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Equips sellers with macro industry trends by analyzing trending data and specific analyst/competitor channels.
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Makes an existing Python recipe deployable: generates the serving files a container needs (Dockerfile, .dockerignore, fast_api_app.py, app_utils/a2a.py, app_utils/services.py, app_utils/reasoning_engine_adapter.py) and configures the recipe to match (required serving dependencies, the App object in agent.py, the hatch wheel package, manifest.deployable). Interactive by design — it asks the recipe owner about runtime data directories and stops for a human decision when a recipe needs an ADK migration or carries a legacy app_utils generation. When docker is available it offers to PROVE the claim: it builds the generated Dockerfile, runs it, probes it, and refuses to flag a recipe deployable if the container does not come up. Does NOT deploy or write terraform. Use when the user wants to "make this recipe deployable", "add a Dockerfile to a recipe", "add the serving files", "containerize a recipe", "verify the container builds", or prepare a recipe for Cloud Build / Artifact Registry.
End-to-end orchestration to prepare or update a Python recipe under core/python/, contrib/python/, or skills/<vertical>/<solution>/ so it passes every check in .github/workflows/python-validate-recipe.yml. Runs eight phases in order on an already-in-place recipe: manifest.yaml generation, environment-variable extraction, pyproject.toml alignment, ruff format+check, per-recipe `uv lock`, runnability-test generation, compile-and-run verification of the generated test file, and a final pass through the repo's own `validate manifest` / `validate structure` validators. Assumes the user has already done the manual prep (deactivated any venv, `git pull` and `uv sync` from the repo root, placed the recipe at its target path, renamed if needed). Delegates to the existing sub-skills (generate-manifest, extract-python-environment-variables, align-recipe-pyproject, generate-python-runnability-test) so the master never duplicates their logic. Pauses at fixed decision points (description mismatch, existing test regeneratio
Creates virtual try-on agents supporting image and video (catwalk animation) try-ons on Google Cloud (Gemini image models and Veo on Gemini Enterprise Agent Platform). Handles resource setup, user photo uploading, image/video generation pipelines, local testing, and evaluation.
| name | industry-landscape-briefing |
| description | Equips sellers with macro industry trends by analyzing trending data and specific analyst/competitor channels. |
Objective: Equip an internal Google Seller with high-level, macro trends in a specific industry (e.g., Mobile Gaming, Fintech) before they meet with a client. Go beyond keyword spam and find actual industry movements.
Execution Steps:
get_date_range("week")).get_trending_videos tool. Provide a relevant video_category_id (e.g., "20" for Gaming, "28" for Science/Tech) and a region_code relevant to the seller's market. What is naturally rising to the top?search_channel_videos to pull exactly what that authority figure published this week.get_video_transcript on the top 2-3 videos returned from Steps 2 and 3. Do not just read titles. Synthesize the actual industry shift being discussed.generate_timestamp_url) to the exact moments the industry shifts are analyzed by creators.Next Actions: Ask the user if they want to publish this Briefing as a clean HTML asset using publish_file.