A Claude Code skill that generates structured JSON prompts across 7 domains: image generation, video generation, LLM structured outputs, product photography, UI/UX mockups, commercial advertising, and batch/programmatic generation.
JSON prompting uses structured data instead of free-form text to instruct AI systems. The results speak for themselves:
- ~92% precision for visual generation vs ~68% for natural language prompts
- 99%+ schema adherence for LLM structured outputs
- 60% reduction in AI errors across enterprise deployments
- No concept bleeding - adjectives stay isolated to their target element
- Reproducible - same structure, consistent results
- Batch-scalable - swap variables programmatically for mass generation
Generate structured prompts for AI image models with camera metadata, lighting specs, and composition controls.
Platforms: Nano Banana Pro, GPT-Image, Flux 2, Midjourney, Stable Diffusion
Create multi-scene sequences with camera movements, transitions, audio design, and entity consistency.
Platforms: Veo 3, Sora 2, Runway Gen-4, Kling 2
Force consistent, parseable responses from any LLM with schema enforcement, data type declarations, and few-shot examples. Covers data extraction, content generation, analysis/classification, API response formatting, meeting summaries, bug triage, and more.
Platforms: Claude, GPT-4o/o3, Gemini 2.5, or any LLM via prompt-level JSON
E-commerce and commercial product shots with brand preservation, surface/background control, and lighting precision.
Variants: Marketplace packshots, lifestyle context, flat lay grids, hero shots
App screen designs with device frames, design systems, layout hierarchies, and component specifications.
Brand-consistent ad creatives with text overlays, CTA elements, format adaptation, and logo placement.
Wrap any domain in a template system with {{variable}} placeholders for mass customization. Generate hundreds of variations from a single template.
Strategies: Color variants, background swap, platform adaptation, language localization, A/B testing, SKU catalogs
Copy the skill into your Claude Code skills directory:
mkdir -p ~/.claude/skills/json-prompt/references
cp SKILL.md ~/.claude/skills/json-prompt/
cp references/schemas.md ~/.claude/skills/json-prompt/references/
cp references/examples.md ~/.claude/skills/json-prompt/references/
cp references/showcase.md ~/.claude/skills/json-prompt/references/Invoke the skill in Claude Code with /json-prompt or let it auto-trigger:
/json-prompt image cyberpunk street scene at night with neon reflections
/json-prompt video product launch reveal for a smartwatch
/json-prompt product premium headphones on marble surface
/json-prompt llm extract contact information from emails
/json-prompt ui fitness app dashboard for iOS
/json-prompt ad Instagram carousel for a coffee brand
/json-prompt batch generate hero shots for 50 sneaker colorways
json-prompt/
SKILL.md # Main skill - 7 domain workflows, all JSON schemas
references/
schemas.md # Complete field reference for all platforms and domains
examples.md # 15 practical examples across all 7 domains
showcase.md # 8 detailed walkthroughs with user request -> JSON -> expected output
| # | Domain | Example |
|---|---|---|
| 1 | Image | Editorial portrait with camera metadata |
| 2 | Image | Product packshot for e-commerce |
| 3 | Image | Multi-character scene (concept bleeding prevention) |
| 4 | Image | App UI mockup |
| 5 | Image | Brand advertisement with text overlays |
| 6 | Video | Multi-scene product launch sequence |
| 7 | Image | Fantasy concept art with artist references |
| 8 | Image | Flat lay lifestyle composition |
| 9 | LLM | Data extraction from unstructured text |
| 10 | LLM | Content generation with brand voice constraints |
| 11 | LLM | Customer feedback analysis and classification |
| 12 | LLM | Meeting transcript summarization |
| 13 | Batch | Product photography across SKU variants |
| 14 | Batch | Social media ad platform/language adaptation |
| 15 | Batch + LLM | Support ticket triage at scale |
The references/showcase.md file contains 8 in-depth walkthroughs showing real user requests turned into complete JSON prompts with expected AI outputs. Each includes creative reasoning explaining why specific fields were chosen.
| # | User Request | Domain | Highlights |
|---|---|---|---|
| 1 | "Make me a cozy coffee shop scene for Instagram" | Image | Film stock selection, warm/cool light mixing, mood-driven composition |
| 2 | "15-second TikTok video ad for energy drink VOLT" | Video | 3-scene hook-build-payoff, speed ramps, audio design, 9:16 vertical |
| 3 | "Extract job posting info and rate my resume fit" | LLM | Dual-task extraction + analysis, match scoring, gap analysis with mitigations |
| 4 | "Luxury watch product photo for our website" | Product | Low-key lighting, selective metal highlights, premium positioning |
| 5 | "Analyze our app's user reviews and find patterns" | LLM | Per-review tagging + pattern clustering + business impact ranking. Full input/output shown |
| 6 | "Design a fintech app onboarding screen" | UI Mockup | Dark mode, trust signals, progressive disclosure, iOS 18 design system |
| 7 | "6 social media headers for our SaaS launch" | Batch + Ad | Single template producing Twitter, LinkedIn, Facebook, YouTube, Instagram, Discord variants |
| 8 | "Convert competitor landing page into structured analysis" | LLM | Messaging teardown, pricing psychology, competitive gaps. Full input/output shown |
| 9 | "Generate a REST API endpoint with validation and tests" | LLM (Code) | Full stack generation: Zod schemas, Drizzle ORM, Hono routes, bun:test, SQL migration, API docs |
| 10 | "Refactor this function and explain what changed" | LLM (Code) | Refactored code + diff summary + behavioral change flags + complexity metrics |
| 11 | "Generate TypeScript types from this API response" | LLM (Code) | Infers interfaces from raw JSON, generates typed fetch wrapper, documents type decisions |
- Separate subjects into distinct objects - Each character gets its own JSON block
- Use camera/lens metadata - Aperture, focal length, ISO for photorealism
- Be a Creative Director - Natural language descriptions, not keyword tags
- Constrain your palette - Use
color_paletteto prevent drift
- Always define data types -
string,number,booleanin your schema - Use enums for categories -
"low|medium|high"not free text - Include few-shot examples - 1-3 examples are the biggest accuracy boost
- Set
strict: true- Prevents unexpected fields
- Validate variable names - Every
{{placeholder}}needs a matching batch key - Test with one item first - Before scaling to hundreds
- Version your templates - Track iterations with
template_id
MIT