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Design patterns and operational guidance for running agents in remote sandboxed environments with warm pools, snapshots, and session orchestration.
Describes architecture and operational patterns for running AI agents in hosted sandbox environments. Covers image build strategies, warm-pool management, snapshot/restore, auth flows, and techniques to minimize cold-start latency.
Activate this skill when building background agent infrastructure, pre-warmed sandboxes, or systems where agents spawn sub-sessions and create PRs or push code on behalf of users. Use it for decisions about scaling, session isolation, and real-time streaming.
Agent platforms that integrate with sandboxed runtimes and GitHub (e.g., Claude Code, Cursor, Copilot-style tooling, and custom server-first agents).
Well-structured reference skill for designing hosted agent infrastructure with sandboxed execution, warm pools, and session orchestration. SKILL.md is thorough and well-organized. The bundled sandbox_manager.py script is pseudocode with architectural patterns — it runs but crashes in the demo because _cold_start and other infrastructure-specific methods are stubs (pass). DeprecationWarning on utcnow(). No security concerns beyond using f-strings for git config (minor shell injection surface in sandbox context, mitigated by sandbox isolation).
This is a reference/architectural skill, not a runnable tool. The script is well-designed pseudocode demonstrating patterns. SKILL.md is exemplary — clear scope, boundary decisions, integration map, practical checklist, and gotchas. Docked code quality for stubs that crash in demo and deprecated datetime usage. Docked security slightly for f-string command construction. Architecture score is high due to excellent separation of concerns, clear frontmatter, references section, and progressive disclosure.