"""Retention Studio strategy for mechanism-explainer Shorts. This module captures the strategic shift from generic AI-video output to a semi-automatic retention studio: format mining, idea scoring, mechanism-reveal scripts, motion-led visuals, and human quality gates. """ from __future__ import annotations from typing import Any PILOT_IDEAS: list[dict[str, Any]] = [ { "title": "Warum ein billiges Auto teuer sein kann", "pillar": "hidden_money_systems", "hook": "Dieser Occasion-Käufer denkt, er spart 4'000 Franken.", "mechanism": "Röntgenblick durch Preis, Reparaturrisiko, Standtage, Händler-Marge und Liquidität.", "twist": "Der teuerste Fehler ist nicht der Kaufpreis, sondern der Zeitpunkt.", "visual_mechanism": "3D car x-ray with price tag layers and risk meters", "surprise": 9, "data_basis": 8, "comment_potential": 8, "series_potential": 9, "monetization_potential": 10, "production_effort": 6, }, { "title": "Warum 0%-Finanzierung nicht kostenlos ist", "pillar": "hidden_money_systems", "hook": "0 Prozent klingt gratis. Genau deshalb funktioniert es.", "mechanism": "Cash price, margin, fees, lock-in and opportunity cost split into layers.", "twist": "Die Zinsen verschwinden nicht. Sie verstecken sich nur im System.", "visual_mechanism": "contract transforms into hidden cost layers", "surprise": 8, "data_basis": 7, "comment_potential": 8, "series_potential": 9, "monetization_potential": 10, "production_effort": 5, }, { "title": "Warum KI-Agenten ohne Regeln gefährlich werden", "pillar": "ai_agents_explained", "hook": "Dieser Agent wirkt produktiv. Bis er ohne Kontrolle Geld verbrennt.", "mechanism": "Goal, tool access, missing approval gates, runaway loops and audit trail visualized as a circuit.", "twist": "Der Fehler ist nicht Intelligenz. Der Fehler ist fehlende Begrenzung.", "visual_mechanism": "agent control loop with approval gate and red runaway path", "surprise": 8, "data_basis": 8, "comment_potential": 9, "series_potential": 10, "monetization_potential": 10, "production_effort": 5, }, { "title": "Der Unterschied zwischen Chatbot und echtem Agenten", "pillar": "ai_agents_explained", "hook": "Ein Chatbot antwortet. Ein Agent verändert Dinge.", "mechanism": "Intent, tools, memory, approvals and execution are revealed as separate layers.", "twist": "Autonomie beginnt nicht beim Modell, sondern beim Zugriff.", "visual_mechanism": "split-screen chatbot bubble vs agent toolchain", "surprise": 7, "data_basis": 8, "comment_potential": 8, "series_potential": 10, "monetization_potential": 9, "production_effort": 4, }, { "title": "Warum ein Bauprojekt fast nie beim ersten Preis bleibt", "pillar": "swiss_systems_explained", "hook": "Dieser Baukredit sieht fix aus. Dann beginnt die Schweiz.", "mechanism": "Permits, objections, procurement, inflation, scope creep and political process stack over time.", "twist": "Die Überraschung ist nicht der Mehrpreis. Es ist die Anzahl unsichtbarer Schleifen.", "visual_mechanism": "construction budget timeline with process gates and cost bubbles", "surprise": 8, "data_basis": 8, "comment_potential": 7, "series_potential": 9, "monetization_potential": 7, "production_effort": 7, }, { "title": "Wie ein Einwohnerrat wirklich entscheidet", "pillar": "swiss_systems_explained", "hook": "Das sieht wie eine Abstimmung aus. Eigentlich ist es ein Filtersystem.", "mechanism": "Motion, committee, budget, party positions, amendments and final vote visualized as a flow.", "twist": "Die Entscheidung fällt oft, bevor die Schlussabstimmung beginnt.", "visual_mechanism": "council chamber turns into decision flowchart", "surprise": 7, "data_basis": 8, "comment_potential": 7, "series_potential": 8, "monetization_potential": 6, "production_effort": 6, }, { "title": "Warum dein Gehirn schlechte Deals schönrechnet", "pillar": "dangerous_thinking_errors", "hook": "Dein Gehirn merkt diesen schlechten Deal. Dann verteidigt es ihn.", "mechanism": "Sunk cost, confirmation bias and loss aversion appear as competing brain layers.", "twist": "Du kaufst nicht den Deal. Du kaufst die Rechtfertigung.", "visual_mechanism": "brain courtroom where biases argue over a bad deal", "surprise": 9, "data_basis": 7, "comment_potential": 9, "series_potential": 10, "monetization_potential": 8, "production_effort": 5, }, { "title": "Warum du Verlusten hinterherrennst", "pillar": "dangerous_thinking_errors", "hook": "Der Verlust ist vorbei. Trotzdem zahlst du weiter.", "mechanism": "Loss aversion, sunk cost and identity protection visualized as a trap door.", "twist": "Du rettest nicht dein Geld. Du rettest dein altes Ich.", "visual_mechanism": "money trail into trap with brain pulling it back", "surprise": 8, "data_basis": 7, "comment_potential": 8, "series_potential": 9, "monetization_potential": 8, "production_effort": 5, }, { "title": "Warum Shorts dich nicht wegen dem Inhalt halten", "pillar": "hidden_attention_systems", "hook": "Du bleibst nicht wegen dem Thema. Du bleibst wegen der offenen Schleife.", "mechanism": "Hook, open loop, pattern interrupt, payoff delay and reward pulse shown as timeline layers.", "twist": "Retention ist oft Architektur, nicht Interesse.", "visual_mechanism": "shorts timeline with dopamine gates and loop markers", "surprise": 9, "data_basis": 8, "comment_potential": 9, "series_potential": 10, "monetization_potential": 8, "production_effort": 5, }, { "title": "Warum Banken Gebühren verstecken können, ohne sie zu verstecken", "pillar": "hidden_money_systems", "hook": "Diese Gebühr ist sichtbar. Genau deshalb übersiehst du sie.", "mechanism": "Statement line items, percentage framing, monthly smoothing and comparison blindness.", "twist": "Das Versteck ist nicht Geheimhaltung. Es ist Normalisierung.", "visual_mechanism": "bank statement zooms into visible-but-ignored fee layers", "surprise": 8, "data_basis": 7, "comment_potential": 8, "series_potential": 9, "monetization_potential": 9, "production_effort": 5, }, ] def build_retention_studio_strategy() -> dict[str, Any]: """Return the strategic operating model for the channel.""" return { "positioning": "Hidden Money & AI Systems", "series": "Unsichtbare Systeme in 30 Sekunden", "claim": "Wir zeigen Dinge, die dich Geld, Zeit oder Kontrolle kosten – bevor du sie bemerkst.", "operating_model": "semi_automatic_retention_studio", "not_a": ["ki_avatar_news", "static_ai_slideshow", "reused_content_clone"], "core_formula": [ "0.0-1.5s pattern interrupt", "1.5-5s problem", "5-18s mechanism reveal", "18-28s twist", "28-35s follow trigger", ], "format_taxonomy": [ "mechanism_reveal", "mistake_breakdown", "hidden_cost", "before_after", "myth_vs_reality", "mini_simulation", "nobody_notices_this", ], "content_pillars": [ "hidden_money_systems", "auto_trade_mechanics", "ai_agents_explained", "swiss_systems_explained", "dangerous_thinking_errors", ], "quality_gates": { "static_card_limit": 0, "first_two_seconds_must_open_loop": True, "visual_change_every_seconds": 2, "human_review_percent": 20, "reused_content_allowed": False, "synthetic_media_disclosure_required_when_realistic": True, }, "metrics": [ "retention_1s", "retention_3s", "average_view_duration", "rewatches", "comments_per_1000_views", "follows_per_1000_views", ], } def score_idea(idea: dict[str, Any]) -> dict[str, Any]: """Score an idea for production priority.""" weights = { "surprise": 1.6, "data_basis": 1.2, "comment_potential": 1.2, "series_potential": 1.4, "monetization_potential": 1.4, } weighted = sum(float(idea.get(key, 0)) * weight for key, weight in weights.items()) visual_bonus = 12 if _has_specific_visual_mechanism(str(idea.get("visual_mechanism", ""))) else 0 effort_penalty = max(0, float(idea.get("production_effort", 5)) - 5) * 2 total = round(min(100, weighted / sum(weights.values()) * 8.8 + visual_bonus - effort_penalty)) if total >= 80: decision = "produce" elif total >= 65: decision = "develop" else: decision = "reject" return {"total": total, "decision": decision} def _has_specific_visual_mechanism(visual_mechanism: str) -> bool: generic_terms = {"generic montage", "talking avatar", "static image", "slideshow"} if visual_mechanism.lower().strip() in generic_terms: return False return any( token in visual_mechanism.lower() for token in ["x-ray", "röntgen", "layer", "simulation", "timeline", "flow", "split", "3d", "circuit"] ) def build_mechanism_explainer(idea: dict[str, Any]) -> dict[str, Any]: """Build a shot-by-shot mechanism explainer skeleton from a scored idea.""" follow_trigger = "Morgen zeige ich das nächste versteckte System, bevor es dich Geld kostet." return { "series": "Unsichtbare Systeme in 30 Sekunden", "title": idea["title"], "pillar": idea["pillar"], "target_seconds": 34, "follow_trigger": follow_trigger, "beats": [ { "beat": "pattern_interrupt", "start": 0.0, "end": 1.4, "voiceover": idea["hook"], "visual": f"{idea['visual_mechanism']} freezes mid-action with warning signal.", "visual_motion": "hard zoom, freeze-frame, red signal pulse", "sound_cue": "bass hit + camera snap", }, { "beat": "problem", "start": 1.4, "end": 5.0, "voiceover": "Das sieht harmlos aus, bis du den Mechanismus dahinter siehst.", "visual": "surface layer peels away to reveal the hidden system underneath.", "visual_motion": "layer peel + parallax push", "sound_cue": "whoosh + low tension tick", }, { "beat": "mechanism", "start": 5.0, "end": 18.0, "voiceover": idea["mechanism"], "visual": idea["visual_mechanism"], "visual_motion": "sequential layer reveal every 1-2 seconds", "sound_cue": "clicks per revealed layer", }, { "beat": "twist", "start": 18.0, "end": 28.0, "voiceover": idea["twist"], "visual": "camera snaps from obvious cause to real cause; wrong layer fades out.", "visual_motion": "snap zoom + wrong answer collapses", "sound_cue": "drop + silence beat", }, { "beat": "follow_trigger", "start": 28.0, "end": 34.0, "voiceover": follow_trigger, "visual": "series tag appears over a clean animated system map.", "visual_motion": "map draws itself, final node glows", "sound_cue": "resolve hit + soft riser", }, ], }