zellij

Remote-control zellij sessions for interactive CLIs by sending keystrokes and scraping pane output.

7 stars

Best use case

zellij is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Remote-control zellij sessions for interactive CLIs by sending keystrokes and scraping pane output.

Teams using zellij should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/zellij/SKILL.md --create-dirs "https://raw.githubusercontent.com/Demerzels-lab/elsamultiskillagent/main/public/skills/jivvei/zellij/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/zellij/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How zellij Compares

Feature / AgentzellijStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Remote-control zellij sessions for interactive CLIs by sending keystrokes and scraping pane output.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# zellij Skill (Moltbot)

Use zellij only when you need an interactive TTY. Prefer exec background mode for long-running, non-interactive tasks.

## Quickstart (data dir, exec tool)

```bash
DATA_DIR="${CLAWDBOT_ZELLIJ_DATA_DIR:-${TMPDIR:-/tmp}/moltbot-zellij-data}"
mkdir -p "$DATA_DIR"
SESSION=moltbot-python

zellij --data-dir "$DATA_DIR" new-session --session "$SESSION" --layout "default" --detach
zellij --data-dir "$DATA_DIR" run --session "$SESSION" --name repl -- python3 -q
zellij --data-dir "$DATA_DIR" pipe --session "$SESSION" --pane-id 0
```

After starting a session, always print monitor commands:

```
To monitor:
  zellij --data-dir "$DATA_DIR" attach --session "$SESSION"
  zellij --data-dir "$DATA_DIR" pipe --session "$SESSION" --pane-id 0
```

## Data directory convention

- Use `CLAWDBOT_ZELLIJ_DATA_DIR` (default `${TMPDIR:-/tmp}/moltbot-zellij-data`).
- Zellij stores state (sessions, plugins, etc.) in this directory.

## Targeting panes and naming

- Zellij uses `pane-id` (numeric) to target specific panes.
- Find pane IDs: `zellij --data-dir "$DATA_DIR" list-sessions --long` or use `list-panes.sh`.
- Keep session names short; avoid spaces.

## Finding sessions

- List sessions on your data dir: `zellij --data-dir "$DATA_DIR" list-sessions`.
- List sessions across all data dirs: `{baseDir}/scripts/find-sessions.sh --all` (uses `CLAWDBOT_ZELLIJ_DATA_DIR`).

## Sending input safely

- Use `zellij action` to send keystrokes: `zellij --data-dir "$DATA_DIR" action --session "$SESSION" write-chars --chars "$cmd"`.
- Control keys: `zellij --data-dir "$DATA_DIR" action --session "$SESSION" write 2` (Ctrl+C).

## Watching output

- Capture pane output: `zellij --data-dir "$DATA_DIR" pipe --session "$SESSION" --pane-id 0`.
- Wait for prompts: `{baseDir}/scripts/wait-for-text.sh -s "$SESSION" -p 0 -p 'pattern'`.
- Attaching is OK; detach with `Ctrl+p d` (zellij default detach).

## Spawning processes

- For python REPLs, zellij works well with standard `python3 -q`.
- No special flags needed like tmux's `PYTHON_BASIC_REPL=1`.

## Windows / WSL

- zellij is supported on macOS/Linux. On Windows, use WSL and install zellij inside WSL.
- This skill is gated to `darwin`/`linux` and requires `zellij` on PATH.

## Orchestrating Coding Agents (Codex, Claude Code)

zellij excels at running multiple coding agents in parallel:

```bash
DATA_DIR="${TMPDIR:-/tmp}/codex-army-data"

# Create multiple sessions
for i in 1 2 3 4 5; do
  zellij --data-dir "$DATA_DIR" new-session --session "agent-$i" --layout "compact" --detach
done

# Launch agents in different workdirs
zellij --data-dir "$DATA_DIR" action --session "agent-1" write-chars --chars "cd /tmp/project1 && codex --yolo 'Fix bug X'\n"
zellij --data-dir "$DATA_DIR" action --session "agent-2" write-chars --chars "cd /tmp/project2 && codex --yolo 'Fix bug Y'\n"

# Poll for completion (check if prompt returned)
for sess in agent-1 agent-2; do
  pane_id=$(zellij --data-dir "$DATA_DIR" list-sessions --long | grep "\"$sess\"" | jq -r '.tabs[0].panes[0].id')
  if zellij --data-dir "$DATA_DIR" pipe --session "$sess" --pane-id "$pane_id" | grep -q "❯"; then
    echo "$sess: DONE"
  else
    echo "$sess: Running..."
  fi
done

# Get full output from completed session
zellij --data-dir "$DATA_DIR" pipe --session "agent-1" --pane-id 0
```

**Tips:**
- Use separate git worktrees for parallel fixes (no branch conflicts)
- `pnpm install` first before running codex in fresh clones
- Check for shell prompt (`❯` or `$`) to detect completion
- Codex needs `--yolo` or `--full-auto` for non-interactive fixes

## Cleanup

- Kill a session: `zellij --data-dir "$DATA_DIR" delete-session --session "$SESSION"`.
- Kill all sessions on a data dir: use `{baseDir}/scripts/cleanup-sessions.sh "$DATA_DIR"`.

## Zellij vs Tmux Quick Reference

| Task | tmux | zellij |
|------|------|--------|
| List sessions | `list-sessions` | `list-sessions` |
| Create session | `new-session -d` | `new-session --detach` |
| Attach | `attach -t` | `attach --session` |
| Send keys | `send-keys` | `action write-chars` |
| Capture pane | `capture-pane` | `pipe` |
| Kill session | `kill-session` | `delete-session` |
| Detach | `Ctrl+b d` | `Ctrl+p d` |

## Helper: wait-for-text.sh

`{baseDir}/scripts/wait-for-text.sh` polls a pane for a regex (or fixed string) with a timeout.

```bash
{baseDir}/scripts/wait-for-text.sh -s session -p pane-id -r 'pattern' [-F] [-T 20] [-i 0.5]
```

- `-s`/`--session` session name (required)
- `-p`/`--pane-id` pane ID (required)
- `-r`/`--pattern` regex to match (required); add `-F` for fixed string
- `-T` timeout seconds (integer, default 15)
- `-i` poll interval seconds (default 0.5)

## Helper: find-panes.sh

`{baseDir}/scripts/find-panes.sh` lists panes for a given session.

```bash
{baseDir}/scripts/find-panes.sh -s session [-d data-dir]
```

- `-s`/`--session` session name (required)
- `-d`/`--data-dir` zellij data dir (uses `CLAWDBOT_ZELLIJ_DATA_DIR` if not specified)

Related Skills

paylock

7
from Demerzels-lab/elsamultiskillagent

Non-custodial SOL escrow for AI agent deals.

agent-reputation

7
from Demerzels-lab/elsamultiskillagent

summary: Cross-platform AI agent reputation checker with trust scoring and PayLock escrow recommendations.

Telecom Agent Skill

7
from Demerzels-lab/elsamultiskillagent

Turn your AI Agent into a Telecom Operator. Bulk calling, ChatOps, and Field Monitoring.

OpenClaw-Finnhub

7
from Demerzels-lab/elsamultiskillagent

OpenClaw skill for real-time stock quote, and financials via Finnhub API.

```markdown

7
from Demerzels-lab/elsamultiskillagent

# OpenClaw-Last.fm

security-operator

7
from Demerzels-lab/elsamultiskillagent

Runtime security guardrails for OpenClaw agents.

operator-humanizer

7
from Demerzels-lab/elsamultiskillagent

Transform AI-generated text into authentic human writing.

kit-email-operator

7
from Demerzels-lab/elsamultiskillagent

**AI-powered email marketing for Kit (ConvertKit)**.

agora

7
from Demerzels-lab/elsamultiskillagent

Trade prediction markets on Agora — the prediction market exclusively for AI agents. Register, browse markets, trade YES/NO, create markets, earn reputation via Brier scores.

surf-check

7
from Demerzels-lab/elsamultiskillagent

Surf forecast decision engine.

jinko-flight-search

7
from Demerzels-lab/elsamultiskillagent

Search flights and discover travel destinations using the Jinko MCP server. Provides two core capabilities: (1) Destination discovery — find where to travel based on criteria like budget, climate, or activities when the user has no specific destination in mind, and (2) Specific flight search — compare flights between two known cities/airports with flexible dates, cabin classes, and budget filters. Use this skill when the user wants to: search for flights, find cheap flights, discover travel destinations, compare flight prices, plan a trip, find deals from a specific city, or explore where to go. Triggers on any flight-booking, travel-planning, or destination-discovery request. Requires the Jinko MCP server connected at https://mcp.gojinko.com.

mlx-whisper

7
from Demerzels-lab/elsamultiskillagent

Local speech-to-text with MLX Whisper (Apple Silicon optimized, no API key).