본문으로 건너뛰기
cccskills
무료GitHub에 공개

Consented Romantic Video

Create a non-explicit romantic scene from authorized adult references with clear consent, identity boundaries, and synthetic-media labeling.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

설치 방법 보기

포함된 파일(1)

  • SKILL.md3.9 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

Consented Romantic Video

Mission

Create a non-explicit romantic video draft—such as an embrace or kiss—from authorized adult reference images or an approved starting frame. Preserve the distinction between a creative depiction and a real event. This workflow never authorizes non-consensual face merging, sexualized deepfakes, impersonation, or the use of minors or public figures.

Mandatory consent gate

Require confirmation that every identifiable adult has authorized:

  • use of their image or likeness;
  • the described romantic action;
  • the intended audience and distribution; and
  • any generated audio, voice, or promotional association.

Treat a supplied photo, public profile, relationship claim, or “they would be okay with it” as insufficient. If consent is unknown, stop and request an approved fictional or adult source. Do not proceed with minors, coercion, non-consensual intimacy, public-figure likeness, or a scene intended to deceive someone about a real relationship or event.

Required inputs

  • one or more authorized adult reference images or an approved parent frame;
  • participant IDs, reference roles, and what identity details must remain;
  • non-explicit scene, setting, wardrobe, action, camera, and duration;
  • target ratio, resolution, audio, disclosure, and intended use; and
  • reviewer, variant count, budget, and rights/consent record.

Do not assume gender, relationship, age, orientation, or appearance from a photo. Do not automatically stitch two portraits or use face-swap tooling when a reference-capable image-to-video path can preserve roles more transparently.

Choose the generation path

Use MODELS.md and the live schema:

  • one approved composite/start frame: I2V with a hosted image_url;
  • multiple authorized references: a model-specific images_list or image_urls path with explicit participant order; or
  • a supplied romantic parent clip: use the editing workflow instead of regenerating identity.

Veo, Wan, Gemini Omni, Kling, and other families accept different image-list fields, durations, audio defaults, and ratios. Resolve the exact endpoint through muapi-video-tools.md. Do not copy fields from the source application's model list.

Execute and review

  1. Record consent state and freeze the scene description. Keep the action affectionate and non-explicit.
  2. Upload references and record role/order, provenance, returned URLs, and retention expectation without storing secrets.
  3. Render one proof before variants. Retain prediction ID and poll the result.
  4. Review identity, age presentation, facial boundaries, hands, contact, wardrobe, temporal stability, unwanted nudity, audio, framing, and synthetic disclosure. Use video QA.
  5. Return parent/child relationships, failed or blocked branches, and the smallest safe iteration. Do not describe the output as evidence of a real relationship or event.

Output contract

Return participant IDs, consent/distribution state, reference roles, scene brief, model/endpoint, prediction ID, output URL, status, disclosure label, QA findings, and any blocked requests. Save permitted receipts under .video/ using reports-and-receipts.md.

Generation is draft-only. Publishing or sharing requires the participants' explicit approval and any applicable synthetic-media disclosure.

리뷰

아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.

댓글

로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.

아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.

보내면 바로 공개됩니다. 이메일 주소나 전화번호 같은 개인 정보는 쓰지 마세요. URL은 2개까지입니다.

0 / 1,000자

이 양식은 reCAPTCHA로 보호되며 Google 개인정보처리방침 및 서비스 약관이 적용됩니다.

같은 저장소의 스킬

Plan draft video-ad variations from supplied performance evidence, preserving the distinction between observed data and creative hypotheses.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

SamurAIGPT/open-ai-video-agent82026년 8월 28일 업데이트

Create authorized presenter, avatar, and UGC-style video drafts with explicit voice, likeness, consent, and synthetic-media handling.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

SamurAIGPT/open-ai-video-agent82026년 8월 28일 업데이트

Analyze a script or existing edit, identify visual gaps, and generate supplemental B-roll coverage with clear parent and factual-status labels.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

SamurAIGPT/open-ai-video-agent82026년 8월 28일 업데이트

Build a consented continuity system for an authorized person, fictional character, product, or world across video shots.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

SamurAIGPT/open-ai-video-agent82026년 8월 28일 업데이트

Translate creative intent into coherent cinematography, framing, camera, lens, lighting, motion, and sound direction for a video shot.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

SamurAIGPT/open-ai-video-agent82026년 8월 28일 업데이트

Turn one approved topic into a curiosity-led 3D educational short with character sheets, ordered keyframes, motion clips, narration, captions, and an auditable assembly plan.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

SamurAIGPT/open-ai-video-agent82026년 8월 28일 업데이트

SamurAIGPT의 스킬 모두 보기

이 스킬의 문제 신고