SLB - Simultaneous Launch Button

A CLI that implements a two-person rule for running potentially destructive commands from AI coding agents. When an agent wants to run something risky, SLB requires peer review and approval before execution.

Why Use SLB

Coding agents can hallucinate or misunderstand context. A second reviewer catches mistakes before they become irreversible. Essential for multi-agent workflows where a bad command could destroy work.

Risk Tiers

Tier	Approvals	Auto-approve	Examples
CRITICAL	2+	Never	`rm -rf /`, `DROP DATABASE`, `terraform destroy`
DANGEROUS	1	Never	`rm -rf ./build`, `git reset --hard`, `DROP TABLE`
CAUTION	0	After 30s	`rm file.txt`, `git branch -d`
SAFE	0	Immediately	`rm *.log`, `git stash`

Quick Start

Initialize in a Project

bash

cd /path/to/project
slb init

Creates .slb/ directory with:

state.db - SQLite database
config.toml - Configuration
pending/ - Pending request files

Start a Session

bash

slb session start --agent "GreenLake" --program "claude-code" --model "opus"
# Returns session_id and session_key

Run a Dangerous Command

bash

# Blocks until approved
slb run "rm -rf ./build" --reason "Clean build artifacts" --session-id <id>

Review and Approve

bash

# See pending requests
slb pending

# View request details
slb review <request-id>

# Approve the request
slb approve <request-id> --session-id <reviewer-id> --comment "Looks safe"

# Or reject
slb reject <request-id> --session-id <reviewer-id> --reason "Too risky"

Commands

Session Management

bash

# Start a session
slb session start --agent "AgentName" --program "codex" --model "gpt-5"

# List sessions
slb session list

# End a session
slb session end <session-id>

Running Commands

bash

# Run with reason
slb run "git push --force" --reason "Force push after rebase" --session-id <id>

# Run with timeout override
slb run "terraform destroy" --reason "Tear down test env" --timeout 300s

# Dry run (classify only, don't execute)
slb run "rm -rf /tmp/test" --dry-run

Reviewing

bash

# List pending requests
slb pending

# List all requests
slb requests

# View specific request
slb review <request-id>

# Approve
slb approve <request-id> --session-id <id>

# Reject
slb reject <request-id> --session-id <id> --reason "Reason"

TUI Dashboard

bash

# Interactive review dashboard
slb tui

# Features:
# - See all pending requests
# - Review details
# - Approve/reject inline

Configuration

.slb/config.toml:

toml

[risk]
# Override risk levels for specific patterns
[[risk.overrides]]
pattern = "rm -rf ./node_modules"
tier = "safe"
reason = "Node modules are regenerated"

[timeouts]
caution_auto_approve = "30s"
request_expiry = "1h"

[notifications]
agent_mail = true

Integration with Agent Mail

SLB can notify reviewers via MCP Agent Mail:

bash

slb init --with-agent-mail

# Reviewers get inbox messages for pending requests

Audit Trail

All requests, reviews, and executions are logged:

bash

# View audit log
slb audit

# Export audit log
slb audit --export audit.json

Status and Health

bash

# Check SLB status
slb status

# Check system health
slb health

Example Multi-Agent Workflow

bash

# Agent 1 wants to run dangerous command
slb run "git push origin main --force" \
  --reason "Rebase complete, need to force push" \
  --session-id agent1-session

# Agent 2 reviews
slb pending
slb review REQ-123
slb approve REQ-123 --session-id agent2-session --comment "Verified rebase is clean"

# Original command executes automatically

Command Classification

SLB automatically classifies commands based on:

Command patterns (rm -rf, DROP, destroy, etc.)
Arguments and flags
Working directory context
Historical data

Override with explicit tier:

bash

slb run "custom-script.sh" --tier dangerous --reason "Modifies prod data"

Search AI Tools

slb

Install this agent skill to your Project

SKILL.md

SLB - Simultaneous Launch Button

Why Use SLB

Risk Tiers

Quick Start

Initialize in a Project

Start a Session

Run a Dangerous Command

Review and Approve

Commands

Session Management

Running Commands

Reviewing

TUI Dashboard

Configuration

Integration with Agent Mail

Audit Trail

Status and Health

Example Multi-Agent Workflow

Command Classification