cyberian-control

name	cyberian-control
description	Control and coordinate multiple Claude Code sessions via cyberian's agentapi wrapper. Use this proactively when tasks involve multi-agent orchestration, delegating work to other agents, or managing agent farms.

Cyberian Control

Overview

This skill enables a Claude Code session to control and coordinate other Claude Code (or other AI agent) sessions via the cyberian CLI tool, which wraps agentapi.

Use Cases:

• Orchestrating multiple agents working on different aspects of a problem
• Delegating specialized tasks to dedicated agent instances
• Managing farms of agents for parallel processing
• Running complex multi-step workflows across multiple agents
• Monitoring and retrieving results from remote agent sessions

When to Use This Skill

This skill should be invoked when:

• The user asks to control multiple Claude Code sessions
• Tasks involve delegating work to other agents
• You need to send messages to or monitor remote agents
• Managing agent server farms or workflows
• Coordinating parallel agent work

Installation

The cyberian CLI must be installed and available:

# Install from PyPI
pip install cyberian

# Or use uvx for one-off commands
uvx cyberian --help

# For workflow/provider features
pip install cyberian[providers]

Core Commands

Sending Messages to Agents

Send a message to a remote agent and optionally wait for response:

# Fire-and-forget message
cyberian message "Write a hello world function in Python" -H localhost -P 3284

# Synchronous - wait for agent to complete
cyberian message "What is 2+2?" --sync -H localhost -P 3285

# With timeout
cyberian message "Complex task here" --sync --timeout 300 -H localhost -P 3286

Options:

• --host, -H - Agent API host (default: localhost)
• --port, -P - Agent API port (default: 3284)
• --sync, -s - Wait for agent response
• --timeout, -T - Timeout in seconds for sync mode (default: 60)
• --type, -t - Message type (default: "user")

Checking Agent Status

Check if an agent is running and get its status:

cyberian status -H localhost -P 3284

Retrieving Messages

Get conversation history from an agent:

# Get all messages as JSON
cyberian messages -H localhost -P 3284

# Get last 5 messages in YAML
cyberian messages -f yaml -l 5 -H localhost -P 3285

# Export to CSV
cyberian messages -f csv -H localhost -P 3286 > conversation.csv

Options:

• --format, -f - Output format: json, yaml, or csv
• --last, -l - Get only last N messages

Managing Servers

Start a new agent server:

# Start Claude agent on port 3284
cyberian server claude -p 3284 -d /tmp/workdir --skip-permissions

# Start aider agent
cyberian server aider -p 3285 -d /path/to/project

Options:

• agent - Agent type (aider, claude, cursor, goose, custom)
• --port, -p - Port number (default: 3284)
• --dir, -d - Working directory
• --skip-permissions, -s - Skip permission checks

List running servers:

cyberian list-servers

Stop a server:

# By PID
cyberian stop 12345

# By port
cyberian stop -p 3284

Managing Agent Farms

Start multiple agents from a config file:

# farm.yaml
base_port: 4000
servers:
  - name: researcher
    agent_type: claude
    directory: /tmp/researcher
    skip_permissions: true
    template_directory: .config  # Copy config to each server

  - name: coder
    agent_type: claude
    directory: /tmp/coder
    port: 5000
    skip_permissions: true

cyberian farm start farm.yaml

Farm Features:

• Auto-assign ports starting from base_port
• Copy template directories (like .claude/CLAUDE.md) to each server
• Manage multiple specialized agents

Running Workflows

Execute complex multi-step workflows defined in YAML:

# Basic workflow
cyberian run workflow.yaml -p query="quantum computing" -d ./output

# With specific agent type
cyberian run workflow.yaml -a claude -p topic="AI safety"

# With timeout and custom server
cyberian run workflow.yaml -H example.com -P 8080 -T 600

Workflow Options:

• --param, -p - Pass parameters (key=value)
• --dir, -d - Working directory
• --agent-type, -a - Agent type to use
• --timeout, -T - Timeout per task (default: 300s)
• --agent-lifecycle - reuse (keep server) or refresh (restart between tasks)

Workflow System

Basic Workflow Structure

name: simple-task
description: A simple research task

params:
  query:
    range: string
    required: true

subtasks:
  research:
    instructions: |
      Research {{query}} and write a summary.
      COMPLETION_STATUS: COMPLETE

Workflow with Subtasks

name: complex-workflow
description: Multi-step research

params:
  query:
    range: string
    required: true

subtasks:
  initial_search:
    instructions: |
      Perform initial research on {{query}}.
      Write a research plan in PLAN.md.
      COMPLETION_STATUS: COMPLETE

  deep_dive:
    instructions: |
      Read PLAN.md and do deep dive into {{query}}.
      Write detailed findings in FINDINGS.md.
      COMPLETION_STATUS: COMPLETE

  summary:
    instructions: |
      Read FINDINGS.md and create final summary in SUMMARY.md.
      COMPLETION_STATUS: COMPLETE

Looping Tasks

Tasks can loop until a condition is met:

subtasks:
  iterate:
    instructions: |
      Keep researching {{query}}. Find new angles.
      When exhausted, yield: NO_MORE_RESEARCH

    loop_until:
      status: NO_MORE_RESEARCH
      message: |
        If all research avenues are exhausted,
        yield status: NO_MORE_RESEARCH

Provider Calls

Call external providers directly (requires pip install cyberian[providers]):

subtasks:
  research:
    provider_call:
      provider: deep-research-client
      method: research
      params:
        query: "{{query}}"
        provider: openai
        model: o3-mini
        use_cache: true
      output_file: "{{workdir}}/research.md"

  analyze:
    instructions: |
      Read {{workdir}}/research.md and create analysis.
      COMPLETION_STATUS: COMPLETE

Common Patterns

Pattern 1: Delegate Task to Remote Agent

# Start a remote agent for specialized work
cyberian server claude -p 4000 -d /tmp/specialist

# Send it a task
cyberian message "Implement a binary search tree in Rust" \
  --sync -P 4000 --timeout 300

# Get the results
cyberian messages -f yaml -l 10 -P 4000

Pattern 2: Parallel Research with Farm

# research-farm.yaml
base_port: 5000
servers:
  - name: quantum
    agent_type: claude
    directory: /tmp/quantum-research
    skip_permissions: true

  - name: classical
    agent_type: claude
    directory: /tmp/classical-research
    skip_permissions: true

# Start farm
cyberian farm start research-farm.yaml

# Send different tasks to each
cyberian message "Research quantum algorithms" -P 5000 &
cyberian message "Research classical algorithms" -P 5001 &

# Monitor status
cyberian status -P 5000
cyberian status -P 5001

Pattern 3: Multi-Step Workflow

# deep-research.yaml
name: deep-research
description: Comprehensive research workflow

params:
  query:
    range: string
    required: true

subtasks:
  initial:
    instructions: |
      Research {{query}}. Write initial findings.
      COMPLETION_STATUS: COMPLETE

  expand:
    instructions: |
      Read initial findings. Research deeper.
      COMPLETION_STATUS: COMPLETE

  synthesize:
    instructions: |
      Create final comprehensive report.
      COMPLETION_STATUS: COMPLETE

cyberian run deep-research.yaml \
  -p query="CRISPR applications" \
  -d ./research-output \
  -a claude

Pattern 4: Hybrid Provider + Agent

# hybrid-workflow.yaml
name: hybrid-research
description: Use provider for data, agent for synthesis

params:
  query:
    range: string
    required: true

subtasks:
  gather:
    provider_call:
      provider: deep-research-client
      method: research
      params:
        query: "{{query}}"
      output_file: "raw_data.md"

  analyze:
    instructions: |
      Read raw_data.md and create structured report.
      COMPLETION_STATUS: COMPLETE

Best Practices

1. Always specify ports when working with multiple agents to avoid conflicts
2. Use --sync mode when you need to wait for agent completion
3. Set appropriate timeouts for complex tasks (use -T flag)
4. Use farm template_directory to share configuration across agents
5. Monitor agent status before sending new messages
6. Retrieve conversation history to check agent progress
7. Use workflows for complex multi-step tasks
8. Use provider calls for deterministic operations (research, data retrieval)
9. Use agent_lifecycle: refresh when tasks need isolated state

Troubleshooting

Check if Agent is Running

cyberian status -P 3284
cyberian list-servers

Agent Not Responding

# Check status
cyberian status -P 3284

# Restart server
cyberian stop -p 3284
cyberian server claude -p 3284 -d /tmp/workdir

View Agent Conversation

# See what the agent is doing
cyberian messages -f yaml -l 20 -P 3284

Workflow Not Completing

Ensure tasks include completion status:

instructions: |
  Do the work here.
  COMPLETION_STATUS: COMPLETE  # Required!

Examples

See the tests/examples/ directory for workflow examples:

• deep-research.yaml - Iterative research workflow
• simple-provider-research.yaml - Basic provider call
• deep-research-with-provider.yaml - Hybrid workflow

Command Reference

All commands support --help for detailed options:

cyberian --help
cyberian message --help
cyberian server --help
cyberian farm --help
cyberian run --help

Integration with Claude Code

This skill is designed to work seamlessly within Claude Code sessions. When you invoke this skill, Claude will use cyberian commands via the Bash tool to control remote agents. The skill provides the knowledge and patterns for effective multi-agent orchestration.