation Logic — Regum Owns This
Rules (Hardcoded)
RULE 1 — No Run of Three:
Same presenter cannot appear in 3 consecutive published videos.
Enforced at scheduling time by Regum — before job is queued.
RULE 2 — Content Type Match:
Presenter must have content_type in primary_types or secondary_types.
If no presenter matches: default to presenter_f1 (editorial power — broadest range).
RULE 3 — Performance Weight:
Zeus performance scores feed a weighted probability distribution.
Higher-performing presenter gets more slots — not all slots.
Distribution is recalculated weekly by Zeus after analytics review.
RULE 4 — 20% Controlled Randomness:
Even if one presenter is scoring highest, 20% of rotation is
random-sampled from the full eligible roster. Prevents the channel
from feeling algorithmically sampled — human channels have variety.
RULE 5 — Recovery Period:
After a video underperforms (CTR below 50% of channel mean for that type),
that presenter gets a minimum 2-video rest before being re-assigned to
the same content type. Performance isolation, not presenter blame.
Rotation Pseudocode
function selectPresenter(
contentType: string,
recentHistory: string[],
performanceScores: Record<string, number>,
allProfiles: AvatarProfile[]
): AvatarProfile {
const eligible = allProfiles.filter(p =>
p.content_assignment.primary_types.includes(contentType) ||
p.content_assignment.secondary_types.includes(contentType)
);
if (eligible.length === 0) {
return allProfiles.find(p => p.presenterId === "presenter_f1")!;
}
const lastTwo = recentHistory.slice(-2);
const filtered = eligible.length > 1
? eligible.filter(p => {
const appearsInLastTwo = lastTwo.includes(p.presenterId);
const wouldBeThirdConsecutive =
lastTwo.length === 2 &&
lastTwo[0] === p.presenterId &&
lastTwo[1] === p.presenterId;
return !wouldBeThirdConsecutive;
})
: eligible;
const useRandom = Math.random() < 0.20;
if (useRandom) {
return filtered[Math.floor(Math.random() * filtered.length)];
}
const weights = filtered.map(p => ({
presenter: p,
weight: performanceScores[p.presenterId] ?? 50,
}));
const totalWeight = weights.reduce((sum, w) => sum + w.weight, 0);
let random = Math.random() * totalWeight;
for (const { presenter, weight } of weights) {
random -= weight;
if (random <= 0) return presenter;
}
return filtered.sort(
(a, b) => (performanceScores[b.presenterId] ?? 50) - (performanceScores[a.presenterId] ?? 50)
)[0];
}
DynamoDB Rotation State
Table: avatar-profiles
Field: last_used → ISO timestamp, updated by Regum after slot assignment
Field: use_count → incremented on every slot assignment
Regum reads recent production-jobs table to reconstruct recentHistory[]
before every rotation decision. Does not cache rotation state — always
derives from source of truth in production-jobs.
SkyReels Integration — How Presenters Feed Into Video Production
What Happens Every Video
1. Regum selects presenter via rotation logic → writes presenterId to QeonBrief.
2. Qeon reads character_config.json from S3 for that presenter.
3. Qeon routes TALKING_HEAD + SPLIT_SCREEN beats to SkyReels EC2.
4. SkyReels job parameters include:
- reference_image: s3://content-factory-assets/{s3_reference}
- audio_path: s3 key of voiceover MP3
- expression_hint: matched from expression_hints[] by content type
- pacing: from expression_hints[].skyreels_params.pacing
5. SkyReels V2 I2V reads same reference.jpg → same face → different expressions.
6. Output: talking head MP4 segments → av-sync Lambda stitches final video.
Expression Hint Resolution
function resolveExpressionHint(
presenter: AvatarProfile,
contentType: string
): ExpressionHint["skyreels_params"] {
const exactMatch = presenter.expression_hints.find(
h => h.content_type === contentType
);
if (exactMatch) return exactMatch.skyreels_params;
const fallback = presenter.expression_hints.find(
h => h.content_type === "EXPLAINER"
);
return fallback?.skyreels_params ?? {
expression: "engaged_neutral",
intensity: 0.6,
pacing: "MEASURED",
energy: "MEDIUM",
};
}
Human-in-Loop Approval Gate
Default State: OFF
Human approval gate is disabled by default.
Pipeline generates portraits, auto-approves after portrait upload to S3,
and proceeds immediately to character_config.json generation.
Enable via: user-settings DynamoDB table
field: avatar_approval_gate = true | false (default: false)
Gate Flow (When Enabled)
1. FLUX generates portrait batch → uploads to S3.
2. Pipeline hard stops.
3. /app/onboarding/avatar-review page renders:
- Portrait image preview (full quality)
- Character name + archetype
- Personality summary card
- Core traits list
- Content type ownership badges
- Voice style description
4. User sees three actions:
[APPROVE] — accepts portrait + personality, proceeds
[REGENERATE] — discards current portrait, re-runs FLUX with new random seed
New seed is stored. Old seed discarded. One retry per presenter.
[EDIT TRAITS] — keeps portrait, opens personality editor
User can edit: core_traits, delivery_style, avoid[]
Cannot edit: visual direction, voice assignment (Regum's domain)
5. 24-hour timeout → AUTO_APPROVED if no user action.
Zeus logs auto-approval as approval_status: "AUTO_APPROVED".
Oracle reads this as lower confidence than human approval.
6. Human APPROVE → Zeus logs:
approval_status: "APPROVED"
approved_by: "HUMAN"
Zeus writes episode to rrq-memory with signal_type: "HIGH_CONFIDENCE_SIGNAL"
Oracle uses human approval signal in Domain 10 presenter analytics.
Approval Gate UI State Machine
type ApprovalGateState =
| "GENERATING"
| "AWAITING_REVIEW"
| "REGENERATING"
| "EDITING_TRAITS"
| "APPROVED"
| "AUTO_APPROVED"
| "COMPLETE";
Personality Evolution — Face Never Changes
What Evolves vs What Is Permanent
PERMANENT (seed-locked, never changes):
Portrait face geometry
Hair colour and style in portrait
Physical features
EVOLVES (personality deepens over time):
core_traits[] — new traits added, rarely removed
delivery_style — refined based on what's working
hook_style — updated if Oracle finds new hook meta
expression_hints[] — new content types added as channel expands
content_assignment — niche_fit_score updated by Zeus performance data
voice_id — can be updated if ElevenLabs releases better model match
Evolution Trigger Conditions
ORACLE_REVIEW trigger (primary):
Oracle Domain 10 runs — finds presenter performance data suggests
personality adjustment would improve CTR or retention.
Oracle writes recommendation to oracle-updates table.
Zeus reads recommendation at next morning brief.
Zeus decides: APPLY | DEFER | REJECT.
If APPLY: Zeus writes updated personality to avatar-profiles.
version++ in DynamoDB.
evolution_history[] gets new EvolutionRecord.
ZEUS_DIRECTIVE trigger:
Zeus detects cross-video pattern (e.g., presenter_f2 underperforms
every time she covers finance topics). Zeus directly updates
content_assignment.excluded_types[] and logs reason.
MANUAL trigger:
User edits traits via EDIT TRAITS gate (only during onboarding or
via user settings page). Increments version. Logs as MANUAL trigger.
Evolution Safety Rules
RULE: Never change more than 2 core_traits in a single evolution pass.
Audience builds recognition on presenter consistency.
Dramatic overnight personality shifts undermine trust.
RULE: Oracle must provide performance evidence for evolution recommendation.
"avg CTR improved 12% on TECH_ANALYSIS beats after delivery_style
shift toward drier humour" — this is valid evidence.
Gut feel or stylistic preference is not valid.
RULE: Zeus has final authority on all evolution decisions.
Oracle recommends. Zeus decides.
Zeus logs reason for REJECT alongside Oracle's recommendation.
Oracle Domain 10 — Presenter Performance Analytics
{
id: "PRESENTER_PERFORMANCE_ANALYTICS",
name: "Presenter Performance & Roster Analytics",
description: "Tracks per-presenter CTR, retention, and engagement across " +
"content types. Identifies which presenter/content-type combinations " +
"are overperforming or underperforming. Generates roster evolution " +
"recommendations: personality adjustments, content reassignment, " +
"or roster expansion. Feeds Zeus weekly performance brief.",
primaryAgent: "ZEUS",
secondaryAgents: ["REGUM"],
researchDepth: "STANDARD",
runFrequency: "WEEKLY",
sources: [
"avatar-profiles DynamoDB table (performance_scores fields)",
"production-jobs DynamoDB table (per-video presenter assignment)",
"channel-health DynamoDB table (per-video CTR + retention snapshots)",
"zeus-briefs DynamoDB table (Zeus weekly performance synthesis)",
],
outputs: [
"presenter_performance_report: per-presenter score card with trend direction",
"combination_matrix: presenter × content_type CTR and retention grid",
"evolution_recommendations: specific trait or assignment changes with evidence",
"expansion_recommendation: flag if new presenter slot needed (with justification)",
"rotation_weight_update: new performance_scores for Regum rotation logic",
],
thresholds: {
sustained_underperformance: "CTR below 60% of channel mean for 10+ videos",
content_type_mismatch: "Performance gap > 30% between primary and secondary types",
roster_saturation: "All presenters averaging < 70% content type fit score for 30 days",
new_niche_coverage_gap: "Content type added to channel with no presenter scoring > 50 fit",
rotation_weight_recalc: "Every run — weights fed to Regum for next week's rotation",
},
}
Oracle Domain 10 — DynamoDB Write Format
export interface PresenterAnalyticsUpdate {
updateId: string;
domain: "PRESENTER_PERFORMANCE_ANALYTICS";
channelId: string;
generatedAt: string;
rotationWeights: Record<string, number>;
evolutionRecommendations: EvolutionRecommendation[];
expansionRecommendation?: ExpansionRecommendation;
combinationMatrix: Record<string, Record<string, number>>;
}
export interface EvolutionRecommendation {
presenterId: string;
changeType: "TRAIT_UPDATE" | "CONTENT_REASSIGNMENT" | "VOICE_UPDATE";
evidence: string;
specificChange: string;
expectedImpact: string;
confidence: number;
}
export interface ExpansionRecommendation {
justification: string;
suggestedSlotId: string;
suggestedArchetype: string;
coverageGap: string[];
urgency: "IMMEDIATE" | "NEXT_CYCLE" | "MONITOR";
}
Lambda — avatar-gen
Location
lambdas/avatar-gen/
src/handler.ts — main Lambda handler
src/flux-runner.ts — EC2 spot launch + FLUX job coordination
src/character-builder.ts — Muse brief → character_config.json generation
src/approval-gate.ts — human-in-loop gate state management
Handler Events
export type AvatarGenEvent =
| { type: "GENERATE_ROSTER"; channelId: string; characterBriefs: CharacterBrief[] }
| { type: "GENERATE_ONE"; channelId: string; slotId: string; characterBrief: CharacterBrief }
| { type: "APPROVE"; channelId: string; presenterId: string; approvedBy: "HUMAN" | "AUTO_TIMEOUT" }
| { type: "REGENERATE"; channelId: string; presenterId: string }
| { type: "EDIT_TRAITS"; channelId: string; presenterId: string; traitEdits: Partial<PersonalityProfile> }
| { type: "APPLY_EVOLUTION"; channelId: string; evolution: EvolutionRecord };
Handler Response
export interface AvatarGenResponse {
success: boolean;
channelId: string;
presenterId?: string;
s3Reference?: string;
approvalStatus?: AvatarProfile["approval_status"];
error?: string;
}
S3 Asset Paths
content-factory-assets/
avatars/
dynamic/
{channelId}/
{presenterId}/
reference.jpg — FLUX portrait (permanent, seed-locked)
character_config.json — full personality + assignment config
portrait_preview.jpg — 512x512 thumbnail for approval gate UI
generation_metadata.json — seed, prompt, FLUX params (audit trail)
Environment Variables
EC2_FLUX_AMI_ID=
EC2_FLUX_INSTANCE_TYPE=g4dn.xlarge
FLUX_MODEL_PATH=s3://content-factory-assets/models/flux-dev-fp8/
EC2_ROLE_ARN=
EC2_SUBNET_ID=
EC2_SECURITY_GROUP_ID=
AVATAR_GEN_LAMBDA_ARN=
AVATAR_APPROVAL_TIMEOUT_HOURS=24
Cost Summary
Portrait generation (FLUX batch, 4 presenters):
g4dn.xlarge spot ~$0.16/hr × ~15min = ~$0.04 total
Fired once at onboarding. Never again unless REGENERATE or expansion.
Per-video avatar cost:
SkyReels EC2 (g5.12xlarge, ~12min) = ~$0.32
FLUX: $0.00 (reusing existing reference.jpg)
character_config.json read: $0.00 (S3 GET)
Presenter analytics (Oracle Domain 10):
Bedrock Nova Pro synthesis of DynamoDB performance data = ~$0.01/run
Runs weekly alongside Zeus analytics review.
Integration Points Summary
MUSE → Generates CharacterBrief[] during onboarding
Reads character_config.json to inform expression direction in MuseBlueprint
REGUM → Selects presenter via rotation logic before every video
Sets content_assignment during onboarding (strategic fit)
Updates last_used + use_count in DynamoDB after slot assignment
ZEUS → Scores presenter performance after every published video
Applies Oracle Domain 10 evolution recommendations
Logs high-confidence human approval signals to rrq-memory
Arbitrates all evolution decisions
ORACLE → Domain 10: weekly presenter performance analytics
Generates evolution and expansion recommendations for Zeus
QEON → Reads character_config.json for assigned presenter
Passes expression_hints to SkyReels job parameters
Reports presenter assignment in step logs to Zeus
SKYREELS → Receives reference.jpg + expression hint per job
Same face every video. Different expressions per content type.
No FLUX involvement after onboarding — SkyReels handles all per-video avatar video.
VERA → Visual QA pass includes presenter segment check
Flags: face inconsistency (SkyReels drift), expression mismatch, lip-sync errors
