
How Google Builds and Tests Agent Skills
Google's playbook for scaling Agent Skills: standard structure, CI, with-vs-without evals, accuracy and efficiency, ownership, and safe export.
ReadInsights on AI agents, model routing, and building production-ready AI systems.

Google's playbook for scaling Agent Skills: standard structure, CI, with-vs-without evals, accuracy and efficiency, ownership, and safe export.
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Compare Google Cloud API Gateway model routing, LiteLLM, and OpenRouter on model reach, routing control, fallbacks, data boundaries, cost, and operations.
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Review Open Multi-Agent's dynamic task DAGs, mixed coding-agent backends, approvals, replay, security defaults, and when the complexity pays off.
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Review Orloj's infrastructure-as-code approach to multi-agent systems: declarative resources, governance, workers, retries, isolation, and trade-offs.
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Ouroboros can modify its own harness through reviewed Git commits. We examine its memory, evolution loop, benchmark claims, and safety boundaries.
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Review UiPath for Coding Agents: how skills and the uip CLI build and operate automations, where approvals sit, and what enterprises must validate.
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Warp Agent CLI has become Oz CLI. Learn local and cloud runs, profiles, MCP, skills, orchestration, and how it compares with Claude Code and Codex.
ReadA reproducible workflow combining a portable research Skill with sandboxed execution, audit trails, and replay using two open-source SandBase projects.
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NVIDIA Switchyard routes Claude Code, Codex, and OpenClaw to hosted or local LLMs. Learn how protocol translation, routing profiles, and fallback work.
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