Update to fal

{
  "name": "fal-prompting",
  "description": "Apply model-family-specific prompting for fal.ai endpoints after an endpoint has been selected. Use for GPT Image 2, Kling, Happy Horse, Seedance, product prompts, exact text, multi-prompt video, and prompt debugging.\n",
  "included_files": [],
  "skill_md_contents": "---\nname: fal-prompting\ndescription: >\n  Apply model-family-specific prompting for fal.ai endpoints after an endpoint\n  has been selected. Use for GPT Image 2, Kling, Happy Horse, Seedance, product\n  prompts, exact text, multi-prompt video, and prompt debugging.\n---\n\n# fal.ai Prompting\n\nUse this after model selection. The goal is to write prompts that match the\nmodel family, not generic \"cinematic masterpiece\" text.\n\n## Universal Rules\n\n- Visual facts beat prestige adjectives.\n- Preserve one controlled variable per iteration.\n- Inspect schema before assuming negative prompts, seeds, multi-image inputs,\n  multi-prompt arrays, duration, audio, or camera controls exist.\n- Keep exact text short and quoted when the model supports text.\n- For product and character work, name the invariant explicitly in every prompt.\n- For video, describe action over time, camera motion, framing, and ending.\n\n## GPT Image 2\n\nUse for exact text, packaging, posters, UI, premium stills, and complex image\ncomposition.\n\nPrompt structure:\n\n1. Output type and composition.\n2. Exact subject/product/character.\n3. Style and medium.\n4. Lighting, material, camera, color.\n5. Text requirements, only if needed.\n6. Guardrails: no extra logos, no unreadable text, preserve reference details.\n\nGPT Image 2 benefits from structured, explicit prompts. It can handle longer\ninstructions than fast video models.\n\n## Kling\n\nUse for controlled video prompts when schema supports multi-prompt or element\ncontrols.\n\nPrompt structure:\n\n1. Subject identity and first frame.\n2. Shot type and camera motion.\n3. Action sequence in temporal order.\n4. Motion constraints.\n5. Ending state.\n\nKeep control fields schema-driven. Do not invent element fields without schema.\n\n## Happy Horse\n\nUse short natural-language prompts. Long production paragraphs usually hurt.\n\nGood pattern:\n\n`Handheld phone video, close shot of a runner tying neon shoes on wet pavement, soft morning light, quick push-in.`\n\nAvoid stacking style tags or long guardrails. Put important action first.\n\n## Seedance\n\nUse for high-quality cinematic image-to-video and text-to-video. Build prompts\nlike a short shot direction:\n\n- Subject/action.\n- Camera motion.\n- Environment.\n- Lighting.\n- Duration feel.\n- What should not change.\n\nFor image-to-video, the uploaded frame is the anchor. The prompt should animate\nthe frame, not redesign it.\n\n## Prompt Failure Fixes\n\n- Product changed: switch to reference/edit workflow and tighten invariants.\n- Text is wrong: use GPT Image 2, shorten text, or reserve safe space for\n  external typography.\n- Video drifts: reduce prompt scope, use image-to-video from an approved frame,\n  shorten duration, or choose a model with stronger controls.\n- Character identity drifts: use the character anchor wording and reference\n  images in every step.\n- Output is generic: replace style adjectives with concrete camera, light,\n  materials, setting, and action.\n"
}