Natural language to shell commands. Just describe what you want, get the command.
Powered by local AI via Ollama — no API keys, no cloud, 100% private.
$ coderdodo start redis locally using docker and show me the logs
────────────────────────────────────────
docker run -d --name redis -p 6379:6379 redis && docker logs redis
────────────────────────────────────────
Breakdown:
-d Run container in background (detached)
--name redis Name the container "redis"
-p 6379:6379 Map host port 6379 → container port 6379
redis Docker image to use
Execute? (Y/n):
$ coderdodo copy all png files from Downloads to Desktop
────────────────────────────────────────
cp /Users/anshu/Downloads/*.png /Users/anshu/Desktop
────────────────────────────────────────
Breakdown:
cp Copy files
*.png Glob pattern for PNG files only
~/Downloads/ Source directory
~/Desktop/ Destination directory
Execute? (Y/n):
$ coderdodo run the voxship backend docker container with port 8080 exposed
────────────────────────────────────────
docker run -d --name voxship-backend -p 8080:8080 voxship/backend
────────────────────────────────────────
Breakdown:
-d Run container in background (detached)
--name voxship-backend Name the container "voxship-backend"
-p 8080:8080 Map host port 8080 → container port 8080
voxship/backend Docker image to use
Execute? (Y/n):
- Natural language input — just describe what you want in plain English
- Command breakdown — explains every flag and argument before you run it
- Confirm before execute — shows the command, you press Enter to run or
nto cancel - Context-aware — detects your OS, shell, and current directory for accurate commands
- 100% local — runs on Ollama, no API keys, no cloud, your data stays on your machine
- Tiny footprint — 1 runtime dependency (
chalk), ~25KB total - Cross-platform — works on macOS, Linux, and Windows
- Zero setup — auto-installs Ollama, auto-starts it, and auto-pulls the model on first run
npm i -g coderdodoThat's it. One command. On first run, coderdodo will automatically:
- Install Ollama if not present (via
brewon macOS,curlon Linux,wingetor direct download on Windows) - Start the Ollama server
- Download the AI model (~986MB, one-time)
Then you're ready to go — no manual setup needed on any platform.
coderdodo <describe what you want in plain English>coderdodo copy all png files from Downloads to Desktop
coderdodo find all files larger than 100MB
coderdodo compress the src folder into a tar.gz
coderdodo rename all .jpeg files to .jpg in current directorycoderdodo show git log for the last 5 commits
coderdodo stage all files and commit with message "initial commit"
coderdodo remove some files from my previous commit and commit again
coderdodo create a new branch called feature-login
coderdodo squash the last 3 commitscoderdodo start a redis container with port 6379 exposed
coderdodo show docker logs for redis
coderdodo stop and remove all running containers
coderdodo build a docker image from the Dockerfile in current directory
coderdodo run the voxship backend container with port 8080coderdodo show disk usage in human readable format
coderdodo show me all processes using port 3000
coderdodo find my public IP address
coderdodo show system memory usage
coderdodo list all open network connectionscoderdodo find all TODO comments in this project
coderdodo find all typescript files modified in the last 24 hours
coderdodo count lines of code in all javascript files
coderdodo search for "API_KEY" in all env filescoderdodo install express and typescript as dev dependency
coderdodo list all globally installed npm packages
coderdodo update all outdated pip packages
coderdodo clear npm cache You type coderdodo starts redis with docker
│
▼
┌──────────┐ ┌───────────────┐ ┌──────────┐
│ Your │───▶│ Local Ollama │───▶│ Generated │
│ Query │ │ LLM (local) │ │ Command │
└──────────┘ └───────────────┘ └──────────┘
│
▼
┌──────────────┐
│ Breakdown │
│ (flags + │
│ explanation) │
└──────────────┘
│
▼
┌──────────────┐
│ Confirm & │
│ Execute │
└──────────────┘
- You describe a task in natural language
- coderdodo sends it to your local Ollama instance with context about your OS, shell, and current directory
- The LLM generates the right shell command
- You see the command + a breakdown of every flag and argument
- Press Enter to execute, or n to cancel
The default model is qwen2.5-coder:1.5b (986MB, code-specialized). You can use any Ollama model:
# Use a different model for one command
CODERDODO_MODEL=llama3.1 coderdodo show disk usage
# Or export it for the session
export CODERDODO_MODEL=llama3.1
coderdodo show disk usage| Model | Size | Best for |
|---|---|---|
qwen2.5-coder:1.5b (default) |
986MB | Fast, great for common commands |
qwen2.5-coder:7b |
4.7GB | More accurate for complex commands |
llama3.1 |
4.7GB | Good general purpose |
llama3.3 |
43GB | Best accuracy, needs beefy hardware |
| coderdodo | Other tools | |
|---|---|---|
| Privacy | 100% local via Ollama | Most require OpenAI/cloud API keys |
| Cost | Free forever | API calls cost money |
| Speed | Fast (local inference) | Network latency + API overhead |
| Explain flags | Built-in breakdown | Most just show the command |
| Dependencies | 1 (chalk) | Often 10+ |
git clone https://github.com/AnshuMishra01/commander.git
cd commander
npm install
npm run build
node dist/cli.js list files in current directory
# Install locally for testing
npm install -g .
coderdodo show disk usageMIT