Workbench

Develop and iterate on code inside a persistent Docker container with no host access.

Usage

/workbench build a python script that reads a CSV and outputs summary stats /workbench write a script that fetches data from an API and processes it /workbench create an express server prototype /workbench # Uses recent conversation context

Security Model

Why Docker: Running python script.py on the host gives the script full access to the user's filesystem, even if the script file lives in /tmp . Docker containers isolate execution so the host filesystem doesn't exist inside the container.

Permission strategy:

• docker exec cc-workbench and docker exec cc-workbench-* are whitelisted (runs inside existing containers - safe)

• docker stop/rm/start cc-workbench and cc-workbench-* are whitelisted

• docker info is whitelisted (read-only check)

• docker build and docker run are NOT whitelisted - the user approves these once

The user can verify the docker run command has:

• Only the workspace directory mounted (-v /tmp/workbench:/workspace )

• No --privileged flag

Remaining risks:

• Container escape CVEs exist (e.g. CVE-2024-21626). Keep Docker updated.

• Output files from the workbench should be reviewed before production use.

Workflow

Phase 1: Setup

1. Check Docker is available:

docker info >/dev/null 2>&1

If Docker is not running, tell the user and stop.

2. Check if the workbench container already exists:

docker ps -a --filter "name=^cc-workbench$" --format "{{.Names}} {{.Status}}"

Three cases:

• Container is running → skip to step 4

• Container exists but is stopped → restart it: docker start cc-workbench

• Container doesn't exist → continue to step 3

3. First-time setup (one-time, requires user approval):

Check if the cc-workbench image exists:

docker images cc-workbench --format "{{.Repository}}"

If no image, find the Dockerfile and build it (user approves once):

Use Glob tool to find: **/claude/skills/workbench/Dockerfile

Then build from that directory:

docker build -t cc-workbench /absolute/path/to/claude/skills/workbench/

Create the workspace directory and start the container (user approves once):

mkdir -p /tmp/workbench docker run -d --name cc-workbench
--memory 512m --cpus 1
-v /tmp/workbench:/workspace
-w /workspace
cc-workbench
tail -f /dev/null

After this, the container is running and all subsequent operations are auto-approved.

4. Create a task directory inside the container:

docker exec cc-workbench mkdir -p /workspace/{task-name}

Use a short descriptive name for the task (e.g., csv-parser , api-client , etl-pipeline ).

Phase 2: Iterative Development (auto-approved)

This is the main loop. All docker exec commands are whitelisted and auto-approve.

1. Write code to the task directory using the Write tool:

Write tool -> /tmp/workbench/{task-name}/script.py

2. Detect language and execute inside the container:

Language Run command

Python docker exec cc-workbench python3 /workspace/{task-name}/script.py

Node.js docker exec cc-workbench node /workspace/{task-name}/script.js

Go docker exec -w /workspace/{task-name} cc-workbench go run main.go

Bash docker exec cc-workbench bash /workspace/{task-name}/script.sh

Auto-detect the language from the task description and file extensions. No flag needed — the container has all runtimes.

3. Read output, fix issues, iterate. Repeat steps 1-3 until the script works correctly.

Guidelines for iteration:

• Write files to /tmp/workbench/{task-name}/ using the Write tool (this writes to the host path which is mounted into the container)

• Execute via docker exec (runs inside the container)

• Read output files from /tmp/workbench/{task-name}/ if the script produces file output

• Install packages if needed: docker exec cc-workbench pip install pandas

• Use docker exec for all filesystem operations inside the container (e.g., docker exec cc-workbench mkdir -p /workspace/{task-name}/subdir ). Do NOT use host commands like mkdir or chmod on the workspace — only docker exec is whitelisted and auto-approved. Host commands will trigger a permission prompt.

Phase 3: Graduation

When the script is working:

1. Present the finished script to the user with a summary of what it does.

2. Ask where to copy it in the project (or if the user wants it left in the workspace).

3. Copy if requested:

cp /tmp/workbench/{task-name}/script.py /path/to/project/script.py

4. Keep the container running for future tasks. Only clean up the task directory if asked:

docker exec cc-workbench rm -rf /workspace/{task-name}

Custom Environments

If a task needs services not in the default image (e.g., PostgreSQL, Redis, Elasticsearch), create a task-specific container instead:

docker run -d --name cc-workbench-{task-name}
--memory 512m --cpus 1
-v /tmp/workbench/{task-name}:/workspace
-w /workspace
postgres:16
...

This falls back to the per-task flow — the user approves the docker run once. The whitelisted docker exec cc-workbench-* pattern covers these named containers too.

Full Cleanup

Only when the user explicitly requests it:

docker stop cc-workbench && docker rm cc-workbench rm -rf /tmp/workbench

Examples

Prototype a CSV parser in Docker isolation:

/workbench build a python script that reads a CSV and outputs summary stats

Sets up (or reuses) the cc-workbench Docker container, creates a csv-parser task directory, and iterates on a Python script inside the container. Installs pandas if needed, runs against sample data, and presents the working script for graduation to the project.

Test untrusted npm package safely:

/workbench try out the new csv-parse package and see if it handles edge cases

Creates a Node.js script inside the container that imports and exercises the package with various edge-case inputs. All execution stays sandboxed -- nothing touches the host filesystem or project dependencies.

Troubleshooting

Docker daemon not running

Solution: Start Docker Desktop (or the Docker daemon via sudo systemctl start docker on Linux) and wait for it to be ready. Run docker info to confirm it is accepting commands before retrying the workbench setup.

Container runs out of memory or disk

Solution: Stop the container with docker stop cc-workbench && docker rm cc-workbench , then recreate it with higher limits (e.g., --memory 1g ). For disk issues, prune unused images and containers with docker system prune to free space, or clear the /tmp/workbench directory of old task folders.

Notes

• The container runs as root inside its own namespace - this is fine because it has no host access

• Files written to /tmp/workbench/ on the host appear at /workspace inside the container

• Installed packages persist across tasks (the container stays running)

• If a task needs a clean environment, use python3 -m venv or create a custom container

• For long-running processes (servers), use docker exec -d for background execution and docker exec ... curl localhost:PORT to test