본문으로 건너뛰기
cccskills
무료GitHub에 공개

scenario-product-shots

Use when producing product photography with Scenario from a real product photo: e-commerce packshots on white, transparent, or brand backgrounds, lifestyle scenes placing the product in an environment, hero shots for a landing page or marketplace listing, angle and colorway sets, relighting or shadow fixes, or any shot where the label, logo, and shape must stay exact. Keywords: product photo, packshot, e-commerce, lifestyle shot, compositing, relight, label fidelity, catalog.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

설치 방법 보기

포함된 파일(1)

  • SKILL.md7.1 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

Scenario Product Shots

Overview

The product is never generated. A text-prompted bottle ships a wrong label to a landing page; every credible shot starts from an uploaded photo of the real product, and fidelity is a gated check, not a hope. Two lanes cover most work: deterministic packshot tools (cutout, background, shadow, relight) and generative scene placement with an instruction-editing model. Connection and the core loop: see the scenario skill. Edit-model contracts: scenario-image. Deterministic tools: scenario-image-editing. Reading assets back: scenario-asset-analysis. Animating an approved still into an ad: scenario-video-ads. If a sibling skill named here is missing from your available skills, ask the user to install it (npx skills add scenario-labs/skills --skill <name>); unattended, proceed from tool schemas and flag the gap.

Quick reference

NeedRoute
The sourceupload_asset the best photo available: sharp, evenly lit, whole product in frame (search "uncrop" finds tools that rebuild a clipped edge)
Fidelity checklistasset_analyze the upload once, instructing an inventory of label text, geometry, materials, and colors; every later check reuses it
Packshotrecommend with the packshot need in the user's own words (a cutout-and-stage tool with solid, transparent, or custom backgrounds, margins, and shadows was the authoring-time hit), or background removal plus your own compose
Lifestyle scenerecommend with capability="img2img", product photo as reference, preserve-first prompt, one scene per run
Relightsearch "relighting" (the authoring-time hit adjusts light, exposure, and mood, with a brand-color lock)
Upscale keepersrecommend with the user's own words; product-tuned upscalers existed at authoring time
Gateasset_analyze the outputs against the checklist: up to 10 ids in images, the saved checklist in text_inputs, the letter-by-letter brief in instruction

The preserve-first prompt

Scene prompts subordinate the world to the product: "The exact can from the reference image, proportions and colors unchanged, the label reads 'SUMMIT COLD BREW', no other text, standing on a wet slate counter, morning side light, shallow depth of field." Name the placement, the surface, the light. What goes unstated drifts: "label unchanged" alone leaves the type to whatever the model resolves from the reference, so the preserve clause quotes the checklist's label copy (existing on-pack text only) and the gate reads it back letter by letter rather than trusting the render.

Shadows and reflections carry the realism: a cutout pasted without them floats. Prefer a stage tool that rebuilds shadows, or name one in the edit prompt ("soft contact shadow falling right").

Worked example: one can, a packshot plus three scenes

  1. upload_asset the studio photo, then upload_asset_complete: asset_can.
  2. Build the checklist once with asset_analyze (write lane, contract in scenario-asset-analysis); the inventory lands as a text asset, so asset_download it and keep the text.
  3. Packshot: recommend with the packshot need in the user's own words, model_schema_get the pick, then run it with asset_can in its image field, the background set to the brand hex, and margins per the marketplace's current spec (confirm specs with the user; unattended, take them from the task instructions, else keep the tool's defaults).
  4. Scenes: recommend with capability="img2img"; on next_step.type="ask_user", present the options (unattended, the task instructions name the pick, else proceed: specialty.model_id first, skipping a specialty whose caveats or when_general_better name the task at hand, then the top ranked entry, never one flagged requires_plan_upgrade). model_schema_get the pick, then three runs, each the preserve-first prompt with one scene clause and asset_can wired as the schema says (an array only under array: true).
  5. jobs_wait, then gate all four outputs in one asset_analyze call, the saved checklist passed via text_inputs and an instruction to read the label back letter by letter. A drifted label fails the shot: re-run from asset_can with the preserve clause tightened (the quoted copy spelled letter by letter, "no other text" kept), never from the drifted output. Text the gate cannot resolve at output resolution is unverified, not passed: upscale and re-gate, or flag it in the delivery note.
  6. Upscale the keepers, asset_download with format="png", file the set in a collection.

Common mistakes

  • Generating the product from a text description because the photo seems easy to describe: the one unfixable error, since no edit restores a label that never existed.
  • Compositing from a screenshot of a crop: fidelity caps at the source; ask for the original file, and when nobody can supply one, proceed with the best source at hand and flag the ceiling in the delivery note.
  • Skipping the gate because it "looks fine": label drift hides at thumbnail size; the checklist compare reads letter by letter.
  • Prompting prices, claims, or promo copy into the image: overlay them with scenario-text-overlay; regulations and locales change faster than plates.
  • Removing the background and losing the real shadow with it: restage with a shadow-building tool or prompt a new one.
  • One run with a batch count for "the same scene, four angles": per-angle clauses need one run each (scenario-image).

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

Handle review comments received on GitHub for the current branch's PR or a given PR number or URL, by triaging each unresolved thread, fixing what is warranted, pushing, and replying in each thread, when asked to address, fix or answer PR comments. Works in any Scenario repository.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

scenario-labs/skills1.1천2026년 10월 24일 업데이트

Generate one short Conventional Commits squash message describing only the net diff between the PR's base and head, set its header as the PR title, validate it with the repository's commitlint and both copy its description to the clipboard and display it, when asked for a squash message. Works in any Scenario repository, whatever its default branch.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

scenario-labs/skills1.1천2026년 10월 24일 업데이트

Refresh the current pull request description from its net changes, following the repository's PR template, when asked to update or write the PR summary. Works in any Scenario repository, whatever its default branch.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

scenario-labs/skills1.1천2026년 10월 24일 업데이트

scenario

무료

Use when connecting an AI agent to Scenario (scenario.com) through MCP, or when a task involves generating images, video, 3D, audio, sprites, textures, or game assets. Also when picking a Scenario model, running a LoRA, refining a generation prompt, uploading reference images, waiting on generation jobs, checking credits or quota, hitting Scenario auth, scope, or Forbidden errors, or setting up mcp.scenario.com in Claude Code, Cursor, VSCode, or another agent.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

scenario-labs/skills1.1천2026년 10월 24일 업데이트

Use when generating or handling 3D assets through the Scenario MCP server, including text-to-3D or image-to-3D meshes, GPT-6 Astra 3D or Claude Opus 5.5 3D, GLB, FBX, OBJ, STL, or VOX files, PBR-textured or game-ready models, voxel models, multi-view reconstruction, editing a mesh by prompt, retexture, remesh, UV unwrap, auto-rigging a biped or quadruped, or retargeting an animation, previewing a mesh in the 3D viewer, or downloading a model for import into Unity, Unreal, Godot, or Blender.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

scenario-labs/skills1.1천2026년 10월 24일 업데이트

Use when generating navigable 3D worlds or Gaussian splat scenes on Scenario via MCP: text-to-world, image-to-world, a 360 panorama or skybox turned into a walkable splat, multi-view photos or a video walkthrough reconstructed as a splat, or one object photo to splat. Keywords: World Labs Marble 1.1, 1.1 Plus, 1.0 Draft, Hunyuan World, HY World, image-to-skybox, skybox-to-splat, multiview-to-splat, TripoSplat, gaussian splatting, 3DGS, spz, world generation, explorable scene, VR.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

scenario-labs/skills1.1천2026년 10월 24일 업데이트

scenario-labs의 스킬 모두 보기

이 스킬의 문제 신고