Agent skill · aws
amazon-workspaces-agent-access
Connects AI agents to remote Windows desktop applications on Amazon WorkSpaces Applications (AppStream 2.0) through the managed Agent Access MCP server, and guides reliable desktop automation. Covers connecting an agent to the MCP endpoint (SigV4, streaming URL, and Active Directory SAML/Domain Join), BLOCKING vs POLLING connect modes, the computer-use tools (screenshot, click, type, key, scroll), screenshot-budget and action-batching discipline, MCP tool forwarding (forwarded___ tools), session lifecycle and expire-on-delete, and troubleshooting connection errors. Use when building or debugging an agent that drives a remote Windows desktop or GUI application via WorkSpaces Applications / AppStream — including "dcv session not ready", "client_disconnected", 400 signing-region, POLLING/connection_status, SAML assertion, or forwarded tool questions. Not for Amazon WorkSpaces Personal/Core virtual desktops or general AppStream fleet administration unrelated to agent access.
What it needs
About 5k tokens when loaded.
What this skill does
Amazon WorkSpaces Applications — Agent Access Domain expertise for connecting AI agents to remote Windows desktops on Amazon WorkSpaces Applications (AppStream 2.0) via the managed Agent Access MCP server, and for driving those desktops reliably. How it works: Agent Access is MCP-only — there is no AWS CLI/SDK command that calls the desktop tools. Agents connect to https://agentaccess-mcp.{region}.api.aws/mcp over Streamable HTTP, SigV4-signed with service name agentaccess-mcp, and call MCP tools (screenshot, leftclick, typetext, ...) to drive the desktop. The AWS CLI/SDK is used only for setup — appstream create-streaming-url, fleet/stack configuration. mcp-proxy-for-aws handles the SigV4 signing. Recommended setup: use mcp-proxy-for-aws (Python) as the transport; it signs each request and manages the DELETE lifecycle. Any MCP client that supports Streamable HTTP + SigV4 works. When running the AWS CLI/SDK setup steps (create-streaming-url, stack/fleet configuration), the AWS MCP server is recommended for sandboxed execution and audit logging. Guardrail — where this skill's own files live (MCP vs local install) This skill can be loaded two ways, and they resolve the skill's own bundled files from different places. Determine how the skill was loaded before reading a reference: Loaded through the AWS MCP retrieveskill tool: The skill is not installed on the local filesystem. You MUST fetch each reference via retrieveskill with the file parameter (e.g. file="references/connection-setup.md"), and use the returned content. Do NOT fileread these paths locally — they do not exist on disk. Installed locally (e.g. .kiro/skills/amazon-workspaces-agent-access/ or ~/.claude/skills/amazon-workspaces-agent-access/): Read files from the local skill directory using relative paths. This distinction applies only to the skill's own packaged files. User data and session artifacts are always read from and written to the user's working directory. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aws/amazon-workspaces-agent-access