Sequence Planning
Nine tools for arranging scenes into timed sequences, generating and comparing variants, working with sequence archetypes, and using style packs and brief templates.
plan_sequence
Plan a sequence from analyzed scenes and a style pack. Decides shot order, hold durations, transitions, and camera overrides. Scenes must already carry metadata — run analyze_scene first. Supports per-scene style blending via metadata.style_override, and beat-synced editing when you pass beats from analyze_beats.
- Name
scenes- Type
- object[]
- Description
Required. Array of scene objects with metadata (
content_type,visual_weight,motion_energy,intent_tags). Setmetadata.style_overrideon individual scenes to blend style packs per scene.
- Name
style- Type
- string
- Description
Required. Default style pack for the sequence —
prestige,energy,dramatic,minimal,intimate,corporate,kinetic, orfade. Per-scene overrides take precedence.
- Name
beats- Type
- object
- Description
Beat analysis data from
analyze_beats. When supplied, scene durations snap to beat boundaries and camera intensities match audio energy.
- Name
duration_target_s- Type
- number
- Description
Target total duration in seconds (from the brief). Style-pack hold durations are scaled proportionally to land within ±0.5s, with each scene's own duration_s as the floor. If the floor sum exceeds the target, durations stay at the floor and notes.duration_target.warning is set. Omit to use raw style-pack durations.
- Name
preserve_source_order- Type
- boolean
- Description
Default true: keep the input scene order (upstream already establishes narrative order). Set false to let the planner reorder by intent buckets / energy curve — the rewrite is logged in notes.ordering_rationale and notes.ordering_mode.
Returns: A valid sequence manifest with scene order, durations, transitions, camera overrides, and editorial notes.
Try asking your AI:
"Plan a sequence from these 5 analyzed scenes with the prestige style pack"
"Plan a beat-synced sequence using these scenes, energy style, and these beats"
plan_variants
Plan multiple sequence variants from the same scenes with different styles. Each style produces an independent manifest. Use this to generate A/B choreography options for comparison.
- Name
scenes- Type
- object[]
- Description
Required. Array of analyzed scene objects with metadata (run
analyze_scenefirst).
- Name
styles- Type
- string[]
- Description
Required. Array of style pack names to generate variants for. Minimum 2 styles.
- Name
sequence_id- Type
- string
- Description
Base sequence ID. Each variant receives a suffixed ID (e.g.,
seq_hero__prestige).
- Name
duration_target_s- Type
- number
- Description
Target total duration in seconds, applied to every variant (see plan_sequence).
- Name
preserve_source_order- Type
- boolean
- Description
Default true: keep input scene order across all variants. Set false to let the planner reorder (logged per variant).
Returns: An array of variants — one manifest per style — ready to feed into compare_variants.
compare_variants
Score and rank multiple sequence variants. Evaluates each variant across pacing, variety, flow, and adherence dimensions. Use after plan_variants to pick the best choreography.
- Name
variants- Type
- object[]
- Description
Required. Array of variant objects from
plan_variantsoutput — each shaped as{ variant_id, style, manifest }.
- Name
scenes- Type
- object[]
- Description
Required. The same analyzed scene objects used for
plan_variants.
Returns: Ranked results with per-dimension comparison (pacing, variety, flow, adherence) and an overall winner.
Try asking your AI:
"Compare these plan_variants outputs against my scene array"
evaluate_sequence
Score a planned sequence manifest against style rules and cinematography principles. Handles per-scene style blending — scenes with metadata.style_override are scored against their override pack.
- Name
manifest- Type
- object
- Description
Required. Sequence manifest from
plan_sequence(must have ascenesarray).
- Name
scenes- Type
- object[]
- Description
Required. The same analyzed scene objects used for
plan_sequence.
- Name
style- Type
- string
- Description
Required. Default style pack to evaluate against. Per-scene
metadata.style_overridevalues take precedence.
Returns: Pacing, variety, flow, and adherence scores (0-100) with findings per dimension.
Try asking your AI:
"Evaluate this manifest against the prestige style pack"
validate_manifest
Validate a sequence manifest against camera guardrails. Checks speed limits, acceleration easing, jerk/settling, lens bounds, and personality boundaries for each scene. Returns a PASS/WARN/BLOCK verdict with per-scene diagnostics.
- Name
manifest- Type
- object
- Description
Required. Sequence manifest from
plan_sequence(must have ascenesarray).
- Name
personality- Type
- string
- Description
Required. Personality to validate against —
cinematic-dark,editorial,neutral-light, ormontage.
Returns: PASS / WARN / BLOCK verdict plus per-scene diagnostics covering speed, easing, jerk, lens bounds, and personality boundaries.
Try asking your AI:
"Validate this manifest against the editorial personality before I render"
recommend_sequence_archetype
Recommend a sequence archetype (multi-scene recipe) for a given output type. Returns scene roles, transitions, camera progression, pacing profile, and recommended primitives.
- Name
output_type- Type
- string
- Description
Required. What kind of video —
brand-teaser,feature-reveal,onboarding-explainer,launch-reel,testimonial-cutdown,social-loop, or a freeform description.
- Name
personality- Type
- string
- Description
Filter archetypes by personality compatibility —
cinematic-dark,editorial,neutral-light, ormontage.
- Name
duration_s- Type
- number
- Description
Target duration in seconds. Helps narrow archetype selection.
Returns: The recommended archetype with scene roles, transitions, camera progression, pacing profile, and recommended primitives.
Try asking your AI:
"Recommend an archetype for a 30-second brand-teaser in cinematic-dark"
instantiate_sequence_archetype
Generate a manifest skeleton from an AI demo sequence archetype (prompt_to_answer, brief_to_board, query_to_report, upload_to_insight). Returns pre-configured scenes with timing, transitions, and camera intent.
- Name
archetype_slug- Type
- string
- Description
Required. Archetype slug (e.g.,
prompt_to_answer,brief_to_board).
- Name
personality- Type
- string
- Description
Personality —
cinematic-dark,editorial,neutral-light, ormontage.
- Name
duration_s- Type
- number
- Description
Target total duration in seconds.
- Name
content_hints- Type
- object
- Description
Optional content hints keyed by scene role.
Returns: A manifest skeleton with pre-configured scenes, timing, transitions, and camera intent.
Try asking your AI:
"Instantiate the prompt_to_answer archetype for a 25-second editorial demo"
get_style_pack
Retrieve a style pack definition. Ten packs are available: prestige, energy, dramatic, minimal, intimate, corporate, kinetic, fade, analog, and documentary.
- Name
name- Type
- string
- Description
Required. The style pack name to retrieve.
Returns: Style pack definition with cut cadence, camera preferences, transition weights, and timing biases.
Try asking your AI:
"Show me the prestige style pack definition"
list_brief_templates / get_brief_template
Browse and retrieve brief templates. Five templates are available for common video intents.
list_brief_templates takes no parameters and returns all templates with their IDs, names, descriptions, default style packs, and suggested scene counts.
- No parameters.
- Name
template_id- Type
- string
- Description
Required. Brief template ID (e.g.,
product-launch,brand-story,tutorial).
Returns: The full template with section structure, suggested layouts/content types per section, defaults (style pack, tone, duration), and an example brief.
Try asking your AI:
"What brief templates are available?"
"Show me the product-launch brief template"