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wedding-immortalist婚礼神仙

Agent Skill

wedding-immortalist 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

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CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:wedding-immortalist(婚礼神仙)
来源仓库:https://github.com/curiositech/some_claude_skills
仓库路径:skills/wedding-immortalist
安装命令:
npx skills add https://github.com/curiositech/some_claude_skills --skill wedding-immortalist
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/curiositech/some_claude_skills --skill wedding-immortalist

简介

用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息。

  • 适合围绕仓库状态、代码变更或协作事项进行整理。
  • 使用时需要明确具体需求和目标,区分不同类型的设计任务。
  • 安装前建议确认权限范围和维护状态,避免触发联网或文件读写操作。
  • 注意该技能侧重视觉设计,不适合后端逻辑或数据库设计。

SKILL.md

Wedding Immortalist

Transform wedding photos and video into an eternal, immersive 3D experience. Create living memories that let couples and guests relive the magic forever.

When to Use This Skill

Use for:

  • Processing thousands of wedding photos into 3DGS scenes
  • Creating theatre-mode experiences where ceremony/reception moments play in-place
  • Building face-clustered guest rosters with best-photo selection
  • Matching design aesthetics to wedding themes (disco, rustic, beach, modern, queer celebrations)
  • AI-curated photo selection per guest with aesthetic scoring

NOT for:

  • General photo editing → use native-app-designer
  • Non-wedding 3DGS → use drone-inspection-specialist
  • Event planning → not a wedding planner
  • Video editing without 3D reconstruction

Core Pipeline

┌─────────────────────────────────────────────────────────────────┐
│                    WEDDING IMMORTALIST PIPELINE                  │
├─────────────────────────────────────────────────────────────────┤
│                                                                  │
│  1. INGEST                2. RECONSTRUCT        3. CLUSTER       │
│  ├─ Photos (1000s)        ├─ COLMAP SfM         ├─ Face detect   │
│  ├─ Video (hours)         ├─ 3DGS training      ├─ Embeddings    │
│  └─ Audio/speeches        └─ Scene merge        └─ Identity link │
│                                                                  │
│  4. CURATE                5. DESIGN             6. PRESENT       │
│  ├─ Aesthetic score       ├─ Theme extract      ├─ Web viewer    │
│  ├─ Per-person best       ├─ Color palette      ├─ Theatre mode  │
│  └─ Moment detect         └─ Typography         └─ Guest roster  │
│                                                                  │
└─────────────────────────────────────────────────────────────────┘

Theme-Adaptive Design

Theme Detection & Matching

Every wedding has a unique aesthetic. Extract and honor it:

Theme TypeColor PaletteTypographyUI Elements
70s DiscoGold, orange, burnt sienna, deep purpleGroovy script, bold sansMirror balls, starbursts, warm gradients
Rustic/BarnEarth tones, sage, cream, woodSerif, hand-letteredBurlap textures, wildflower accents
Beach/CoastalOcean blues, sand, coral, seafoamLight sans, scriptShell motifs, wave patterns
Modern MinimalBlack, white, metallicsClean geometric sansSharp lines, negative space
Queer JoyRainbow spectrums, bold colorsExpressive, variedPride elements, celebration maximalism
Cultural FusionPer traditionTraditional + modernCultural motifs, heritage patterns

Extracting Theme from Photos

# Theme extraction signals
THEME_SIGNALS = {
    'color_palette': 'Dominant colors from venue, florals, attire',
    'lighting_mood': 'Warm/cool, natural/dramatic, string lights/chandeliers',
    'decor_elements': 'Rustic/modern/vintage/eclectic',
    'attire_style': 'Traditional/non-traditional, formal/casual',
    'cultural_markers': 'Religious symbols, cultural traditions',
    'era_aesthetic': '70s disco, 20s gatsby, etc.'
}

3D Gaussian Splatting Pipeline

Photo/Video Ingestion

Optimal Input Strategy:
├── Video: Extract 2-3 fps (80% overlap minimum)
├── Photos: Include ALL photographer shots
├── Phone photos: Guest uploads (georeferenced bonus)
└── Coverage: Ceremony + reception + all spaces

Quality Thresholds:
├── Minimum images per space: 50-100
├── Overlap requirement: 60-80%
├── Blur rejection: Laplacian variance < 100 = skip
└── Exposure: Reject severe over/underexposure

COLMAP Structure from Motion

# Feature extraction
colmap feature_extractor \
  --database_path database.db \
  --image_path images/ \
  --ImageReader.single_camera 0 \
  --SiftExtraction.max_image_size 3200

# Exhaustive matching for comprehensive coverage
colmap exhaustive_matcher \
  --database_path database.db \
  --SiftMatching.guided_matching 1

# Sparse reconstruction
colmap mapper \
  --database_path database.db \
  --image_path images/ \
  --output_path sparse/

# Dense reconstruction (optional, for mesh)
colmap image_undistorter ...
colmap patch_match_stereo ...

3DGS Training

# Wedding-optimized 3DGS settings
WEDDING_3DGS_CONFIG = {
    'iterations': 50_000,          # High quality for permanent archive
    'densify_from_iter': 500,
    'densify_until_iter': 15_000,
    'densification_interval': 100,
    'opacity_reset_interval': 3000,
    'sh_degree': 3,                # Full spherical harmonics for lighting
    'percent_dense': 0.01,
    'densify_grad_threshold': 0.0002,
}

# Multi-space merge strategy
SPACES = ['ceremony', 'cocktail_hour', 'reception', 'photo_booth', 'dance_floor']
# Train each separately, then create unified navigation

Face Clustering System

Pipeline

┌────────────────────────────────────────────────────────┐
│               FACE CLUSTERING PIPELINE                  │
├────────────────────────────────────────────────────────┤
│  1. Detection (RetinaFace/MTCNN)                       │
│     └─ All faces in all photos                         │
│  2. Alignment (5-point landmark)                       │
│     └─ Standardize for embedding                       │
│  3. Embedding (ArcFace/AdaFace)                        │
│     └─ 512-dim identity vector per face                │
│  4. Clustering (HDBSCAN)                               │
│     └─ Group by identity, handle edge cases            │
│  5. Identity Linking                                   │
│     └─ Match to couple, wedding party, family, guests  │
│  6. Best Photo Selection                               │
│     └─ Aesthetic scoring per cluster                   │
└────────────────────────────────────────────────────────┘

Clustering Parameters

CLUSTERING_CONFIG = {
    'min_cluster_size': 3,         # At least 3 photos to form identity
    'min_samples': 2,
    'metric': 'cosine',
    'cluster_selection_epsilon': 0.3,
    'cluster_selection_method': 'eom',
}

# Identity priority for naming
IDENTITY_PRIORITY = [
    'couple_1', 'couple_2',        # The married couple
    'wedding_party',               # Bridesmaids, groomspeople
    'parents',                     # Parents of the couple
    'grandparents',
    'siblings',
    'extended_family',
    'friends',
    'vendors',                     # Photographer, DJ, etc.
]

Identity Linking Workflow

  1. Couple identification: User tags couple in 2-3 photos
  2. Wedding party: User identifies key people
  3. Auto-propagation: Embeddings match across all photos
  4. Guest matching: Optional guest list import for name assignment
  5. Manual corrections: UI for fixing mismatches

Aesthetic Scoring

Per-Photo Quality Metrics

AESTHETIC_FEATURES = {
    # Technical quality
    'sharpness': 'Laplacian variance, MTF analysis',
    'exposure': 'Histogram analysis, dynamic range',
    'noise': 'High-ISO detection, grain analysis',

    # Composition
    'rule_of_thirds': 'Subject placement scoring',
    'symmetry': 'For venue/group shots',
    'framing': 'Negative space, balance',

    # Face-specific
    'expression': 'Smile detection, eye openness',
    'blink_detection': 'Eyes closed penalty',
    'gaze_direction': 'Looking at camera vs. candid',
    'face_occlusion': 'Nothing blocking the face',
    'face_lighting': 'Even illumination, no harsh shadows',

    # Emotional
    'genuine_smile': 'Duchenne marker detection',
    'moment_quality': 'Laughter, tears, embraces',
}

Best Photo Selection Per Person

def select_best_photos(cluster_photos, n=5):
    """Select top N photos for a person across all their appearances."""

    scores = []
    for photo in cluster_photos:
        score = (
            0.25 * technical_quality(photo) +
            0.25 * composition_score(photo) +
            0.30 * expression_quality(photo) +
            0.20 * context_diversity(photo, scores)  # Avoid all similar shots
        )
        scores.append((photo, score))

    # Select top N with diversity constraint
    return diverse_top_n(scores, n, diversity_threshold=0.7)

Theatre Mode

Moment Detection & Playback

KEY MOMENTS (auto-detected + user-tagged):
├── Ceremony
│   ├── Processional
│   ├── Vows exchange
│   ├── Ring ceremony
│   ├── First kiss
│   └── Recessional
├── Reception
│   ├── Grand entrance
│   ├── First dance
│   ├── Parent dances
│   ├── Toasts/speeches
│   ├── Cake cutting
│   └── Bouquet/garter
├── Party
│   ├── Dance floor highlights
│   └── Exit/sendoff
└── Candids
    ├── Emotional moments (tears, laughter)
    └── Spontaneous joy

In-Scene Video Projection

Theatre Mode Rendering:
1. User navigates 3DGS scene freely
2. Approaches "moment marker" (glowing orb/frame)
3. Video/slideshow plays IN the 3D space
   ├── On walls where projector was
   ├── Floating frames in dance floor area
   └── Photo booth backdrop location
4. Spatial audio for speeches/music
5. User can pause, scrub, exit to continue exploring

Web Viewer Architecture

// Wedding Immortalist Viewer Components
const VIEWER_FEATURES = {
  // 3DGS Navigation
  gaussianSplatting: {
    renderer: 'three-gaussian-splat',
    navigation: 'orbit + first-person',
    qualityLevels: ['preview', 'standard', 'maximum'],
  },

  // Theatre Mode
  theatreMode: {
    momentMarkers: true,
    videoInScene: true,
    spatialAudio: true,
    transitionEffects: 'theme-matched',
  },

  // Guest Roster
  guestRoster: {
    faceGrid: 'clustered by identity',
    photoGallery: 'per-person best shots',
    searchByName: true,
    shareableLinks: 'per-guest galleries',
  },

  // Theme
  theming: {
    colorPalette: 'extracted from wedding',
    typography: 'theme-matched',
    uiElements: 'aesthetic-consistent',
  },
};

Anti-Patterns

"All Frames, All the Time"

Wrong: Extracting every video frame for 3DGS. Why: Redundant data, 10x slower processing, no quality improvement. Right: 2-3 fps extraction with motion-based keyframe selection.

"One Giant Scene"

Wrong: Training single 3DGS for entire venue. Why: Memory explosion, quality degradation, impossible on consumer hardware. Right: Train per-space, create unified navigation with seamless transitions.

"Default Clustering Threshold"

Wrong: Using default HDBSCAN settings. Why: Wedding photos have varying lighting, makeup, angles—need tuning. Right: Tune per-wedding based on photo count and quality variance.

"Ignoring Theme"

Wrong: Generic white/gray viewer UI for disco wedding. Why: Destroys the personality and joy of the event. Right: Extract and honor the couple's aesthetic choices.

"Photographer Only"

Wrong: Using only professional photos. Why: Misses candid moments, guest perspectives, coverage gaps. Right: Merge professional + guest photos for complete coverage.

Guest Experience Features

Shareable Guest Galleries

Per-Guest Experience:
├── Personalized link: yourwedding.com/guests/aunt-martha
├── Their best photos (AI-curated)
├── Photos with the couple
├── Group photos they appear in
├── Download options (full-res)
└── "Add to my memories" for their own archives

Collaborative Enhancement

Guest Contribution Portal:
├── Upload their own photos
├── Tag themselves in unidentified clusters
├── Correct misidentifications
├── Add names to unknown guests
└── Submit video moments they captured

Output Deliverables

wedding-immortalist-output/
├── 3dgs-scenes/
│   ├── ceremony/
│   ├── cocktail/
│   ├── reception/
│   └── unified-navigation.json
├── guest-roster/
│   ├── face-clusters/
│   ├── identity-mapping.json
│   └── per-person-galleries/
├── theatre-mode/
│   ├── moment-markers.json
│   ├── video-segments/
│   └── spatial-audio/
├── web-viewer/
│   ├── index.html
│   ├── theme-config.json
│   └── assets/
└── exports/
    ├── full-resolution-photos/
    ├── guest-gallery-zips/
    └── video-compilations/

Integration Points

  • drone-inspection-specialist: 3DGS techniques, COLMAP pipeline
  • collage-layout-expert: Photo arrangement, aesthetic composition
  • color-theory-palette-harmony-expert: Theme color extraction
  • clip-aware-embeddings: Photo-text matching for search
  • photo-composition-critic: Aesthetic quality scoring

Core Philosophy: A wedding happens once. The memories should live forever. This skill transforms ephemeral moments into an eternal, explorable experience that honors the couple's unique celebration—whether it's a disco dance party, a rustic barn gathering, or two grooms celebrating their love with chosen family.

适合场景

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平台分布

Codex

35.08%
按下载量换算46

Claude

30.57%
按下载量换算40

Cursor

21.38%
按下载量换算28

Gemini CLI

10.73%
按下载量换算14

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执行命令

安装流程涉及命令执行,可能通过 npx skills add https://github.com/curiositech/some_claude_skills --skill wedding-immortalist 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

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