product-shots-multi-angle

사용 방법

설치 명령을 복사하고 자신의 에이전트 환경에 설치하기 전에 소스 패키지를 확인하세요.
필요한 도구, 인증 정보, 지원 환경은 원본 문서에서 확인하세요.
입력과 원하는 산출물을 설명하고 출처와 결과를 대조하세요.

npx skills add https://github.com/motiful/product-shots --skill product-shots-multi-angle

시작 전 확인

설치에는 모델 구독, API 한도, 파일 접근 권한이 포함되지 않습니다.
별점 수와 설치 수는 활동 지표이며 독립적인 품질·보안 검증이 아닙니다.

원본 Skill 문서

원본 저장소의 제목, 예시, 코드, 표, 링크, 이미지를 유지해 표시합니다.

Multi-Angle

Persona — You are a fashion editorial director specializing in multi-image model campaigns.

Produces a 9-image fashion-editorial series (the "Model Consistency Series") from a single user-uploaded reference photo. The skill extracts 14 controllable variables from the reference, presents 3 photography-style presets (Retro Analog Flash / Soft Muted Film / Hard Flash Editorial), then renders 9 task-prompt templates (one per image) with strict crop, pose, hairstyle, and style continuity rules so all 9 frames read as a single shoot.

This skill is part of the product-shots ecosystem — designed for cross-border e-commerce apparel, footwear, and accessory listings that need a coherent multi-angle lookbook from a single reference shot.

Engagement Principles

These rules apply across every Section. Read before acting.

Reference image is mandatory — every image-generation call MUST pass REFERENCE_IMAGE as image input. Pure text descriptions are not allowed; identity consistency cannot be guaranteed without it.
Analyse before generate — extract all 14 variables from the reference image before filling any prompt. Never guess defaults, never skip extraction.
Hairstyle structure is non-negotiable — every prompt MUST include {HAIRSTYLE} intact, NO loose hair, NO reinterpretation. A tied / pinned / braided hairstyle in the reference must remain so across all 9 angles.
Crop boundaries are hard constraints — "framed to mid-thigh" means knees/lower legs/feet are forbidden in frame; "framed to chest" forbids the abdomen; "framed to hip line" forbids thighs. Treat each frame's crop as a verifiable rule, not a hint.
Style is global — the same {PHOTOGRAPHY_STYLE} block is repeated verbatim in every one of the 9 prompts. No image may look cleaner / more digital / higher-contrast than the others.
Accessories follow the reference — if the reference has accessories AND the crop reveals them → keep them; if the reference has none → never add them; if the crop excludes them → annotate with where possible or No accessories — frame doesn't reach them.
Pause for style selection — if the user has not specified a style and has not uploaded a style reference image, present the 3 presets via <suggestion> chips (do not auto-pick a default).
Batch generate by default — produce all 9 images in a single batch unless the user explicitly asks for stepwise review (avoids inter-call model drift).
Match the user's language — respond in the language the user writes in. Never switch unprompted.

Execution Procedure

generate_multi_angle_series(user_request) → 9_images

# Step 0 — Pin hard constraints (MUST, before any decision)
load references/hard-constraints.md
    → Reference Image / Analyse-Before-Generate / Hairstyle Intact /
      Accessory Fidelity / Crop Boundaries / Style Unity / Override / Batch
keep these in working context for Steps 1-4 — violations break identity / hairstyle /
crop integrity which the validation views (Image 4 back, Image 8 side) cannot recover.

# Step 1 — Reference image gate + constraint pre-check
if user did NOT upload REFERENCE_IMAGE:
    abort with: "This skill requires a reference image to guarantee identity consistency.
                 Please upload a photo and retry."
    # NEVER fall back to text-only description.
# Pre-check RULE_001 + RULE_002 setup before extraction proceeds (extracted_vars
# + prompts + outputs are empty at this stage — call gates the workflow entry).
enforce_constraints(extracted_vars={}, prompts=[], outputs=[])
    → see references/hard-constraints.md §Execution Procedure (RULE_001 reference-image
      presence; later re-invoked at Step 5 with full payload).

# Step 2 — Extract 14 variables from reference (Vision pass)
extracted_vars = extract_variables(reference_image=REFERENCE_IMAGE)
    → see references/variables-and-workflow.md §Variable Extraction Specifications
    REQUIRED   = REFERENCE_IMAGE, HAIR_COLOR, HAIRSTYLE, SKIN_TONE, EYE_COLOR,
                 FACE_SHAPE, OUTFIT, BACKGROUND_COLOR, PHOTOGRAPHY_STYLE, ASPECT_RATIO
    OPTIONAL   = HAIR_ACCESSORIES, BAG, JEWELRY, OTHER_ACCESSORIES (default "none")
    if any required field cannot be extracted with confidence → ask the user to clarify
    (do NOT silently default).

# Step 3 — Photography style selection
# Inference sources (per variables-and-workflow.md §Style detection):
#   has_explicit_style_specification(user_request) → True if user_request
#     contains any keyword in STYLE_KEYWORDS_LIST (e.g., "retro", "flash",
#     "muted", "editorial", "soft", "analog")
#   has_style_reference_image(context) → True if context.attached_images
#     contains an image flagged role="style_reference" by the caller
selected_style = select_or_emit_presets(reference_image=REFERENCE_IMAGE,
                                        has_style_kw=has_explicit_style_specification(user_request))
    → see references/photography-style-presets.md §Execution Procedure
    # Returns chosen_style block verbatim OR pauses (emits 3 preset images +
    # 5 <suggestion> chips) and waits for user click. Never auto-picks a default.

# Step 4 — Fill 9 task-prompt templates (single batch)
image_ids = [1, 2, 3, 4, 5, 6, 7, 8, 9]
prompts = fill_task_prompts(extracted_vars=extracted_vars,
                            selected_style=selected_style,
                            image_ids=image_ids)
    → see references/task-prompts.md §Execution Procedure
      + references/task-prompts-6-9.md (images 6-9)
    # Each prompt repeats the full {PHOTOGRAPHY_STYLE} block verbatim.
    # Each prompt re-asserts {HAIRSTYLE} intact + NO loose hair where applicable.

images = Skill("product-shots-image-gen",
               f"batch_generate: {len(prompts)} prompts | "
               f"reference_image={REFERENCE_IMAGE} | "
               f"model=gemini-3-pro-image-preview")
# Do NOT substitute with direct API call. product-shots-image-gen owns
# API-key resolution + reference-image preprocessing.
assert images.delivered and len(images) == 9

# Step 5 — Self-check gate (re-validate against hard-constraints)
enforce_constraints(extracted_vars=extracted_vars, prompts=prompts, outputs=images)
    → see references/hard-constraints.md §Execution Procedure (full 8-rule sweep)
critical checks (subset of RULE_003 / RULE_005 / RULE_006):
    - Image 4 (back view) — hairstyle structure visible from behind, no loose hair
    - Image 8 (side profile) — hairstyle structure visible from side, no loose hair
    - Image 5 (extreme close-up) — only eyes/nose/lips visible, no forehead/chin/shoulders
    - All 9 — same {PHOTOGRAPHY_STYLE} signature (lighting / shadow direction / grain)
if any check fails → regenerate the affected image(s)

# Step 6 — User overrides (re-render selectively)
on user override of any extracted variable:
    extracted_vars = apply_user_overrides(extracted_vars, user_overrides)
        → see references/variables-and-workflow.md §Variable Override Logic
        # Internally calls mark_affected_images_for_regeneration(variable_key):
        HAIRSTYLE / HAIR_COLOR / HAIR_ACCESSORIES → re-render images 1-9
        OUTFIT                                     → re-render 1, 2, 3, 4, 6, 7, 8, 9 (skip 5)
        BAG / JEWELRY                              → re-render 1, 2, 3, 6, 9 (in-frame ones)
        PHOTOGRAPHY_STYLE                          → re-render images 1-9

TOC of Module Files

references/hard-constraints.md — The 8 Rules (RULE_001-008) covering reference image, analysis-first, hairstyle intact, accessory fidelity, crop boundaries, style unity, override handling, batch generation. Loaded at EP Step 0, re-validated at EP Step 5.
references/variables-and-workflow.md — Section 1 (14 input variables + extraction specs for HAIRSTYLE / OUTFIT / SKIN_TONE) + Section 3 (Workflow) + variable-override re-render logic.
references/photography-style-presets.md — Section 2: the 3 presets (Retro Analog Flash / Soft Muted Film / Hard Flash Editorial) with verbatim lighting / shadow / film / colour / material specs, plus the style-selection output format (3 preset images + 5 <suggestion> chips).
references/task-prompts.md — Section 4.1-4.5: Image 1 Three-Quarter Fashion Portrait through Image 5 Extreme Facial Close-Up. Each prompt template uses {VARIABLE} placeholders.
references/task-prompts-6-9.md — Section 4.6-4.9: Image 6 Over-Right-Shoulder Glance through Image 9 Opposing Torso Twist. Split from task-prompts.md to keep both files under the 300-line cap.

Section Index

1. Variables                                            → references/variables-and-workflow.md §Variables
   14 variables: REFERENCE_IMAGE, HAIR_COLOR, HAIRSTYLE, HAIR_ACCESSORIES,
   SKIN_TONE, EYE_COLOR, FACE_SHAPE, OUTFIT, BAG, JEWELRY, OTHER_ACCESSORIES,
   BACKGROUND_COLOR, PHOTOGRAPHY_STYLE, ASPECT_RATIO
2. Photography Style Presets                            → references/photography-style-presets.md
   2.1 Preset A — Retro Analog Flash
   2.2 Preset B — Soft Muted Film
   2.3 Preset C — Hard Flash Editorial
3. Workflow                                             → references/variables-and-workflow.md §Workflow
4. Task Prompts                                         → references/task-prompts.md (images 1-5)
                                                         + references/task-prompts-6-9.md (images 6-9)
   4.1 Image 1 — Three-Quarter Fashion Portrait              → task-prompts.md
   4.2 Image 2 — High-Angle Bird's-Eye View                  → task-prompts.md
   4.3 Image 3 — Over-the-Shoulder Close-Up                  → task-prompts.md
   4.4 Image 4 — Back View with Hairstyle Visible            → task-prompts.md
   4.5 Image 5 — Extreme Facial Close-Up                     → task-prompts.md
   4.6 Image 6 — Over-Right-Shoulder Glance                  → task-prompts-6-9.md
   4.7 Image 7 — Low-Angle Upward Gaze, Contrapposto         → task-prompts-6-9.md
   4.8 Image 8 — Side Profile, Chest Crop                    → task-prompts-6-9.md
   4.9 Image 9 — Medium Portrait, Opposing Torso Twist       → task-prompts-6-9.md
5. Rules                                                → references/hard-constraints.md
   8 rules: Identity / Workflow / Hair / Accessories / Crop / Style / Override / Batch

Cross-Skill Notes

This skill is invoked only when the user explicitly requests multi-angle / 9-angle / model-consistency portraits, typically for apparel, footwear, or accessory listings. Routed from product-shots when asset_type ∈ {multi-angle, lookbook, model-series}.
REFERENCE_IMAGE-anchored identity locking is a pattern shared conceptually with product-shots-main-image and product-shots-detail-page (which anchor on the main product image instead of a model reference), but the three skills do not call each other.
Photography-style preset images (3 hard-coded CDN URLs) are owned by this skill.
Image generation is delegated to product-shots-image-gen (the product-shots image-gen engine) — this skill produces prompts and reference_image inputs; product-shots-image-gen calls the actual API.

Tooling

The skill emits prompts + reference image binding. Actual image generation is invoked through product-shots-image-gen (the product-shots image-gen engine), or by any image-to-image–capable tool the host platform exposes. Vision-based variable extraction (Step 2) is invoked by the parent agent (Planner) using the rules and prompt templates produced here. The 9-image batch is rendered by passing REFERENCE_IMAGE as the reference input to the image-generation model and the filled task templates as text prompts.

사용 방법

시작 전 확인

Multi-Angle

Engagement Principles

Execution Procedure

TOC of Module Files

Section Index

Cross-Skill Notes

Tooling

관련 작업

더 많은 Skills

product-shots-ad-creative

product-shots-multi-angle

사용 방법

시작 전 확인

Multi-Angle

Engagement Principles

Execution Procedure

TOC of Module Files

Section Index

Cross-Skill Notes

Tooling

관련 작업

taste-distillation

design-system

taste-application

fal-ai-media

더 많은 Skills

product-shots-ad-creative