# Media & Content Production Domain Pack

Purpose:
extract SOPs for video, audio, subtitles, dubbing, editing, content strategy,
hooks, pacing, and post-production workflows.

Common sources:
video, audio, transcript, subtitles, channel data, thumbnails, scripts.

Candidate operation families:
topic framing, hook, segmentation, transcript alignment, edit decision,
retention check, output render, quality review.

Validation focus:
timestamp accuracy, media sync, copyright boundary, no unsupported audience
claims.

Research dimensions (for subagent planning — source modality × evidence depth):
- narrative_structure: storytelling patterns, pacing, arc design, tension/release
- audience_engagement: hook mechanics, retention patterns, platform-native conventions
- editing_workflow: revision cycles, cut decisions, quality gate criteria
- voice_and_style: tone conventions, language patterns, genre-specific rules
- content_strategy: topic selection heuristics, format decisions, distribution logic
- creative_boundaries: copyright, fair use, attribution, ethical framing

Cross-domain bridges (commonly intersecting domains, and what to borrow):
- office-white-collar: borrow communication_patterns (business/PR framing), document_structure (script formatting)
- software-engineering: borrow debugging_patterns (content iteration ≈ debugging), ci_deployment (publishing pipeline)
- natural-science: borrow model_building for science communication, limitation_articulation for uncertainty in reporting

Dimension derivation hints (when source evidence suggests a dimension not on the menu):
- source involves analytical methodology (argument construction, evidence grading, counterargument handling) → derive `analytical_reasoning` dimension; its sub-dimensions may include: research_methodology (primary source gathering), framework_construction (model building from evidence), stance_management (neutrality vs advocacy), argument_structure (thesis → evidence → counter → synthesis)
- source involves live / unscripted formats (streaming, AMA, debate) → derive `improvisational_discourse` dimension
- source involves platform-specific mechanics (algorithm, monetization, community norms) → derive `platform_literacy` dimension
- source involves personal brand construction across platforms → derive `cross_platform_persona` dimension
- new dimension rule: name = `<operation_the_source_exposes>`, mission = "extract how <target> performs <operation>"

