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ai-product-photoshoot

Turn one product photo into a full ecommerce image set with the PopNow API: clean white-background main image, lifestyle scenes, angle and detail shots, infographic slots and marketplace sizes for Amazon, Shopify, Etsy and social. Quotes every image in USD first. Use when the user needs product photos, listing images or a catalog shoot without a studio.

Quality

88%

Does it follow best practices?

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SecuritybySnyk

Passed

No findings from the security scan

SKILL.md
Quality
Evals
Security

Quality

Content

81%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A well-built skill body: executable commands, a payment-safe workflow with strong validation and feedback loops, and disciplined conciseness. The main gaps are the missing worked input_json example and a moderately long single-file body that could offload model/cost reference detail.

Suggestions

Add one small worked example of an `input_json` object (e.g., a white-background main-image edit) so the quote/create steps are fully copy-paste executable.

Move the model path table, billing/limits detail and the link/license footer into a short reference file (e.g., references/models.md), keeping SKILL.md as the overview.

Trim conversational asides like "pending is not failure" and "The helper does not fetch them" that add tokens without adding guidance.

DimensionReasoningScore

Conciseness

The body is lean and PopNow-specific with no filler explaining concepts Claude already knows, but a few lines could be trimmed ("pending is not failure", "The helper does not fetch them") and the versioned model table ("PopNow model paths at the time of writing") is time-sensitive, though mitigated by the "Confirm with `models`" instruction. Not 5 because of these minor padding/version-detail instances.

4 / 5

Actionability

Four concrete, copy-paste-ready commands (models/quote/create/status) with exact argument specs and a specific review checklist ("wrong logos, extra parts, warped text or changed colours mean regenerate"). Not 5 because no worked `input_json` example is shown, leaving the most important payload format as a placeholder.

4 / 5

Workflow Clarity

A clearly sequenced workflow with explicit validation checkpoints for a paid batch operation: quote table → explicit user approval → one test item → generate rest → review against source; plus error-recovery loops (re-quote on expiry/price change, retry with the same idempotency key, regenerate bad outputs).

5 / 5

Progressive Disclosure

Sections are well organized and the referenced helper (`scripts/popnow.py`) exists and is one level deep, but the ~110-line body inlines the model catalog, billing/legal detail and a link footer that could live in small reference files. Not 3 because structure and signaling are genuinely good; not 5 because there is minor content that would be better split out.

4 / 5

Total

17

/

20

Passed

Description

92%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A strong description: concrete capability list, explicit use-when triggers, and a distinct ecommerce-photoshoot niche. The only improvement is broader synonym coverage of natural trigger terms.

DimensionReasoningScore

Specificity

The description lists multiple concrete deliverables — "clean white-background main image, lifestyle scenes, angle and detail shots, infographic slots and marketplace sizes" — plus USD quoting, giving comprehensive coverage of the skill's actions with no real gaps.

5 / 5

Completeness

It explicitly answers both what ("Turn one product photo into a full ecommerce image set...") and when ("Use when the user needs product photos, listing images or a catalog shoot without a studio") with concrete trigger phrases.

5 / 5

Trigger Term Quality

Natural phrases like "product photos", "listing images" and "catalog shoot" plus marketplace names (Amazon, Shopify, Etsy) give good keyword coverage, but common variations such as "product photography", "lifestyle shots" or "white background" images are missing. Not 3 because several genuinely natural user phrases are present; not 5 because synonym coverage is incomplete.

4 / 5

Distinctiveness Conflict Risk

A clear niche — ecommerce listing image sets via the PopNow API with named marketplaces — makes it unlikely to trigger for the wrong skill; the trigger phrasing is specific to product/catalog imagery.

5 / 5

Total

19

/

20

Passed

Validation

87%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 14 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

metadata_version

'metadata.version' is missing

Warning

metadata_field

'metadata' should map string keys to string values

Warning

Total

14

/

16

Passed

Repository

popnow-ai/popnow-skills

Path

skills/popnow-ai-product-photoshoot/SKILL.md

Reviewed

Table of Contents

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