Create faithful studio, lifestyle, and on-model product photography through the GooseWorks Product Photos workflow, then approve selected results for reuse in future creative work.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Turn real catalog images into publish-ready product photography while preserving silhouette, materials, logo, packaging, and colorway. The GooseWorks backend uses the same generation, fidelity review, and retry pipeline as its Product Photos studio.
This workflow requires the GooseWorks MCP tools. If they are unavailable, tell the user how to install the GooseWorks MCP connection and stop before generation.
list_ad_brands and the product with list_brand_products.import_product with a product URL, Shopify store, or public image URL. Poll get_product_import; do not re-submit an in-progress import.estimate_product_photos and show the user the credit estimate. Confirm count and quality before spending.generate_product_photos with the chosen product, category, controls, count, quality, and optional prompt. Human model imagery requires the user's rights attestation.get_product_photo_generation until complete, partial_failure, or failed. Do not submit a duplicate while it is running.approve_product_photo only for selected results and archive_product_photo for rejected ones.list_ad_brands, list_brand_products, import_product, get_product_importestimate_product_photos, generate_product_photos, get_product_photo_generationlist_product_photos, approve_product_photo, archive_product_photoまだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Research supported advertising promises from a specific buyer's context, current product facts, customer language and observed ads. Turn them into static, video or copy ideas; match video ideas to actual recipes and save a product-scoped angle bank for write-video-ad-script. Product-led discovery can use verified facts without reviews. Reference persistence and reach are observations, not proof of performance.
日本語の概要は準備中です。原文の説明を表示しています。
Analyze ad campaign performance data (Google, Meta, LinkedIn) to identify what's working, what's wasting budget, and specific cut/scale/test recommendations. Runs statistical analysis, funnel diagnostics, and multi-channel budget reallocation with specific dollar-amount shift recommendations and scenario modeling.
日本語の概要は準備中です。原文の説明を表示しています。
For paid lead-gen and participant-recruitment ads, replaces vanity CPA with true CAC per qualified lead by joining ad-platform data with downstream funnel events, surfaces tracking gaps, and classifies every creative into Scale / Keep / Investigate / Cut.
日本語の概要は準備中です。原文の説明を表示しています。
The intake stage of a Meta ad harness. Researches the brand first, states what it assumes, and asks only what it cannot infer and what would change money, claims or legal exposure if guessed wrong — at most four questions a turn, in plain business language. Writes the brand's stable facts (ads/brand.md), the campaign brief, and a log of what was asked vs assumed. Use it when someone wants to run Meta ads and the brand's ad facts are not yet recorded.
日本語の概要は準備中です。原文の説明を表示しています。
Analyze the message match between your ads and landing pages. Checks if the promise in the ad copy carries through to the landing page headline, body, and CTA. Flags disconnects that kill conversion rates. Works with Google, Meta, and LinkedIn ads.
日本語の概要は準備中です。原文の説明を表示しています。
Turn a brand's facts, a campaign brief and the product into a one-minute Meta ad strategy with its reasoning — goal, audience, objective and placements, budget split, how many ads and how different they must be (generate 3x so the user can choose), the test frame, the success metric, and safety rules for the user to accept. Decides what launch-meta-ad-campaign then executes, and re-reads the strategy after a deep check to show which assumption changed. Use it after intake and before any creative is generated or any campaign is built.
日本語の概要は準備中です。原文の説明を表示しています。