scenario-consistency — скилл для Claude Code: один образ во всех

---
name: scenario-consistency
description: "Use when one look must hold across Scenario generations: one character across scenes, a turnaround, or a video animated from its references, one product across angles, one style across icon sets, a character from an uploaded drawing, a variant off an approved baseline, or deciding when references stop scaling and a trained model is due. Triggers: make this match, same character, on-model, reference to video, style reference. Keywords: consistency, identity, control map, seed, LoRA."
license: MIT
---

# Scenario Consistency

## Overview

"Make variant two look exactly like variant one except for X" is the most repeated creative ask, and
agents reach for seeds, which do not solve it. Consistency comes from what you feed the model, in
rising order of durability: a prompt baseline, reference images, a style reference, a control map, a
trained model. The same references carry a character into video. Connection and the core loop: see
the `scenario` skill; training: see `scenario-model-training`; creating and filing a named character
or prop library, with its sheets: see `scenario-identity-library`. If a sibling skill named here is
missing from your available skills, ask the user to install it
(`npx skills add scenario-labs/skills --skill <name>`); unattended, proceed from tool schemas and
flag the gap.

## Quick reference

| Technique                  | Holds                             | Effort | Reach for it when                     |
| -------------------------- | --------------------------------- | ------ | ------------------------------------- |
| Baseline-plus-delta prompt | identity, framing, palette        | low    | always, it is the floor               |
| Reference image input      | identity and world                | low    | a set of scenes or angles             |
| Style reference input      | rendering style, not a subject    | low    | icon sets, UI, an illustration series |
| `asset_detect` control map | pose, geometry, composition       | medium | the layout must not move              |
| Reference-to-video         | identity through motion           | medium | the character or product has to move  |
| Seed reuse                 | one image's exact roll            | low    | re-rolling a single generation        |
| Trained model              | a house style, or a cast at scale | high   | references stop scaling (see below)   |

Scenario's published pipeline guidance: generate one strong reference image, pass it as an image
input to every scene generation, and prefer models with the most reference-image slots.

## The baseline-plus-delta prompt

Write out everything that must **not** change, then put the single change in a final clause. Keep
the prompt byte-identical between runs and edit only that clause. A change every item must carry
(new wardrobe, a haircut) is baseline, not delta: run it once as the hero's own delta, approve the
result as the new hero, then anchor the set to it, so the reference and the enumeration describe the
same look.

Enumerate specifics: subject geometry, camera height and angle, subject size and position in frame,
lighting direction, each named sub-element and where it sits, palette by name or hex, and embedded
text. Look at the baseline first (`asset_display`): you cannot enumerate a shade you have not seen,
and vague anchors drift.

Attach the approved baseline as a reference image alongside it. Image models converge on
`referenceImages`, but cap, requiredness, and cardinality come from `model_schema_get`, and on some
the field is a single scalar file. A scalar `image` plus `strength` is img2img, not a reference
slot: it anchors composition along with identity, and at the default strength a plain-background
anchor overrides a whole-scene delta, so a scene set needs a true reference field. Wrap an array
only where the schema says `array: true`, even a lone asset as `["asset_..."]`: a bare string is
silently dropped and the run succeeds ignoring it. State each reference's role in the prompt.
Approved on-model shots fill the remaining slots: curate them as a collection (catalog tools, write
lane) and retrieve them with `search` `filters={"collection_ids": [...]}`. Some style-reference
members mint a reusable style on the first run: the id sits in the finished job's output metadata
(`job_get` with `verbose=true`), not in the run response or on the asset, and later runs pass it in
the schema's id field in place of the images. The two fields are exclusive and only the image field
is flagged `cost_impact`, so the mint is paid once: read the field text before passing the same
reference set on every run. The first run must still carry the images even where the schema marks
both fields optional: the member refuses a run with neither.

The anchor can be an upload: the user's character art, sketch, or product photo goes up with
`upload_asset` plus `upload_asset_complete` (see `scenario`) and rides the reference field like any
approved hero. `asset_describe` (see `scenario-asset-analysis`) returns a style synthesis (medium,
palette, lighting, composition, mood) that seeds the palette and lighting lines of the enumeration;
the sub-element inventory comes from looking, or from `asset_analyze` with the enumeration headings
as its instruction (both are catalog tools; `asset_describe` prices with `dry_run`).

A set takes one `model_run` per item: a batch-count field repeats one prompt and cannot carry a
per-item delta clause. Launch in waves within the team's concurrency ceiling (the
`parallel-custom-jobs` row in the `scenario` skill): a launch refused with that 429 created no job,
so relaunch it in the next wave instead of dropping it.

## Locking a style without a subject

A style reference is a different slot from a subject reference, and a few image models expose it: at
authoring time one family took one to ten images as `styleReferenceImages`, built a private style
from them for a small extra charge, and returned a reusable style id in the job output that later
runs pass as `styleId` instead of the images (the two are mutually exclusive), with `styleMatch`
choosing `precise` or `flexible` adherence. It holds rendering, palette, and line, not a character:
pair it with a subject reference or the baseline enumeration for identity. Find such members with
`recommend` and the style need in the user's own words, then confirm the field on
`model_schema_get`. Palette fields taking RGB triples (`colors`) are preferences, not constraints:
keep the hex values in the prompt too.

## Locking structure with a control map

Generate the map and pass it as a conditioning input. Never extract it for the attribute that is the
delta: a pose map from the approved image locks the pose you were asked to change; a pose set needs
its map from a target-pose image, or none.

`asset_detect` takes an `asset_id` and a `modality` from `canny`, `depth`, `grayscale`,
`lineart_anime`, `mlsd`, `normal`, `pose`, `scribble`, `segmentation`, `sketch` (`remove_background`
defaults true). Catalog-only and write lane, despite the docs page grouping it under Analysis: run
it via `scenario_tool_execute_write`.

The control block (`controlImage`, `controlModality`, `controlStrength`, `controlStart`,
`controlEnd`) exists only on models listing `controlnet` in `capabilities`: check before planning
around it; models without it take reference images. `controlModality` allows `canny`, `tile`,
`depth`, `blur`, `pose`, `gray` and `low-quality`: only canny, depth and pose map across,
`grayscale` becoming `gray`. `controlStrength` defaults to 0.7 with a recommended 0.3 to 0.8 band:
near 0.7 for canny, depth and tile, 0.8 to 0.9 for pose, gray and blur, rigid above 0.9. Strength is
how much, `controlStart` and `controlEnd` are when: `controlEnd` near 0.65 locks composition early,
then releases so the prompt refines detail.

## Carrying identity into video

Reference-to-video members animate the subject the references show, with no first frame locking
composition. The catalog has no reference-to-video capability value: these members are tagged
`img2video`, so `recommend` with `capability="img2video"` and the consistency need in the user's own
words ("the same character as in these images"), then confirm on `model_schema_get` that the pick
carries a multi-slot `referenceImages` array (7 to 9 slots on the leading members at authoring time,
one of them billing slots past the first four) and, to copy a motion, a `referenceVideo` or
`referenceVideos` slot (capped between 3 and 15 seconds, billed per second of upload on some). When
nothing ranked carries the array, `search` a family name from the video model-family skills
`scenario-video` lists. Slot count moves the price less than resolution and duration: read both
enums off `model_schema_get`, where `cost_impact` marks them, and prove identity on the cheapest
roll that still shows the whole action, the lowest resolution at the delivery duration (a shorter
clip hides late drift), pricing that payload and the delivery payload with `dry_run` first (one
proving roll priced 80 CU against 130 at the defaults). A passing proving roll validates the recipe,
not the roll: the delivery render gets its own sweep before it ships. `image`, the first-frame
anchor, and the reference arrays are mutually exclusive on several families: a reference run opens
free, and pinning the opening frame is a different member. Fill the slots from the library: the hero
and two or three approved on-model shots per `scenario-identity-library`, as individual shots, never
the turnaround sheet. With no library yet, the approved hero alone in one slot is a valid first run
(the published pipeline starts from one strong reference), and the next slots fill from approved
stills out of the image reference loop above, once one reads as a clean character view (neutral
pose, no scene props: a scene still carries its props and pose into the slot), never from cropping
the sheet. Prompt the action, not the look: some schemas address uploads by order ("Image 1 turns
toward the camera, moving as in Video 1"), others state that the subjects appear with or without a
prompt, and on all of them re-describing the character fights the images. Per-family contracts and
prompt syntax: the video model-family skills `scenario-video` lists. Judge identity across the whole
clip: `asset_get` returns `firstFrame` and `lastFrame` as free asset ids, but a drift that fades in
mid-shot passes both, so sweep the frames with `model_scenario-video-to-image-seq` (a fixed
first-party id: Scenario's single deterministic frame extractor, so discovery would only re-derive
it) before the clip ships: about two frames a second (`frameInterval` is an integer count of source
frames between extractions, the clip's `frameRate` from `asset_get` halved and rounded, 12 at 24
fps; the clip goes in `video`), since a spot check of one frame per shot read clean at 10.5 seconds
on a clip whose defect appeared at 11.0. Judge each frame against the baseline enumeration by
sub-element: a named part gained, lost, recolored or reshaped is drift; a part that moves with the
action, a cloak swinging or an antenna tilting, is animation and passes.

## When references stop scaling

Reference-first is the published order and the cheaper one, so exhaust it before training. Training
pays back when one character recurs across hundreds of images, when four or more identities must
share scenes, or when every run needs the same rendering style whatever the subject. Train one model
per recurring character and a separate one for the style: a single model carrying both drifts on
whichever it saw less of. Multi-character scenes composite rather than co-generate, because
reference slots split attention: generate each identity alone against its baseline, then place them
together with the edit workflows in `scenario-image-editing`. Dataset size, base choice, and the
quote-before-launch rule: `scenario-model-training`. A trained model never runs by its own id
(`runs_as` and `run_with`, see `scenario`), and it still takes the baseline-plus-delta prompt:
training lightens the enumeration, not the delta discipline.

Sprite sheets and animation frames are the hard case: adjacent frames from a general image model
drift in proportion, scale, and silhouette, which an engine exposes at once. Frames of one character
come from the video lanes in `scenario-sprite-animation`, where one render holds the identity
instead of a generation per frame.

## Worked example: five poses of one mascot

1. `asset_display` the approved hero (`asset_hero`) and write its baseline: the full must-not-change
   enumeration above.
2. `recommend` with the task's own words as `prompt` (`search` only for a named family), preferring
   models with reference-image slots, then `model_schema_get`: the reference field's name, cap,
   cardinality, requiredness. No reference field in the schema disqualifies the candidate: go back
   to the ranked list, and when that holds nothing reference-capable either (`recommend` ranks
   community fine-tunes and can miss first-party models), take a family name from a sibling
   model-family skill (`scenario-gemini-image`, `scenario-seedream`) and `search` for it.
3. One `model_run` per pose, five in all, launched with `wait=false` in waves within the concurrency
   ceiling: the byte-identical baseline, the pose alone in the final clause, the hero in the
   reference field shaped as the schema says: `["asset_hero"]` only under `array: true`. No seed. No
   control map: a pose map from the hero locks the pose being changed.
4. `jobs_wait` on each wave, re-calling with `pending_job_ids`, and launch the next pose as a slot
   frees until all five are done. `asset_display` each against the hero; fix drift by tightening the
   enumeration, not by chaining outputs.

## Common mistakes

- Reaching for a seed to make two prompts match: it reproduces one generation and transfers nothing.
  Leave it unset across a set; set one only to re-roll a single unchanged prompt.
- Writing "same as before": there is no memory between calls; restate the baseline in full each
  time.
- Chaining a set output to output: drift compounds. Anchor every item to the same approved baseline.
- Training for a one-off: one character in one scene is a reference job, and a recurring cast at
  scale is the training case above, one model per character plus one for the style.
- Assuming `asset_detect` modality names are valid `controlModality` values.
- Passing the turnaround sheet into a reference slot, video or image: a video member animates the
  grid, and on an image model the sheet is a filing and review artifact, not a reference. Slots take
  individual approved shots.
- Re-describing the character in a reference-to-video prompt: the images carry the look; the prompt
  carries action, camera, and timing.