google/adk-recipes

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name: blog-writer

description: Blog post writing skill with structure templates and style guidelines. Guides the agent through writing well-structured, engaging technical blog posts with proper formatting, section flow, and reader engagement techniques.

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name: content-research-writer

description: Content research and SEO writing methodology. Guides the agent through topic research, keyword identification, competitive analysis, and writing SEO-optimized content that ranks well and provides genuine value to readers.

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name: agents-cli-aqua

description: Work with the Ambient Quality Agent (AQuA) added to this agents-cli project: augment the agents-cli agent with AQuA or attach the agent to an AQuA deployed elsewhere, read the quality insights it finds in production conversations, get AQuA's root-cause diagnosis, read what the developer asked it to remember, and fix the defects in the agent's code. Propose proactively when the user mentions AQuA or AQA, wants to monitor the quality of a deployed agent, mentions traces or observability, asks what is going wrong with their agent in production, wants to fix AQuA insights, or wants to publish code metrics to AQuA. Don't use for offline evals of a local agent (use google-agents-cli-eval) or for deploys without AQuA (google-agents-cli-deploy).

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name: custom-investigation

description: Run a custom investigation: review a chosen slice of conversations instead of a random sample, by writing a SQL selector over the observed agent's telemetry, and optionally narrow what the reviewer reports. Use when a message asks to investigate only certain conversations -- ones that hit an error, used a particular tool, ran slowly, mention a topic, looped, or were abandoned -- or asks to re-review conversations already seen with a different focus. Covers the telemetry table a selector reads, writing the selector, previewing what it matched, and starting the run.

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name: rca

description: Root-cause analysis of one AQuA insight against the observed agent's own source code. Use when a message names an insight and asks what the root cause is, how to fix it, why the agent behaved that way, or to diagnose a failure -- for example `Diagnose insight <id> ("<label>") — what is the root cause, and how would you fix it?`. Reads the source snapshot at the revision the failure ran on, plus the complete conversations behind the insight, and answers with `<path>:<start>-<end>` citations, and records the proposed fix against the insight.

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name: bootstrap-google-tools

description: Install/auth CLIs the sandbox lacks - `gws` (Drive, Gmail, Sheets, Calendar), `gcloud`, `agents-cli`, `mcp-cli` (MCP servers). Use on "command not found" or before GCP/Workspace/MCP work.

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name: find-skills

description: Discover/install NEW agent skills from external sources (skills.sh). Use for capabilities you lack, e.g. "find a skill for X". Do NOT use to list skills you already have; see `<available_skills>`.

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name: google-workspace

description: Read/write Google Drive, Docs, Sheets, Gmail, Calendar, Chat, Tasks, Slides via the `gws` CLI. Needs an OAuth client or service account configured first. Use for any Google Workspace task.

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name: policy

description: Inspect the per-workspace tool-policy overlay (.lha/policies.jsonl) that gates destructive or sensitive tool calls, and tell the user what to change if they want a rule added or removed.

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name: routines

description: Use for a recurring unattended task (cron; pull data, open a PR, post a digest), or to test/dry-run a routine first. Explains authoring and testing via the routine tool.

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name: retail-product-search

description: Creates product search agents with semantic search and RAG on Google Cloud (Vector Search on Gemini Enterprise Agent Platform, BigQuery, embeddings). Use when the user wants to "build a product search agent", "create an e-commerce search", "make a shopping assistant", "set up semantic catalog discovery", "ingest products into Vector Search", or "deploy a retail RAG agent". Handles the full pipeline: catalog data ingestion to BigQuery, Gemini Enterprise Agent Platform Vector Search collection setup, ADK agent scaffolding, evaluation, and Cloud Run deployment.

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name: retail-virtual-tryon

description: 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.