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Agent Commands

NikCLI provides powerful AI agent management capabilities for autonomous task execution. These commands allow you to list, configure, and execute tasks with specialized AI agents.

Core Agent Commands

/agents

List all available AI agents with their capabilities and status. Syntax:
Output includes:
  • Agent name and type
  • Specialization areas
  • Current status (available/busy)
  • Performance metrics
  • Last used timestamp
Example:
Sample Output:

/agent [name] [task]

Execute a specific task using a named agent. Syntax:
Parameters:
  • agent-name - Name of the agent to use
  • task-description - Detailed description of the task
Available Agents:
  • backend-expert - Backend development, APIs, databases
  • frontend-expert - Frontend development, React, UI/UX
  • devops-expert - DevOps, CI/CD, containerization
  • code-review - Code review and quality analysis
  • system-admin - System administration and configuration
  • universal-agent - General-purpose agent
  • autonomous-coder - Fully autonomous coding
Examples:

/auto [task]

Execute a task with full autonomous mode - the system selects the best agent and approach. Syntax:
Features:
  • Automatic agent selection
  • Multi-step task breakdown
  • Progress monitoring
  • Error recovery
  • Resource optimization
Examples:

/parallel [tasks]

Execute multiple tasks in parallel using different agents. Syntax:
Features:
  • Concurrent execution
  • Resource management
  • Progress tracking
  • Dependency handling
  • Result aggregation
Examples:

Agent Factory Commands

/factory

Show the agent factory status and available agent templates. Syntax:
Output includes:
  • Available agent templates
  • Custom agent definitions
  • Factory configuration
  • Resource usage
  • Creation history

/create-agent [specification]

Create a custom agent with specific capabilities. Syntax:
Agent Specifications:
  • frontend - Frontend-focused agent
  • backend - Backend-focused agent
  • fullstack - Full-stack development agent
  • mobile - Mobile development agent
  • data - Data analysis and ML agent
  • security - Security-focused agent
  • custom - Interactive custom agent creation
Examples:
Interactive Creation Process:
  1. Agent name and description
  2. Specialization areas
  3. Tool access permissions
  4. Resource limits
  5. Behavioral parameters

/launch-agent [name]

Launch a previously created custom agent. Syntax:
Examples:

Advanced Agent Features

Agent Streaming

Real-time output streaming from agent execution:

Agent Context

Agents maintain context across interactions:

Agent Memory

Agents can remember information across sessions:

Agent Configuration

Model Selection

Configure which AI model agents use:

Agent Permissions

Configure what agents can access:

Resource Limits

Set limits on agent resource usage:

Agent Workflows

Development Workflow

Code Review Workflow

Multi-Agent Collaboration

Agent Troubleshooting

Common Issues

Agent not responding:
Poor agent performance:
Agent errors:

Debug Commands

Best Practices

Task Description

Write clear, specific task descriptions:

Agent Selection

Choose the right agent for the task:

Resource Management

Monitor and manage agent resource usage:

Error Handling

Handle agent errors gracefully:

Integration Examples

CI/CD Integration

Development Environment

Code Maintenance