| name | muse |
| description | MUSE is RRQ's video architect. It runs inside Qeon's pipeline before the script is written. MUSE does two things: (1) selects the right video format for the topic using a dynamic format library maintained by ORACLE, and (2) generates a precise second-by-second blueprint — every structural beat, tension device, visual instruction, and retention checkpoint mapped out before a single word of script is written. Qeon writes INTO the blueprint, not around it. MUSE never uses a hardcoded format list — it always queries the live format library from ORACLE's knowledge base. ORACLE discovers new formats, MUSE executes them. Read this skill whenever building or modifying video structure, format selection, retention engineering, pacing, or visual direction for any RRQ video.
|
MUSE — Video Architecture & Retention Engineering
What MUSE Does Not Own
NOT MUSE'S JOB: Deciding what topic to cover → Rex + ARIA + Regum
NOT MUSE'S JOB: Researching the topic → Qeon research step
NOT MUSE'S JOB: Writing the script → Qeon script step
NOT MUSE'S JOB: Maintaining the format library → ORACLE
NOT MUSE'S JOB: Tracking format performance → Zeus
What MUSE Owns — Three Hardcoded Things Only
HARDCODED 1: RRQ Editorial Voice
Never changes. Every video, every format, every market.
HARDCODED 2: The Four Retention Walls
Psychology not meta. Do not shift with trends.
HARDCODED 3: Visual Direction Rules
Cut timing, visual type pairing. Platform mechanics not style.
Everything else — formats, structures, tension curves, beat sequences —
lives in the dynamic format library that ORACLE discovers and maintains.
MUSE queries it at runtime. Never imports a hardcoded list.
Additional MUSE Responsibilities
CHARACTER BRIEF GENERATION
Character brief generation for presenter roster (channel onboarding + expansion).
Personality design: traits, tone, visual direction, content type assignment.
Collaborates with Regum on strategic fit before finalizing character brief —
Regum provides niche/content type assignment, Muse provides creative direction.
HARDCODED 1 — RRQ Editorial Voice
Injected into every hook, transition, beat, and CTA. Non-negotiable.
TONE: Smart & Informative
Explains with clarity and authority. Never dumbs down.
Never over-complicates. Treats the viewer as smart but busy.
FILTER: Balanced & Factual
Presents evidence. Acknowledges opposing views before
dismissing them. Respects the viewer's ability to decide.
NEVER: Sensationalist. Clickbait. Hyperbolic. Vague. Condescending.
Saying "in this video" in the first 10 seconds.
Starting with "Hey guys welcome back."
Ending without a clear point of view.
ALWAYS: Has a position. States it early. Backs it with evidence.
The cold open earns attention. The climax earns the watch time.
IN ONE LINE: RRQ tells you what is actually happening, why it matters,
and what to think about it — without wasting your time.
HARDCODED 2 — The Four Retention Walls
Every video engineers against these four moments regardless of format.
WALL 1 — 0:30
What happens: First judgment. "Is this worth my time?"
Less than 45% of viewers make it past 1 minute.
MUSE rule: Hook + pre-commitment device must land before 0:30.
Viewer must have made a psychological investment here.
A question they want answered. A promise they need fulfilled.
WALL 2 — 1:00
What happens: Final commitment decision.
MUSE rule: First real payoff or revelation before 1:00.
Not a tease — actual value delivered.
Viewer must feel the click was worth it by this mark.
WALL 3 — MIDPOINT
What happens: Fatigue. "I get it. Do I need the rest?"
MUSE rule: Midpoint re-hook mandatory. New angle, escalation,
unexpected reveal, or partial loop close that opens
a bigger loop. Never let midpoint feel like an exit point.
WALL 4 — 2 MINUTES BEFORE END
What happens: "I know where this is going. I can leave."
MUSE rule: Tease the final payoff but withhold it.
Force completion by making the ending feel essential.
HARDCODED 3 — Visual Direction Rules
VISUAL TYPES (Qeon's production palette):
TALKING_HEAD Avatar — opinion, verdict, analysis, personal take
B_ROLL Stock video — context, atmosphere, illustration
SLIDE Static graphic — facts, lists, definitions, data points
CHART Chart.js graph — trends, comparisons, numerical data
DIAGRAM Mermaid — processes, flows, systems, relationships
SCREEN_RECORD UI recording — product demos, comparisons, walkthroughs
IMAGE Single image — emotional moments, reference, evidence
SPLIT_SCREEN Side by side — comparisons, before/after, versus
GRAPHIC_OVERLAY Text on B-roll — emphasis, statistics, key quotes
CUT TIMING RULES:
Hook + first 60s: cut every 3-4 seconds — maximum urgency
Body sections: cut every 5-6 seconds — no static shot longer
Climax: cut every 4-5 seconds — tension re-engaged
Outro: cut every 6-8 seconds — natural wind-down
Never same visual type three consecutive cuts
VISUAL-SCRIPT PAIRING (mandatory):
Number or statistic spoken → SLIDE or CHART simultaneously
"For example" spoken → B_ROLL or IMAGE immediately
Verdict or opinion delivered → TALKING_HEAD
Process explained → DIAGRAM
Two things compared → SPLIT_SCREEN or alternating cuts
Key quote or claim → GRAPHIC_OVERLAY
The Dynamic Format Library
MUSE never imports a hardcoded format list. Before every blueprint
generation MUSE queries ORACLE's RAG for the current format library.
ORACLE discovers, names, documents, and maintains every format.
MUSE reads and executes. If ORACLE adds a new format tonight,
MUSE uses it tomorrow morning.
export interface VideoFormat {
id: string;
name: string;
description: string;
tensionCurve: TensionCurve;
idealLength: { min: number; max: number };
topicSignals: string[];
retentionProfile: {
hookStyle: string;
pacingStyle: string;
payoffStructure: string;
};
beats: FormatBeat[];
status: "ACTIVE" | "EMERGING" | "DECLINING" | "DEPRECATED";
performanceWeight: number;
discoveredAt: string;
lastUpdated: string;
source: "ORACLE_DISCOVERED" | "SEED";
}
export type TensionCurve =
| "ESCALATING_REVEAL"
| "RAPID_DOPAMINE"
| "OPEN_LOOP_CLOSE"
| "THESIS_PRESSURE_TEST"
| "NARRATIVE_ARC"
| "POSSIBILITY_ESCALATION"
| "COURTROOM"
| "COUNTDOWN_BUILD"
| "DOCUMENTARY_ARC"
| "DISCOVERY_JOURNEY";
export async function getFormatLibrary(): Promise<VideoFormat[]> {
const knowledge = await queryOracleKnowledge(
"Return the complete current video format library as JSON array",
"VIDEO_FORMAT_LIBRARY"
);
try {
return JSON.parse(knowledge);
} catch {
return SEED_FORMAT_LIBRARY;
}
}
CharacterBrief — Presenter Roster Output
Generated by MUSE during channel onboarding and roster expansion events.
Consumed by the avatar-gen Lambda for portrait generation.
Roster ratio: MUSE defaults to a 3F:1M ratio when generating the initial
roster. Female presenters are described as "bold, classy, well-groomed,
photorealistic — varied ethnicity and age range (25-45)." Roster expansions
follow the same ratio unless Regum instructs otherwise based on channel analytics.
Regum collaborates with Muse before the brief is finalised: Regum provides
strategic fit (niche, content type assignment) while Muse provides creative
direction (personality, visual style, voice hints, flux prompt).
interface CharacterBrief {
presenterId: string;
gender: 'female' | 'male';
ageRange: '25-30' | '30-35' | '35-40' | '40-45';
ethnicity: string;
personality: {
archetype: 'ANCHOR' | 'ANALYST' | 'COMMENTATOR' | 'INVESTIGATOR';
traits: string[];
tone: 'formal' | 'semi-formal' | 'conversational';
energyLevel: 'high' | 'medium' | 'measured';
};
visual: {
style: 'power-suit' | 'editorial-blazer' | 'smart-casual-presenter' | 'executive';
colorPalette: string[];
grooming: string;
background: string;
mood: string;
};
contentAssignment: {
primaryTypes: string[];
nicheAffinity: string[];
avoidTypes: string[];
};
voice: {
elevenlabsVoiceId: string;
pace: 'fast' | 'medium' | 'measured';
expressionHints: string[];
};
fluxPrompt: string;
seed: number;
}
Seed Format Library
Ten formats stored in ORACLE's RAG on first deployment.
Treated identically to ORACLE-discovered formats.
ORACLE can update, deprecate, or replace any of them.
SEED 1: COMPARISON
Tension: ESCALATING_REVEAL
Length: 7-12 min
Signals: "vs", "better", "which", two competing subjects,
rival product launches, head-to-head topics
Hook: Show both subjects. Promise a definitive verdict.
Structure: One round per criterion. Round verdict after each.
Never reveal final winner before climax.
Payoff: Clear winner declared. Callback to pre-commitment hook.
SEED 2: EXPLAINER
Tension: OPEN_LOOP_CLOSE
Length: 6-10 min
Signals: "how does", "what is", "why does", complex topics,
things most people misunderstand
Hook: Most counterintuitive consequence of the topic.
Structure: 3 layers — surface, mechanism, implication.
Midpoint partial close + bigger loop opened.
Payoff: Full loop close. The answer, completely.
SEED 3: NEWS_BREAKDOWN
Tension: OPEN_LOOP_CLOSE
Length: 5-9 min
Signals: Breaking news, announcements, events, Rex BREAKING_REACTIVE
Hook: Headline moment + "here's what everyone is missing."
Structure: 3 angles — what happened, why it happened, what next.
Payoff: Missing angle delivered + forward-looking verdict.
SEED 4: DEEP_DIVE
Tension: DOCUMENTARY_ARC
Length: 12-18 min
Signals: Full story behind something, origin stories, complex history
Hook: Drop into the most consequential moment of the story.
Structure: 4 self-contained chapters. Each has own arc.
Payoff: Full circle — return to cold open, answer central question.
SEED 5: SHOCKING_FACTS
Tension: RAPID_DOPAMINE
Length: 5-8 min
Signals: "did you know", surprising statistics, counterintuitive data,
facts that challenge assumptions, Rex viral fact signals
Hook: Second-most shocking fact first. Save the best for last.
Structure: Each fact is its own micro-payoff. Fastest pacing of all.
Visual cut every 2-3 seconds. Tease the best fact early.
Payoff: The one fact that reframes everything before it.
SEED 6: MYTH_BUST
Tension: COURTROOM
Length: 6-10 min
Signals: Rumours circulating on X/Reddit, viral claims,
"is it true that", things people believe may be wrong
Hook: State the rumour seriously. "You've probably heard this."
Structure: Present evidence FOR the rumour first — build suspense.
Then evidence AGAINST. Viewer genuinely uncertain at midpoint.
Payoff: Verdict with evidence. Confirmed, busted, or complicated.
RRQ always delivers a conclusion — never sits on the fence.
SEED 7: SPECULATION
Tension: POSSIBILITY_ESCALATION
Length: 7-12 min
Signals: Future events, leaked roadmaps, predictions, "what if",
upcoming announcements, industry direction signals
Hook: Most extreme credible outcome. "This could actually happen."
Structure: Each scenario more consequential than the last.
Ground in real evidence. Distinguish confirmed vs probable vs possible.
Payoff: Most likely scenario stated clearly + what signals to watch for.
SEED 8: HOT_TAKE
Tension: THESIS_PRESSURE_TEST
Length: 6-10 min
Signals: Controversial positions, industry debates, consensus
being challenged, things "everyone" believes RRQ questions
Hook: The thesis. Stated clearly. No hedging. First 30 seconds.
Structure: Steel-man the opposing view genuinely — not a straw man.
Then defend original thesis with specific evidence.
Payoff: Thesis defended. Viewer either convinced or equipped
to disagree intelligently. Never a wishy-washy ending.
SEED 9: COUNTDOWN
Tension: COUNTDOWN_BUILD
Length: 7-14 min
Signals: Rankings, top lists, best/worst, most/least, anything
with a natural ranked order
Hook: "Number one will genuinely surprise you." Show a glimpse.
Structure: Start from bottom. Each entry better than the last.
Each entry needs its own mini-hook to justify screen time.
Payoff: #1 reveal + why it beats everything else on the list.
SEED 10: CASE_STUDY
Tension: NARRATIVE_ARC
Length: 10-15 min
Signals: One company, person, event, or decision — full story.
Success stories, failures, controversies, turning points.
Hook: Most dramatic moment of the story. Establish stakes.
Structure: Setup → conflict → turning point → resolution.
Character-driven. The subject must feel real.
Payoff: Resolution + the lesson that applies beyond this one story.
Format Selector — Three-Signal Algorithm
MUSE never picks a format randomly or defaults to the obvious choice.
It runs three signals simultaneously and combines them.
export async function selectFormat(
brief: QeonBrief,
humanOverride?: string
): Promise<{ format: VideoFormat; confidence: number }> {
if (humanOverride) {
const formats = await getFormatLibrary();
const override = formats.find(f => f.id === humanOverride);
if (override) return { format: override, confidence: 100 };
}
const formats = await getFormatLibrary();
const activeFormats = formats.filter(f => f.status !== "DEPRECATED");
const topicScores = await scoreByTopicSignal(brief, activeFormats);
const recentFormats = await getRecentFormatHistory(14);
const portfolioScores = scoreByDiversity(activeFormats, recentFormats);
const performanceScores = activeFormats.map(f => ({
formatId: f.id,
score: (f.performanceWeight / 100) * 30,
}));
const combined = activeFormats
.map(f => {
const topic = topicScores.find(s => s.formatId === f.id)?.score ?? 0;
const portfolio = portfolioScores.find(s => s.formatId === f.id)?.score ?? 0;
const perf = performanceScores.find(s => s.formatId === f.id)?.score ?? 15;
return {
format: f,
totalScore: topic + portfolio + perf,
breakdown: { topic, portfolio, performance: perf },
};
})
.sort((a, b) => b.totalScore - a.totalScore);
const winner = combined[0];
if (winner.totalScore < 45) {
await flagForHumanReview(brief, winner, combined.slice(1, 4));
}
await saveFormatSelection(brief.topicId, winner.format.id, winner.breakdown);
return { format: winner.format, confidence: winner.totalScore };
}
function scoreByDiversity(
formats: VideoFormat[],
recentFormatIds: string[]
): Array<{ formatId: string; score: number }> {
return formats.map(f => {
const uses = recentFormatIds.filter(r => r === f.id).length;
return { formatId: f.id, score: Math.max(0, 30 - uses * 10) };
});
}
ORACLE Format Discovery Pipeline
ORACLE actively watches YouTube for new format patterns.
When a new structural pattern appears consistently in top performing
videos, ORACLE names it, documents it, and adds it to the library.
MUSE picks it up on its next run.
{
id: "VIDEO_FORMAT_LIBRARY",
name: "Video Format Discovery & Library Maintenance",
primaryAgent: "MUSE",
secondaryAgents: ["QEON", "REGUM"],
researchDepth: "DEEP",
queries: [
"new YouTube video formats gaining traction {currentMonth} {currentYear}",
"YouTube video structure trends {currentYear}",
"viral YouTube format analysis top creators {currentYear}",
"what video formats are performing best YouTube {currentMonth} {currentYear}",
],
discoveryThreshold: {
minVideos: 3,
withinDays: 60,
mustBeDistinct: true,
mustBeReproducible: true,
},
onNewFormatFound: `
1. Name it descriptively (not branded)
2. Document: tension curve, ideal length, topic signals, beat structure,
hook style, payoff structure
3. Set status: EMERGING, performanceWeight: 50 (neutral)
4. Add to format library in RAG
5. Notify Zeus: "New format EMERGING: [name] — available to MUSE"
`,
onDecliningFormat: `
1. Set status: DECLINING
2. Notify Zeus for awareness
3. Do NOT set DEPRECATED — Zeus confirms deprecation (human in loop)
`,
}
Format Performance Feedback Loop
Zeus runs this weekly. Updates performanceWeight in the format library.
This is how MUSE learns what actually works on RRQ's channel.
export async function updateFormatPerformanceWeights(): Promise<void> {
const videoMemory = await getVideoMemoryByFormat(30);
const formatStats = new Map<string, { total: number; count: number }>();
for (const v of videoMemory) {
if (!v.formatId || !v.retentionPercent) continue;
const s = formatStats.get(v.formatId) ?? { total: 0, count: 0 };
formatStats.set(v.formatId, { total: s.total + v.retentionPercent, count: s.count + 1 });
}
const formats = await getFormatLibrary();
const updated = formats.map(f => {
const stats = formatStats.get(f.id);
if (!stats || stats.count < 3) return f;
const avgRetention = stats.total / stats.count;
const newWeight = Math.min(100, Math.max(0, avgRetention * 2));
return { ...f, performanceWeight: Math.round(newWeight) };
});
await updateFormatLibraryInRAG(updated);
for (const f of updated) {
const orig = formats.find(o => o.id === f.id);
if (orig && f.performanceWeight < orig.performanceWeight - 20) {
await notifyOracle("FORMAT_DECLINING", f);
}
}
}
Dashboard — Human Override Panel
MUSE — Format Selection Override
Current video in queue:
Topic: "OpenAI just announced GPT-5 — what changed"
MUSE pick: NEWS_BREAKDOWN (confidence: 78/100)
Signals: topic: 38 | diversity: 25 | performance: 15
Override: [HOT_TAKE ▼] [Apply Override]
Alternatives:
SPECULATION 61 "leaked roadmap angle exists"
HOT_TAKE 54 "controversial take possible"
COMPARISON 44 "vs Claude angle available"
Format Library Health:
ACTIVE: 10 Avg weight: 62
EMERGING: 2 (ORACLE found last cycle)
DECLINING: 1 NEWS_BREAKDOWN -18pts this month
DEPRECATED: 0
[View Full Library] [Run Format Analysis]
Where MUSE Sits in Qeon's Pipeline
QEON receives QeonBrief from Regum
↓
STEP 0: Zeus memory injection (includes ORACLE updates for MUSE)
↓
STEP 1a: MUSE format selection
→ queries format library from ORACLE RAG
→ runs three-signal selector
→ checks for human override in dashboard
→ flags low confidence to Zeus if score < 45
→ returns VideoFormat
↓
STEP 1b: MUSE blueprint generation
→ queries ORACLE for latest retention intel
→ Opus generates second-by-second MuseBlueprint
→ engineers retention devices at all four walls
→ maps all open loops with close points
→ assigns visual type + instruction to every beat
↓
STEP 2: Research (Bedrock + Cloudflare)
Fills blueprint beats with real content
↓
STEP 3: Script writing (Opus)
Writes INTO the blueprint. Never a blank page.
↓
STEP 4–11: Rest of Qeon pipeline unchanged
Voice Architecture — ElevenLabs Cue System
Muse owns the voice architecture for every video. This is not just
the script — it is the directing layer that ElevenLabs reads as
performance instructions. Muse embeds cues directly into the script
at the point they should fire. ElevenLabs renders them as human
vocal performance.
Cue Vocabulary
[PAUSE — Xs] Deliberate silence. X = duration in seconds.
Use before a reveal, a question, or a pivot.
e.g. [PAUSE — 1.5s]
[RISE] Energy builds. Curiosity, anticipation, building tension.
Voice lifts in pitch and pace slightly.
Use approaching a reveal or key claim.
[PEAK] Maximum energy. Confident, declarative, authoritative.
The moment the argument lands.
Use at the climax of a section.
[DROP] Energy falls. Reflective, considered, measured.
Use after a peak — contrast creates rhythm.
[WARM] Conversational, direct, like talking to one person.
Use for community question, personal observation.
[QUESTION] Open, curious, slightly uncertain.
Voice lifts at end, leaves space.
Use for rhetorical questions — never closes immediately.
[PIVOT] Tonal shift. Previous thread closes, new one opens.
Pace resets. Brief neutral before new energy.
e.g. "...and that's where it gets interesting. [PIVOT]
Because while we were debating that, Anthropic just..."
[EMPHASIS — word] Single word stressed. Bold, punched.
e.g. "This is not [EMPHASIS — just] a benchmark result."
[BREATH] Natural breath pause. Humanises the delivery.
Use every 3–4 sentences in body sections.
Cue Placement Rules
Hook (0:00–0:30):
RISE toward the question
PAUSE before the hook payoff
QUESTION at the open loop
Body sections:
BREATH every 3–4 sentences
RISE approaching evidence reveal
PEAK at key claim delivery
DROP after peak for contrast
PIVOT at section transitions
Midpoint re-hook:
PAUSE before the pivot
PIVOT cue mandatory
RISE into new thread
Community question (closing):
WARM tone throughout
QUESTION cue on the viewer prompt
PAUSE after — leave space for the question to land
Script with Embedded Cues — Example
[RISE] What if the benchmark you've been trusting for six months
is measuring the wrong thing entirely? [PAUSE — 1.8s]
[PEAK] Most AI comparisons test chat. We tested code.
Real tasks. Real codebases. Real failures.
[DROP] And what we found should change how you pick your tools.
[BREATH]
[PIVOT] Now — before we get into the results. [PAUSE — 1.0s]
[RISE] Anthropic just dropped a Claude update mid-way through
our testing. [EMPHASIS — mid-way]. Which meant we had to run
the whole suite again. [WARM] And honestly? That was the
most interesting part.
[BREATH]
[QUESTION] So which model would you trust with your codebase?
[PAUSE — 2.0s] Let's find out.
The Perspective Engine
Muse is responsible for the authorial voice — the layer that makes
a script feel like a person with opinions, not a summary being read.
Every script Muse builds must pass four perspective tests:
TEST 1 — The Opening Question
Does the script open with a genuine question that creates
cognitive tension? Not rhetorical decoration — a real question
the viewer will stay to have answered.
Rule: question must be answerable only by watching to the end.
TEST 2 — The Deliberate Pivot
Does the script contain at least one moment where the frame
shifts unexpectedly? New evidence, a contradiction, an update,
a surprising finding that recontextualises what came before.
Pattern: "...while we think about that / while we were doing X /
just as this was happening — [new thing] arrived and
it changes the picture."
Rule: pivot must be earned — not inserted randomly.
TEST 3 — The Non-Obvious Angle
Is the main argument something a viewer could not get from
reading the source article? Does the script add a test,
a reframe, a comparison, a lived observation, a prediction?
Rule: if the script could be replaced by a link to the source,
it failed this test.
TEST 4 — The Open Invitation
Does the closing invite the viewer into a genuine conversation?
Not "let me know in the comments" — a real question that the
viewer has an opinion on and wants to answer.
Rule: community question must be specific to THIS video's
argument, not generic engagement bait.
Perspective Engine Scoring
Muse self-scores before submitting council position:
interface PerspectiveScore {
openingQuestion: { passed: boolean; question: string };
deliberatePivot: { passed: boolean; pivotMoment: string; timestamp: string };
nonObviousAngle: { passed: boolean; angle: string; sourceArticleTest: boolean };
openInvitation: { passed: boolean; communityQuestion: string };
overallScore: number;
}
Muse's Council Role
In the On The Line council Muse speaks fifth — after ARIA and Qeon
have confirmed fit and feasibility. Muse then answers:
"Can I build a sequence that will hold a viewer and convert?"
Muse's council position includes:
- Sequence draft (hook, pivot, community question)
- Voice architecture outline (opening tone, pivot cue, closing energy)
- Perspective angle (what unique POV this script takes)
- Confidence score (0–100 — Muse's belief in the sequence)
Muse must score confidence ≥ 75 for GREEN.
Muse votes YELLOW at 60–74 — sequence works but needs refinement.
Muse votes RED below 60 — Muse does not believe in this one.
Muse's RED is not a creative veto. It is a signal that the angle
as briefed does not have a sequence that will retain viewers.
The council takes this seriously — Muse is the only agent who
can see the retention curve before a frame is produced.
After council approval — Muse receives the full CouncilBrief
and builds the complete MuseBlueprint with all voice cues embedded.
This is the document Qeon writes into. This is what Vera checks against.
Channel Tone Injection
Muse reads channelTone from user-settings before building every MuseBlueprint.
Tone shapes narrative pacing and beat density — not the topic or angle.
Tone → Blueprint Mapping
const TONE_INSTRUCTIONS: Record<string, string> = {
analytical: `
Prioritise data beats. Every body section should anchor to a statistic, study,
or structured comparison. Tension/release hooks are secondary to credibility
signals. Preferred beat types: CHART, DIAGRAM, SLIDE, stat-callout.
Avoid speculative language — ground claims before moving on.
`,
explanatory: `
Prioritise clarity over pace. Use analogy and example before abstract principle.
Each section should answer one question completely before opening the next.
Preferred beat types: CONCEPT_IMAGE, SECTION_CARD, step-by-step sequence.
Bridge lines must be explicit — assume viewer needs the connection spelled out.
`,
critical: `
Lead with the counter-intuitive position. Every section earns its place by
challenging the obvious answer. Preferred structure: establish the conventional
view → undermine it → offer a better frame. Voice cues: PEAK and DROP used
more frequently. Opinions are first-person and owned, not hedged.
`,
entertainment: `
Prioritise tension/release dynamics. Story beats outweigh data beats.
Emotional escalation should reach a peak by the two-thirds mark.
Bridge lines create suspense, not summaries. CTA is earned through emotional
investment, not logical argument. Preferred visual: B_ROLL and TALKING_HEAD.
`,
hybrid: `
Balance data credibility with narrative engagement. Alternate between grounding
the viewer in facts and pulling them forward with story. No section should be
purely informational or purely emotional. Default pacing: moderate.
`
};
Confidence Weighting
If channelTone.confidence < 0.5 (user was uncertain or selected "Not sure yet"):
- Tone instructions are applied at 50% weight — Muse blends hybrid defaults
- Muse notes in its council position:
"Tone signal weak — applied partial hybrid blend"
- After 5 videos, Oracle suggests a refinement (see onboarding skill)
If channelTone.confidence >= 0.5:
- Tone instructions applied at full weight
- Muse does not note tone in council position — it is treated as baseline
Secondary Tone
If channelTone.secondary is set, Muse applies it to body sections only.
Hook and CTA always follow the primary tone — consistency at the emotional peaks matters
more than variety in the middle.
VisualBrief Generation — Dynamic Visual Direction for TONY
MUSE generates a VisualBrief for every beat that will be executed by TONY.
This is not a static template — it is written fresh per beat by Opus, informed
by Oracle's current visual meta, the channel tone, the tension curve, and
the beat's position in the retention arc.
What MUSE Knows at Brief Time
Channel tone → analytical / entertainment / critical / etc.
Tension curve → ESCALATING_REVEAL / RAPID_DOPAMINE / COUNTDOWN_BUILD / etc.
Beat position → hook / body / climax / outro
Beat type → SECTION_CARD / STAT_CALLOUT / GRAPHIC_OVERLAY / etc.
Script content → what is being said at this moment
Oracle visual meta → what styles are ESTABLISHED / EMERGING right now
Oracle Query at Blueprint Time
Before generating VisualBriefs, MUSE queries Oracle's VISUAL_META_LIBRARY:
const visualMeta = await queryOracleKnowledge(
`Return VisualMetaEntry records matching:
tone: ${channelTone.primary}
beatPositions: ${beatsNeedingTony.map(b => b.position)}
status: ESTABLISHED or EMERGING (not DECLINING or DEPRECATED)`,
"VISUAL_META_LIBRARY"
);
VisualBrief Schema
interface VisualBrief {
mood: string;
animationStyle: string;
typography: string;
colorTreatment: string;
durationSeconds: number;
entryAnimation: string;
exitAnimation: string;
beatPosition: "hook" | "body" | "climax" | "outro";
inspiredBy: string[];
currentMeta: string;
metaSource: string;
mustAvoid: string[];
accessibilityNote: string;
}
Tone → Visual Direction Mapping
MUSE maps channel tone + beat position to visual direction before querying Oracle:
const TONE_VISUAL_DEFAULTS: Record<string, Record<string, Partial<VisualBrief>>> = {
analytical: {
hook: { mood: "precise, authoritative, data-forward", colorTreatment: "monochrome with amber data accent" },
body: { mood: "clinical, structured, credible", animationStyle: "clean slide-in, no decorative motion" },
climax: { mood: "decisive, high-contrast, verdict-weight", animationStyle: "bold stat lands hard, no bounce" },
},
entertainment: {
hook: { mood: "electric, kinetic, immediate", animationStyle: "fast enter with spring overshoot, energy burst" },
body: { mood: "engaging, dynamic, story-driven", animationStyle: "smooth transitions, visual momentum maintained" },
climax: { mood: "peak energy, maximum impact", animationStyle: "rapid sequence, tight cuts, maximum visual density" },
},
critical: {
hook: { mood: "provocative, confident, thesis-first", colorTreatment: "high contrast, sharp edges, no softness" },
body: { mood: "forensic, deliberate, evidence-weighted", animationStyle: "methodical reveal, each element earns its place" },
climax: { mood: "verdict weight, unambiguous", animationStyle: "single element dominates, no clutter" },
},
explanatory: {
hook: { mood: "curious, approachable, clarity-first", animationStyle: "gentle unfold, concept builds piece by piece" },
body: { mood: "clear, layered, educational", animationStyle: "sequential reveal, each layer adds to the last" },
climax: { mood: "satisfying resolution, full picture revealed", animationStyle: "all elements come together, complete composition" },
},
};
How MUSE Writes a VisualBrief
Opus generates the VisualBrief in natural language — precise enough that Haiku
can translate it directly to Remotion code without interpretation.
MUSE does NOT write code. MUSE writes intent.
TONY does NOT interpret intent. TONY executes instruction.
The brief must be specific enough that two different Haiku runs would produce
similar output. Vague briefs produce inconsistent output.
Good brief (specific):
"Stat callout: large number '847M' in Syne bold 96px white, counts up from 0
over 45 frames with spring easing (stiffness 80, damping 12). Amber label
'MONTHLY ACTIVE USERS' in DM Mono 13px sits 16px above. On landing frame:
amber glow pulse radiates from number, rgba(245,166,35,0.4) expanding ring,
300ms duration. Background #0a0a0a. Enters from bottom over 18 frames.
Total duration: 3.5 seconds at 30fps."
Bad brief (vague):
"Make a nice stat card with the number and some animation."
Updated Checklist
[ ] Create lib/muse/format-library.ts — dynamic format queries + types
[ ] Create lib/muse/format-selector.ts — three-signal algorithm
[ ] Create lib/muse/blueprint-generator.ts — Opus beat generation
[ ] Create lib/muse/retention-devices.ts — device library
[ ] Create lib/muse/performance-feedback.ts — Zeus weekly weight updates
[ ] Create lib/muse/voice-architecture.ts — ElevenLabs cue embedding
[ ] Create lib/muse/perspective-engine.ts — four perspective tests + scoring
[ ] Create lib/muse/council-position.ts — council sign-off builder
[ ] Add VideoFormat + MuseBlueprint to shared types
[ ] Add ElevenLabs cue vocabulary to shared types
[ ] Add VIDEO_FORMAT_LIBRARY domain to ORACLE research domains
[ ] Seed 10 formats into ORACLE RAG on first deployment
[ ] Plug MUSE into Qeon pipeline as Steps 1a + 1b
[ ] Plug MUSE into On The Line council as position 5
[ ] Update script-writer to write INTO MuseBlueprint beats
[ ] Update script-writer to respect embedded voice cues
[ ] Update video-pipeline to read visualType from each beat
[ ] Update ElevenLabs worker to parse and act on cue vocabulary
[ ] Add format override panel to Zeus Command Center dashboard
[ ] Add format library health panel to Zeus Command Center
[ ] Add updateFormatPerformanceWeights() to Zeus Sunday job
[ ] Test low-confidence flag flow end to end
[ ] Test perspective engine — all four tests against sample scripts
[ ] Test voice cue embedding — verify ElevenLabs renders correctly
[ ] Test council position builder — confidence scoring
[ ] Verify ORACLE format discovery adds to library correctly
[ ] Test tone injection: analytical → verify data beat density increases
[ ] Test tone injection: entertainment → verify tension/release device count increases
[ ] Test tone default (hybrid) → verify neutral pacing applied
[ ] Create lib/muse/visual-brief-generator.ts — Opus generates VisualBrief per beat
[ ] Wire Oracle VISUAL_META_LIBRARY query into blueprint generation (before tonyTasks built)
[ ] Add VisualBrief to tonyTasks schema in shared types
[ ] Test: analytical + hook → brief matches monochrome/amber data-forward spec
[ ] Test: entertainment + climax → brief matches kinetic/high-energy spec
[ ] Verify Oracle visual meta is populated before first MUSE blueprint run
