product-photoshoot
Skill2 starsChanged 28 days agoMITView on GitHub
Create product photographs or coordinated fashion lookbooks from supplied product images, SKUs, or saved references. Use for "shoot this product", ecommerce packshots, lifestyle product shots, "re-shoot our catalog", or the same product across several scenes. For garments worn by a person use virtual-try-on; a whole-store template pipeline uses store-photoshoot. Respect a named provider and requests for advice only.
In this repository15 other files it gives its agents.
- CLAUDE.mdCLAUDE.md
- docs/CLAUDE.mdCLAUDE.md
- astria-apiSkill
- avatarSkill
- behind-the-scenesSkill
- fashion-music-trackSkill
- headshots-from-photosSkill
- landing-page-editorSkill
- muxSkill
- navigationSkill
- packs-guideSkill
- prompt-writingSkill
- store-photoshootSkill
- unique-headshotSkill
- virtual-try-onSkill
The file
We split this skill into the rules it gives an agent
6 rules. About 0 are common, 4 are distinctive, and 2 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈4
Distinctive · estimate
This author's own choices. Worth a look.
2
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 12
Use astria-api for live schemas, reference tokens, HTTPS media transport, idempotency, workspace scope, and checking completion.
- Very specific: line 17
Use the supplied images or reference IDs.
- Distinctive: line 22
Ask for the product image only when no usable reference is available.
- Distinctive: line 24
Use the requested number of shots and scenes.
- Distinctive: line 32
Do not author or modify a template merely to run a photoshoot.
- Very specific: line 38
Use the returned subject class after its token, for example <faceid:123:1> bag.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Product photoshoots
Produce the requested product photographs with Astria, retaining the supplied product's visible design, color, material, logos, and proportions. Do not claim that generated images guarantee exact SKU fidelity.
Use astria-api for live schemas, reference tokens, HTTPS media transport, idempotency, workspace scope, and checking completion.
Resolve inputs and scope
Use the supplied images or reference IDs. For a named product or SKU, search
list_references by title, then inspect the match with get_reference.
Resolve ambiguity rather than selecting an unrelated SKU. If the only input
is a local path or attachment, obtain an accessible HTTPS URL through a host
upload capability or the optional authenticated CLI; never invent a URL.
Ask for the product image only when no usable reference is available.
Use the requested number of shots and scenes; for an unspecified single-product shoot start with one image and sensible studio lighting. An explicit generation request authorizes that scope. Planning a shoot does not authorize submission.
Make the photographs
For an existing template, use list_templates / get_template before
run_template. Preserve reference-slot order from the returned template.
Do not author or modify a template merely to run a photoshoot.
For a one-off targeted edit of an existing product shot, use generate_images
with input_image and the edit instruction. For a product reused across
several scenes, reuse an existing reference or create one with
create_reference from the supplied views; reuse its ID on every shot.
Use the returned subject class after its token, for example
<faceid:123:1> bag. Discover models with list_models; use the user's
selection or catalog default rather than a hardcoded model ID.
A multi-scene request needs distinct scene prompts, not repeated copies of one prompt with a larger image count. Keep product details and reference IDs stable while varying only the requested setting, angle, lighting, or styling.
Deliver and review
Retain each returned prompt ID and check get_prompt for completion.
Show all requested finished assets through the host's result presentation;
identify pending or failed shots accurately. Check visible product details
against the source when images can be inspected. Report material mismatches;
do not submit extra paid corrections beyond the requested scope.
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