sk8 0.5.0

MCP server exposing a remote Claude Code agent over HTTP

sk8 🛹

A minimal MCP server that exposes a remote Claude Code agent over HTTP. Claude Code on your laptop can call its one tool, run_task, to delegate a complete, self-contained task to a Claude Code instance running on this machine.

The tool runs claude headless here and returns the final text answer. It is synchronous and blocking — no queue, no streaming, no status polling.

server_sdk.py — drives the same agent loop through the Claude Agent SDK (claude-agent-sdk), giving a typed async message stream and structured permission control. This is what the container image runs, because ClaudeAgentOptions applies per-agent profile customization (tools, MCP, system prompt) natively.

Agents can be customized per profile — extra Python packages, bundled Claude Code skills, and a tool/MCP/system-prompt spec baked into the image at build time.

GCP project setup (one-time)

The sk8 CLI drives gcloud to provision agents on Cloud Run, so a GCP project has to be prepared once before sk8 create will work. Run these once per project (not per agent):

# 1. Install the gcloud SDK, then authenticate.
gcloud auth login

# 2. Select the project sk8 should deploy into (must have billing enabled).
gcloud config set project YOUR_PROJECT_ID

# 3. Enable the APIs sk8 uses (Cloud Run, Secret Manager, Artifact Registry, Cloud Build).
gcloud services enable \
  run.googleapis.com \
  secretmanager.googleapis.com \
  artifactregistry.googleapis.com \
  cloudbuild.googleapis.com

# 4. Create the Docker repo sk8 pushes the agent image to.
#    The name "agents" and location must match sk8's defaults
#    (--repo agents, --region us-central1); override both flags if you change them.
gcloud artifacts repositories create agents \
  --repository-format=docker --location=us-central1

# 5. Store the shared Claude credential the agents run under, as the
#    "anthropic-api-key" secret (sk8 mounts it into every agent).
printf '%s' "$ANTHROPIC_API_KEY" | \
  gcloud secrets create anthropic-api-key --data-file=-

With that in place, sk8 create <id> --build builds the image (first time) and deploys the agent. You can preview every gcloud command without running it via sk8 create <id> --dry-run.

🛹 CLI

sk8 is the command-line tool for managing agents — scriptable for both humans and agents alike. It exposes the full agent lifecycle and emits JSON so a running agent can spawn and register sub-agents. Install it as a console script to run sk8 <cmd> from anywhere, or run it in place with python sk8.py <cmd>:

uv tool install .                  # install the `sk8` command from this repo
uv tool install --reinstall .      # reinstall after pulling/editing the code
uv tool uninstall sk8              # remove it
# (or use pipx/pip: `pipx install .` / `pipx reinstall sk8`)

Then drive the agent lifecycle:

sk8 create iris --build     # build image (first time) + provision
sk8 create iris             # subsequent agents reuse the image
sk8 create iris --profile ./profiles/data-analyst --build  # custom deps/skills/tools
sk8 create iris --json      # for agents: parse back {url, token, mcp_add_command}
sk8 create iris --dry-run   # preview the gcloud commands offline
sk8 list                    # list deployed agents in the region
sk8 delete iris --yes       # tear down the service + its token secret
sk8 suggest 5               # propose adjective-noun names

create prints the end-user's claude mcp add line (with --json, in the mcp_add_command field). Re-running create with an existing id rotates its token and redeploys.

Verify

claude mcp list        # sk8 should show as connected

Then in a Claude Code session on your laptop:

Use the sk8 run_task tool with prompt: "list the files in the current directory and summarize them"

The remote agent runs the task in its cwd and returns the final answer.

Cloud deployment (Cloud Run, App Runner, Fly, …)

The GCP services (Cloud Build, Artifact Registry, Secret Manager, IAM, Cloud Run) and the agent lifecycle — image build → token mint → a run_task call triggering Cloud Run:

Limitations

• Synchronous only — run_task blocks until the remote agent finishes (up to a 600s timeout). No streaming, no progress, no status to poll.
• One task at a time — there's no queue or concurrency management; fire tasks serially.
• Arbitrary code execution — claude can do anything the host user can. Treat reaching this endpoint as equivalent to a shell on the box.
• Stateless across calls — each run_task is a fresh headless claude invocation with no memory of previous tasks. Put all needed context in the prompt.