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.
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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还没有评价。欢迎分享你的使用感受。
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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.
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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.
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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.
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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.
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Checks paid ads against the ad platform's rules (Meta, TikTok, Google, LinkedIn and others) and the brand's category rules: the copy, on-image or on-screen text, pictures and people. Rewrites what can be fixed honestly, adds the disclosures that are needed, and flags anything that needs a human or legal review, with one pass, fixed or flag line per ad and platform. Use alone when you already have the ad text or images and know where they will run; otherwise run the playbook for the job (e.g. make-static-ads, make-content, email-lifecycle).
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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.
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