gen-ai-livestream

Use this skill when you want to treat agent system prompts like build artifacts instead of one giant hand-edited file. Covers authoring modular prompt fragments, transpiling them into a single rendered golden file with Jinja2, build-time validation for missing variables and circular imports, and CI drift checks that keep the committed prompt equal to source. Tools used, Python, Jinja2. Ships two runnable utilities, transpile.py and drift_check.py.

Use this skill when evaluating agent memory options, when running or reviewing the always-on memory agent sample (Google generative-ai repo, by Shubham Saboo), or when deciding between a self-hosted memory loop and the managed Vertex AI Memory Bank. Covers how the sample works and its rough edges, and a verified Memory Bank quickstart. SDKs used, Google ADK, google-genai, vertexai agent engines, Gemini 3.1 Flash Lite.

Use this skill when governing an AI agent's tool calls with a plain-English policy on Google's Gemini Enterprise Agent Platform, using Semantic Governance Policies (SGP). Covers deploying an ADK agent to Agent Runtime behind the Agent Gateway with agent identity, standing up the SGP engine and the VPC network path (PSC endpoint, network attachment, DNS peering), wiring the authz extension and policy, and creating a natural-language constraint that returns ALLOW or DENY on each proposed tool call. Tools, ADK (google-adk), vertexai agent_engines, gcloud network-services / compute / ai, Semantic Governance Policies.

Use this skill when adding on-device semantic search, FAQ matching, deduplication, clustering, or RAG reranking to a JavaScript or TypeScript app with no server, no API key, and no runtime model download. Covers the @ternlight/base and @ternlight/mini WASM sentence-embedding packages, the embed / cosineSim / similar API, indexing a corpus once and searching it, and the Vite and webpack bundler setup for the browser build. Works in Node, browsers, Cloudflare Workers, Vercel Edge, Deno, and Bun.

Use this skill when running untrusted or model-generated code safely on Google Cloud Run, building an AI code interpreter, or letting an agent execute Python it wrote without exposing your service credentials. Covers deploying a Cloud Run service with the sandbox launcher, running code in a sealed sandbox via the sandbox CLI, the deny-by-default network and per-call egress control, credential and metadata isolation, and the ADK CloudRunSandboxCodeExecutor one-liner. Tools and SDKs, gcloud beta run, the /usr/local/gcp/bin/sandbox binary, google-genai, google-adk.

Use this skill when running Google's AlphaEvolve, the Gemini-powered evolutionary coding agent, to discover or optimize an algorithm on Google Cloud. Covers wrapping a function in EVOLVE-BLOCK markers, writing a scoring function, driving the controller loop against a Gemini Enterprise engine, reading back the best evolved program, and the license, cost, and non-monotonic-progress gotchas. Uses the alpha_evolve client library, Discovery Engine, gemini-3.5-flash and gemini-3.1-pro-preview.

Use this skill to evaluate an ADK agent with Google's Agent Quality Flywheel, run an evalset, choose the right metric, and read the results. Covers the ADK metric menu and which metrics run locally versus which call an AI judge, how the LLM judge scores meaning rather than words, how a rubric judge writes a plain-English reason for each verdict graded against trusted evidence, and how to close the eval fix loop. SDKs and tools used, agents-cli, adk eval, google-adk[eval], the Gemini API for the agent and the judge.

Use this skill when working with TabFM, Google's zero-shot tabular foundation model, running classification or regression on a CSV or DataFrame without training, benchmarking TabFM against XGBoost or TabICL, or debugging TabFM installation and weight loading. Covers the tabfm Python package, TabFMClassifier and TabFMRegressor, Hugging Face weight download, the PyPI loader bug, CPU dtype performance, hard model limits, and the weight license.

Use this skill when running Gemini API Managed Agents through the Interactions API, especially background execution (background=True, poll and reconnect by interaction id), attaching remote MCP servers as tools, custom function calling with the requires_action flow, and reusing a server side sandbox across turns with environment_id. Covers the antigravity managed agent, the client.interactions SDK surface, and the agent vs model gotcha. SDKs and tools used, google-genai Python SDK (client.interactions), Python 3.10+, Gemini API key.