Skill: phy041/content-type-router (version 1.0.2)

--- name: content-type-router description: Detect and route to the correct visual content type (hero shot, poster, infographic, etc.) from a text description or output type hint. Use when you need to classify a content generation request into a structured type with layout rules, visual mode, and generation hints. homepage: https://canlah.ai metadata: {"category": "content-routing", "tags": ["content-type", "routing", "image-generation", "classification", "layout"], "runtime": "python"} --- # Content Type Router Classify a content generation request into a structured content type configuration that provides layout rules, visual mode constraints, typography rules, and generation hints for downstream image generation. ## Overview The router maps natural language descriptions (and optional industry/type hints) to one of 17 predefined content type configurations across 4 categories: | Category | Slugs | |----------|-------| | `product_focused` | `hero_shot`, `lifestyle_shot`, `flat_lay`, `detail_closeup`, `scale_shot`, `unboxing` | | `typography_heavy` | `poster`, `sale_promo`, `countdown`, `quote_graphic`, `announcement` | | `educational` | `infographic`, `comparison_chart`, `process_howto`, `carousel_educational` | | `social_proof` | `testimonial_graphic`, `before_after`, `ugc_repost` | ## Visual Modes Every content type has one of three visual modes that determine text handling: | Mode | Slug | Behavior | |------|------|----------| | Pure Visual | `pure_visual` | NO text on image — pure photography, composition, and lighting | | Educational | `educational` | Minimal text allowed (data labels, steps) — up to ~35% of area | | CTA | `cta` | Text is required — headlines, body, and call-to-action | ## Content Type Reference ### Product-Focused (`pure_visual`) **`hero_shot`** — Single product on clean background, hero positioning - Focal point: center | Coverage: 60-80% | Background: white/gradient - Hints: studio lighting, no props, Amazon/Shopify listing standard **`lifestyle_shot`** — Product in real-world context showing aspiration - Focal point: rule-of-thirds | Coverage: 30-50% | Background: contextual - Hints: natural lighting, authentic mood, story-driven **`flat_lay`** — Top-down arrangement of multiple items - Focal point: center | Coverage: 65-75% | Camera: overhead (90°) - Hints: 3-12 items, cohesive palette, intentional negative space **`detail_closeup`** — Macro shot for texture and craftsmanship - Focal point: center | Coverage: 80-95% | Camera: macro - Hints: shallow depth of field, premium material visibility **`scale_shot`** — Product with size reference object - Focal point: center | Coverage: 40-70% | Reference: hand or common object **`unboxing`** — Product with packaging, reveal aesthetic - Focal point: center | Coverage: 50-80% | Mood: anticipation/premium ### Typography-Heavy (`cta`) **`poster`** — Designed travel/event poster with headline and imagery - Text zone: overlay | Fonts: headline + body | Max 7-word headline **`sale_promo`** — Promotional graphic with discount/offer messaging - Text zone: prominent | Single CTA required | Max 35% text area **`countdown`** — Time-limited offer with countdown element - Text zone: center | Urgency language | Date/time element required **`quote_graphic`** — Brand quote or testimonial as designed asset - Text zone: center | Single quote | Minimal supporting context **`announcement`** — Product launch or news announcement graphic - Text zone: structured | Hierarchy: news > detail > CTA ### Educational (`educational`) **`infographic`** — Data visualization with labeled information - Text: labels and data points | Max ~35% text area | Clear visual hierarchy **`comparison_chart`** — Side-by-side comparison of options - Layout: split | Labels required | Clear differentiation **`process_howto`** — Step-by-step visual guide - Layout: sequential | Step numbers | 3-7 steps recommended **`carousel_educational`** — Multi-frame educational series (single frame rules) - Consistent style across frames | Progressive information reveal ### Social Proof (`cta` or `educational`) **`testimonial_graphic`** — Customer quote with attribution - Quote text required | Customer name/handle | Optional product image **`before_after`** — Side-by-side transformation visual - Split composition | Clear before/after labeling | High contrast difference **`ugc_repost`** — User-generated content styled for brand repost - Authentic feel | Minimal brand overlay | Source attribution ## Detection API ### Python Usage ```python from content_types.registry import ( detect_content_type, get_content_type_config, get_industry_content_types, get_content_types_by_visual_mode, get_content_types_by_category, get_all_content_types, ) from content_types.base import VisualMode # Detect from description slug, confidence = detect_content_type( description="I need a product shot on white background for Amazon", industry="ecommerce", ) # → ("hero_shot", 0.95) # Get full config with layout rules and generation hints config = get_content_type_config(slug) prompt_context = config.to_prompt_context() # Inject into LLM prompt negative_prompts = config.get_negative_prompt_string() # For image gen # Industry-based recommendations types = get_industry_content_types("beauty") # → [HeroShot, BeforeAfter, FlatLay] # Filter by visual mode pure_visual_types = get_content_types_by_visual_mode(VisualMode.PURE_VISUAL) # Filter by category product_types = get_content_types_by_category("product_focused") ``` ### Detection Logic 1. If `output_type` exactly matches a slug → confidence 1.0 2. Keyword scoring across all registered types 3. Normalized score with multi-match bonus 4. If confidence < 0.3 → fall back to industry default 5. Ultimate fallback: `lifestyle_shot` ### Detection Return ```python (slug: str, confidence: float) # confidence: 0.0-1.0 # 0.3+ = usable, 0.7+ = high confidence ``` ## `ContentTypeConfig` Structure ```python @dataclass class ContentTypeConfig: name: str # Human-readable name slug: str # Machine key: "hero_shot" category: str # "product_focused" | "typography_heavy" | etc. definition: str # One-line definition for LLM context visual_mode: VisualMode # PURE_VISUAL | EDUCATIONAL | CTA layout: LayoutRules # Focal point, coverage, background, camera angle typography: Optional[TypographyRules] # Headline words, weight, CTA rules generation_hints: List[str] # Positive requirements for image gen prompt negative_prompts: List[str] # What to avoid (comma-joined for diffusion) detection_keywords: List[str] # Keyword matching corpus common_industries: List[str] # Industry affinity aspect_ratios: List[str] # Recommended ratios: ["4:5", "1:1"] ``` ## `LayoutRules` Structure ```python @dataclass class LayoutRules: focal_point: str # "center" | "upper_third" | "rule_of_thirds" | "left" | "right" text_zone: Optional[str] # "bottom_40_percent" | "top_20_percent" | "overlay" | None subject_coverage_min: float # Min % of frame for main subject subject_coverage_max: float # Max % of frame for main subject text_area_max: float # Max % of frame for text (0.0 = no text) logo_zone: str # "corner" | "bottom_center" | "none" whitespace_min: float # Minimum negative space target camera_angle: Optional[str] # "eye_level" | "overhead" | "low_angle" | "macro" background: Optional[str] # "white" | "gradient" | "contextual" | "lifestyle" ``` ## Industry Defaults | Industry | Top 3 Content Types | |----------|---------------------| | travel | poster, lifestyle_shot, carousel_educational | | dtc | hero_shot, lifestyle_shot, ugc_repost | | fashion | lifestyle_shot, flat_lay, ugc_repost | | beauty | hero_shot, before_after, flat_lay | | food | flat_lay, lifestyle_shot, process_howto | | saas | infographic, comparison_chart, testimonial_graphic | | luxury | hero_shot, detail_closeup, lifestyle_shot | | health | before_after, testimonial_graphic, infographic | ## Database Mode The registry supports optional DB-backed content types (for per-brand customization): ```bash export USE_DB_CONTENT_TYPES=true # Enable DB mode ``` When enabled, types load from a `content_types` Supabase table with brand-specific overrides. Falls back to code-defined types on DB errors. ```python # Invalidate cache after DB updates from content_types.registry import invalidate_cache invalidate_cache() # Enable/disable at runtime from content_types.registry import enable_db_mode enable_db_mode(True) ``` ## Integration Pattern Inject the config into image generation prompts: ```python slug, confidence = detect_content_type(description, industry=industry) config = get_content_type_config(slug) # Build generation prompt system_context = config.to_prompt_context() negative = config.get_negative_prompt_string() # Pass to image generation generate_image( prompt=f"{system_context}\n\n{user_prompt}", negative_prompt=negative, aspect_ratio=config.aspect_ratios[0], ) ``` --- ## Author **[Canlah AI](https://canlah.ai)** — Run performance marketing without breaking your brand. - GitHub: [github.com/PHY041](https://github.com/PHY041) - All Skills: [clawhub.ai/PHY041](https://clawhub.ai/PHY041)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)