Skill: ggke/story-to-prompts (version 1.2.0)

--- name: story-to-prompts description: "Convert story synopses or single-scene descriptions into high-quality text-to-image prompts. Two modes: (1) multi-scene - a story outline is split into multiple coherent scenes, each with its own prompt; (2) single-scene - a single scene description gets a prompt directly. Outputs scored prompts with bilingual versions. Use when users say story to images, generate prompts for this scene, story split, storyboard prompts, text-to-image prompt, 文生图, 分镜, 故事拆分." --- # Story to Prompts One-shot conversion from story/scene to text-to-image prompts. No interactive confirmation — output the final result directly. ## Output Language Detect language from user input: - Chinese input → primary prompt in Chinese, secondary in English - English input → primary prompt in English, secondary in Chinese - Explicit language override (e.g. "output in English", "用中文输出") → follow user instruction - All structural text (titles, character sheets, scene descriptions) matches the primary language ## Entry Point Determine mode based on user input: - **Multi-scene mode**: Input contains multiple events/plot points, or user explicitly requests N images - **Single-scene mode**: Input describes only one scene/画面, or user asks for a prompt for "one scene" ## Split Strategy (Multi-scene Mode) Priority for determining image count and split: 1. **User specifies count** (e.g. "4 images", "拆成6张") → use directly 2. **User does not specify** → split by spatiotemporal boundaries: - Identify distinct time-space units (location change, time jump) - Each independent time-space = one image - Within the same time-space, if multiple key actions exist, split into 2-3 images with different shot types 3. **Default range**: 3-6 images unless the story is extremely simple or very long ## Workflow (Multi-scene Mode) Complete all steps in one pass. Output final result only. ### Step 1: Extract Story Baseline Determine internally (do not output separately): - Story core (one sentence) - Character fixed features (age, hair, clothing, signature accessories) - Unified visual style - Color palette - Lighting style ### Step 2: Structure Split Determine N images, assign for each: - Shot type (refer to `references/shot-types.md` narrative rhythm template, adjacent images must differ) - Camera angle - Narrative function (establishing / progression / climax / resolution) ### Step 3: Generate Prompt per Image Requirements for each prompt: - **Repeat character fixed features** in every prompt (consistency) - **Vary** viewpoint, composition, posture across images (diversity) - Only include characters/objects mentioned in the current scene (appearance rule) - Include negative prompt (anti-failure) - Follow the writing spec below ### Step 4: Score and Optimize Self-evaluate each prompt on 10 dimensions and optimize: **Structure Completeness (40 pts)** 1. Core intent clarity (10): Is the goal unambiguous? 2. Subject and hierarchy (10): Is the main subject clear with size ratio? 3. Composition and ratio constraints (10): Aspect ratio, viewpoint, composition technique? 4. Style anchor clarity (10): Specific style/medium specified? **Generation Quality Control (40 pts)** 5. Motif unity (10): Do visual details serve a unified theme? 6. Material and lighting description (10): Specific material and light logic? 7. Constraints and negative prompts (10): Anti-failure constraints present? 8. Text-image integration (10): Text layout handled or explicitly absent? **Productization and Reusability (20 pts)** 9. Parameterization (10): Easy to adjust and reuse? 10. Failure anticipation (10): Common AI errors preemptively blocked? Logic check per prompt: character consistency, scene continuity, physics plausibility, style coherence. Fix contradictions if found. **Target: each prompt ≥ 80 points (High Quality).** If below, self-optimize and output the improved version. ## Workflow (Single-scene Mode) Simpler, one pass: 1. Extract character features and visual style from the scene 2. Determine optimal shot type and composition 3. Generate prompt (same requirements as Step 3-4 above) 4. Output ## Output Format Primary language marked ★, secondary marked ☆: ``` ### Image N | [Shot Type] | [Narrative Function] **Scene Description:** [Detailed description in primary language] **Text-to-Image Prompt ★ ([Primary Language]):** [Complete detailed prompt, ready to copy-paste] **Text-to-Image Prompt ☆ ([Secondary Language]):** [Complete prompt adapted to target language conventions] **Negative Prompt:** [negative keywords] Score: [X]/100 | Level: [Product-grade / High Quality / Usable] Strengths: [One sentence] Improvements: [If applicable, one sentence] ``` ## Prompt Writing Spec **Structure** (by priority): ``` [Style] + [Shot type + Composition + Camera angle] + [Subject + fixed features] + [Action/Expression] + [Environment/Background] + [Lighting/Atmosphere] + [Material/Texture] + [Quality tags] + [Negative prompt] ``` **Bilingual output rules:** - Primary language prompt: complete and detailed, ready to copy-paste - Secondary language prompt: equally complete, adapted to target language prompt conventions (not a literal translation) **Consistency rules:** - Character fixed features (age, hair, clothing) must be explicitly repeated in every prompt - Style, color palette, lighting baseline must carry through all images - Key props appearance must remain consistent **Diversity rules:** - Adjacent images use different shot types - Encourage different composition techniques - Character posture, expression, position may vary - Lighting intensity may be adjusted, style remains constant ## Reference Files Read on demand: - `references/shot-types.md` — Shot types, camera angles, narrative rhythm templates - `references/composition-patterns.md` — 12 composition patterns with prompt fragments - `references/style-params.md` — 30+ style parameters (keywords, quality tags, avoid list, lighting)


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