TASK-O-MATIC

TECHNICAL BULLETIN NO. 001

MAIN COMMANDS - CORE FIELD OPERATIONS

DOCUMENT ID: task-o-matic-cli-main-commands-v1

CLEARANCE: All Personnel

MANDATORY COMPLIANCE: Yes

⚠️ CRITICAL SURVIVAL NOTICE

Citizen, ignoring these main commands is like wandering into the radioactive wastes without a geiger counter. These are your primary survival tools in the post-deadline wasteland. Master them or perish in the chaos of unmanaged projects.

COMMAND ARCHITECTURE OVERVIEW

The main command structure represents the central nervous system of Task-O-Matic operations. Each command serves as a critical survival hub, coordinating different aspects of project management, AI integration, and workflow automation. The architecture follows a hierarchical pattern where main commands delegate to specialized subcommands, creating a modular and extensible system.

Core Integration Points:

Service Layer Integration: All commands interface with core services (TaskService, PRDService, WorkflowService, BenchmarkService)
AI Provider Abstraction: Unified AI operations across multiple providers (OpenRouter, Anthropic, OpenAI, Custom endpoints)
Configuration Management: Centralized config system with project-local overrides
Error Handling: Standardized error reporting with TaskOMaticErrorCodes
Streaming Support: Real-time AI output for enhanced user experience

COMPLETE COMMAND DOCUMENTATION

TASKS COMMAND

Primary Command: task-o-matic tasks

COMMAND SIGNATURE

task-o-matic tasks [subcommand] [options]

DESCRIPTION

The tasks command serves as the central hub for all task-related operations. It provides access to the complete task management lifecycle including creation, modification, execution, and analysis. This is your primary interface for managing the hierarchical task structure that keeps projects organized in the wasteland.

SUBCOMMANDS

The tasks command delegates to specialized subcommands:

create - Create new tasks with AI enhancement
list - Display all tasks with filtering options
show - Show detailed task information
update - Modify existing task properties
delete - Remove tasks from the system
status - Change task status
add-tags - Add tags to tasks
remove-tags - Remove tags from tasks
plan - Create and manage implementation plans
enhance - AI-enhance task descriptions
split - Break tasks into subtasks
document - Fetch and analyze documentation
execute - Execute tasks using external tools
execute-loop - Execute multiple tasks with retry logic
subtasks - List task subtasks
tree - Display hierarchical task tree
next - Get next task to work on

INTEGRATION EXAMPLES

# Basic task listing
task-o-matic tasks list

# Create enhanced task
task-o-matic tasks create --title "Build shelter" --ai-enhance

# Execute task with planning
task-o-matic tasks execute --id task-123 --plan --review

# Show task tree
task-o-matic tasks tree

RETURN VALUES

Success: Returns task data or operation confirmation
Error: Exits with code 1 and displays error message
Exit Codes: 0 (success), 1 (error), 2 (validation error)

WORKFLOW COMMAND

Primary Command: task-o-matic workflow

COMMAND SIGNATURE

task-o-matic workflow [options]

DESCRIPTION

The workflow command provides an interactive, step-by-step project setup and management experience. It guides citizens through the complete project lifecycle from initialization to task generation and splitting. This is your all-in-one survival kit for establishing new project bases in the wasteland.

COMPREHENSIVE OPTIONS

#### AI Configuration Options

--stream                    # Show streaming AI output
--ai-provider <provider>    # AI provider override
--ai-model <model>          # AI model override  
--ai-key <key>              # AI API key override
--ai-provider-url <url>     # AI provider URL override

#### Global Workflow Control

--skip-all                  # Skip all optional steps (use defaults)
--auto-accept               # Auto-accept all AI suggestions
--config-file <path>        # Load workflow options from JSON file

#### Step 1: Initialization Options

--skip-init                 # Skip initialization step
--project-name <name>       # Project name
--init-method <method>      # Initialization method: quick, custom, ai
--project-description <desc>  # Project description for AI-assisted init
--use-existing-config       # Use existing configuration if found
--frontend <framework>      # Frontend framework
--backend <framework>       # Backend framework
--database <db>            # Database choice
--auth                     # Include authentication
--no-auth                  # Exclude authentication
--bootstrap                 # Bootstrap with Better-T-Stack
--no-bootstrap             # Skip bootstrapping
--include-docs             # Include Task-O-Matic documentation
--no-include-docs         # Skip including documentation

#### Step 2: PRD Definition Options

--skip-prd                  # Skip PRD definition
--prd-method <method>       # PRD method: upload, manual, ai, skip
--prd-file <path>          # Path to existing PRD file
--prd-description <desc>    # Product description for AI-assisted PRD
--prd-content <content>     # Direct PRD content
--prd-multi-generation      # Generate multiple PRDs and compare
--skip-prd-multi-generation # Skip PRD multi-generation
--prd-multi-generation-models <models>  # Comma-separated model list
--prd-combine              # Combine generated PRDs into master PRD
--skip-prd-combine         # Skip PRD combination
--prd-combine-model <model> # Model to use for combining PRDs

#### Step 2.5: PRD Question/Refine Options

--skip-prd-question-refine  # Skip PRD question/refine step
--prd-question-refine       # Use question-based PRD refinement
--prd-answer-mode <mode>    # Who answers questions: user, ai
--prd-answer-ai-provider <provider>  # AI provider for answering
--prd-answer-ai-model <model>       # AI model for answering
--prd-answer-ai-reasoning  # Enable reasoning for AI answering model

#### Step 3: PRD Refinement Options

--skip-refine              # Skip PRD refinement
--refine-method <method>   # Refinement method: manual, ai, skip
--refine-feedback <feedback> # Feedback for AI refinement

#### Step 4: Task Generation Options

--skip-generate            # Skip task generation
--generate-method <method>  # Generation method: standard, ai
--generate-instructions <instructions>  # Custom task generation instructions

#### Step 5: Task Splitting Options

--skip-split               # Skip task splitting
--split-tasks <ids>       # Comma-separated task IDs to split
--split-all               # Split all tasks
--split-method <method>    # Split method: interactive, standard, custom
--split-instructions <instructions>  # Custom split instructions

WORKFLOW EXECUTION EXAMPLES

#### Basic Workflow

# Interactive workflow with all steps
task-o-matic workflow

# Quick workflow with defaults
task-o-matic workflow --skip-all --auto-accept

# AI-assisted project setup
task-o-matic workflow \
  --project-name "Wasteland Shelter" \
  --init-method ai \
  --project-description "A radiation-proof shelter management system" \
  --prd-method ai \
  --prd-description "Build a comprehensive shelter management system"

#### Advanced Workflow Configuration

# Custom workflow with specific AI models
task-o-matic workflow \
  --ai-provider anthropic \
  --ai-model claude-3.5-sonnet \
  --project-name "Survival Kit" \
  --init-method custom \
  --frontend next \
  --backend hono \
  --database postgres \
  --auth \
  --prd-method upload \
  --prd-file ./requirements.md \
  --prd-multi-generation \
  --prd-multi-generation-models "anthropic:claude-3.5-sonnet,openai:gpt-4,google:gemini-pro" \
  --prd-combine-model anthropic:claude-3.5-sonnet

#### Workflow with Configuration File

# Save workflow configuration to JSON file
cat > workflow-config.json << EOF
{
  "projectName": "Wasteland Communications",
  "initMethod": "ai",
  "projectDescription": "Emergency communication system",
  "prdMethod": "ai",
  "prdDescription": "Build a resilient communication network",
  "skipRefine": false,
  "generateMethod": "ai",
  "splitAll": true,
  "aiProvider": "anthropic",
  "aiModel": "claude-3.5-sonnet"
}
EOF

# Run workflow with configuration
task-o-matic workflow --config-file workflow-config.json

WORKFLOW STEPS BREAKDOWN

#### Step 1: Project Initialization

Purpose: Set up project structure and basic configuration
Methods:

- quick: Use recommended defaults

- custom: Manually select stack components

- ai: AI-assisted configuration based on description

Output: Project directory with configuration files

#### Step 2: PRD Definition

Purpose: Create or import Product Requirements Document
Methods:

- upload: Import existing PRD file

- manual: Open editor for manual PRD creation

- ai: AI-generated PRD from description

- skip: Use existing PRD

Features: Multi-model generation and comparison

#### Step 2.5: PRD Question/Refine

Purpose: Clarify PRD requirements through Q&A
Answer Modes:

- user: Interactive user answers

- ai: AI answers based on project context

Integration: Automatically refines PRD with answers

#### Step 3: PRD Refinement

Purpose: Improve PRD quality based on feedback
Methods:

- manual: Direct editing in editor

- ai: AI-assisted refinement with feedback

- skip: Skip refinement step

#### Step 4: Task Generation

Purpose: Convert PRD into actionable tasks
Methods:

- standard: Rule-based parsing

- ai: AI-powered task generation with custom instructions

#### Step 5: Task Splitting

Purpose: Break complex tasks into manageable subtasks
Methods:

- interactive: Ask for each task individually

- standard: Apply standard AI splitting to all

- custom: Use custom instructions for all tasks

ERROR CONDITIONS AND EXCEPTIONS

#### Common Workflow Errors

# Configuration file not found
Error: Config file not found: workflow-config.json
Solution: Ensure file exists and is readable

# Invalid model format
Error: Invalid model format: invalid-model. Expected provider:model
Solution: Use format "anthropic:claude-3.5-sonnet"

# Missing required options
Error: Project name is required for AI initialization
Solution: Provide --project-name or use interactive mode

# Bootstrap failure
Error: Bootstrap process failed
Solution: Check better-t-stack-cli installation and network connectivity

#### Recovery Procedures

Configuration Recovery: Use --config-file with backup configuration
Step Skipping: Use specific --skip-* flags to bypass problematic steps
AI Provider Fallback: Override with --ai-provider and --ai-model
Partial Recovery: Continue from failed step using workflow subcommands

TECHNICAL SPECIFICATIONS

#### Performance Characteristics

Initialization Time: 2-5 minutes (depends on bootstrap complexity)
PRD Generation: 30-120 seconds per model
Task Generation: 10-60 seconds
Task Splitting: 15-90 seconds per task
Memory Usage: 100-500MB (depends on AI model and project size)

#### Resource Requirements

Minimum: 2GB RAM, 1GB disk space
Recommended: 4GB RAM, 5GB disk space
Network: Required for AI operations and bootstrap

#### Integration Points

Better-T-Stack CLI: Project bootstrapping
AI Services: OpenRouter, Anthropic, OpenAI, Custom endpoints
Context7 MCP: Documentation retrieval and enhancement
Git: Version control integration
File System: Local storage in .task-o-matic/ directory

PRD COMMAND

Primary Command: task-o-matic prd

COMMAND SIGNATURE

task-o-matic prd [subcommand] [options]

DESCRIPTION

The PRD command provides comprehensive Product Requirements Document management capabilities. It supports creation, parsing, refinement, and analysis of PRDs using AI assistance. This is your strategic planning tool for defining project requirements before diving into implementation.

SUBCOMMANDS AND COMPREHENSIVE OPTIONS

#### CREATE SUBCOMMAND

task-o-matic prd create <description> [options]

Required Arguments:

description: Product description for PRD generation

Options:

--ai <provider:model...>     # AI model(s) to use (can specify multiple)
--combine-ai <provider:model> # AI model to combine multiple PRDs
--output-dir <path>          # Directory to save PRDs (default: .task-o-matic/prd)
--stream                     # Enable streaming output (single AI only)

Multi-Model Generation Examples:

# Single model PRD generation
task-o-matic prd create "Emergency shelter system" \
  --ai anthropic:claude-3.5-sonnet \
  --stream

# Multi-model comparison
task-o-matic prd create "Water purification system" \
  --ai anthropic:claude-3.5-sonnet openai:gpt-4 google:gemini-pro \
  --combine-ai anthropic:claude-3.5-sonnet

# Custom output directory
task-o-matic prd create "Communication network" \
  --ai openrouter:anthropic/claude-3.5-sonnet \
  --output-dir ./requirements

#### COMBINE SUBCOMMAND

task-o-matic prd combine [options]

Required Options:

--files <paths...>           # PRD files to combine
--description <text>         # Original product description
--ai <provider:model>        # AI model to use for combining

Optional Options:

--output <path>              # Output file path (default: prd-master.md)
--stream                     # Enable streaming output

Combination Examples:

# Combine multiple PRD files
task-o-matic prd combine \
  --files prd1.md prd2.md prd3.md \
  --description "Emergency response system" \
  --ai anthropic:claude-3.5-sonnet \
  --output combined-prd.md

# Stream combination process
task-o-matic prd combine \
  --files ./drafts/*.md \
  --description "Medical supply tracking" \
  --ai openai:gpt-4 \
  --stream

#### PARSE SUBCOMMAND

task-o-matic prd parse [options]

Required Options:

--file <path>                # Path to PRD file

AI Configuration Options:

--ai-provider <provider>     # AI provider override
--ai-model <model>           # AI model override
--ai-key <key>               # AI API key override
--ai-provider-url <url>      # AI provider URL override
--ai-reasoning <tokens>      # Enable reasoning for OpenRouter models
--stream                     # Show streaming AI output
--tools                      # Enable filesystem tools for project analysis

Content Override Options:

--prompt <prompt>            # Override prompt
--message <message>          # User message

Parsing Examples:

# Basic PRD parsing
task-o-matic prd parse --file ./requirements.md

# Advanced parsing with AI enhancement
task-o-matic prd parse \
  --file ./emergency-shelter-prd.md \
  --ai-provider anthropic \
  --ai-model claude-3.5-sonnet \
  --tools \
  --stream

# Custom parsing with reasoning
task-o-matic prd parse \
  --file ./complex-requirements.md \
  --ai-provider openrouter \
  --ai-model anthropic/claude-3.5-sonnet \
  --ai-reasoning 2048 \
  --prompt "Focus on technical implementation details"

#### REWORK SUBCOMMAND

task-o-matic prd rework [options]

Required Options:

--file <path>                # Path to PRD file
--feedback <feedback>         # User feedback for improvements

Optional Options:

--output <path>              # Output file path (default: overwrite original)
--prompt <prompt>            # Override prompt
--message <message>          # User message
--ai-provider <provider>     # AI provider override
--ai-model <model>           # AI model override
--ai-key <key>               # AI API key override
--ai-provider-url <url>      # AI provider URL override
--ai-reasoning <tokens>      # Enable reasoning for OpenRouter models
--stream                     # Show streaming AI output
--tools                      # Enable filesystem tools for project analysis

Rework Examples:

# Basic PRD rework
task-o-matic prd rework \
  --file ./draft-prd.md \
  --feedback "Add more technical specifications and API details"

# Advanced rework with AI enhancement
task-o-matic prd rework \
  --file ./requirements.md \
  --feedback "Focus on security requirements and compliance" \
  --ai-provider anthropic \
  --ai-model claude-3.5-sonnet \
  --tools \
  --stream \
  --output ./improved-requirements.md

#### QUESTION SUBCOMMAND

task-o-matic prd question [options]

Required Options:

--file <path>                # Path to PRD file

Optional Options:

--output <path>              # Output JSON file path (default: prd-questions.json)
--prompt <prompt>            # Override prompt
--message <message>          # User message
--ai-provider <provider>     # AI provider override
--ai-model <model>           # AI model override
--ai-key <key>               # AI API key override
--ai-provider-url <url>      # AI provider URL override
--ai-reasoning <tokens>      # Enable reasoning for OpenRouter models
--stream                     # Show streaming AI output
--tools                      # Enable filesystem tools for project analysis

Question Generation Examples:

# Generate clarifying questions
task-o-matic prd question --file ./requirements.md

# Advanced question generation
task-o-matic prd question \
  --file ./complex-prd.md \
  --ai-provider anthropic \
  --ai-model claude-3.5-sonnet \
  --tools \
  --stream \
  --output ./clarification-questions.json

#### REFINE SUBCOMMAND

task-o-matic prd refine [options]

Required Options:

--file <path>                # Path to PRD file

Optional Options:

--questions <path>           # Path to questions JSON file
--output <path>              # Output file path (default: overwrite original)
--prompt <prompt>            # Override prompt
--message <message>          # User message
--ai-provider <provider>     # AI provider override
--ai-model <model>           # AI model override
--ai-key <key>               # AI API key override
--ai-provider-url <url>      # AI provider URL override
--ai-reasoning <tokens>      # Enable reasoning for OpenRouter models
--stream                     # Show streaming AI output
--tools                      # Enable filesystem tools for project analysis

Refinement Examples:

# Interactive refinement
task-o-matic prd refine --file ./requirements.md

# Refinement with custom questions
task-o-matic prd refine \
  --file ./draft-prd.md \
  --questions ./custom-questions.json \
  --ai-provider anthropic \
  --ai-model claude-3.5-sonnet \
  --stream

PRD COMMAND ERROR HANDLING

#### Common Error Conditions

# File not found
Error: ENOENT: no such file or directory, open 'requirements.md'
Solution: Verify file path and permissions

# Invalid model format
Error: Invalid model format: invalid-model. Expected provider:model
Solution: Use format "anthropic:claude-3.5-sonnet"

# Missing required arguments
Error: Missing required argument: description
Solution: Provide product description for create command

# AI provider errors
Error: Failed to connect to AI provider
Solution: Check API keys, network connectivity, and provider status

#### Recovery Strategies

File Validation: Verify PRD file exists and is readable
Model Format: Ensure correct provider:model format
API Key Validation: Check environment variables or config
Network Connectivity: Verify internet connection for AI operations
Fallback Models: Use alternative AI providers if primary fails

TECHNICAL SPECIFICATIONS

#### PRD Processing Pipeline

Content Analysis: Parse and understand PRD structure
Context Enhancement: Add project and stack information
AI Processing: Send to configured AI model
Result Validation: Verify output quality and completeness
Storage: Save results to appropriate locations

#### Performance Metrics

Single PRD Generation: 30-120 seconds
Multi-Model Generation: 60-300 seconds (concurrent)
PRD Parsing: 15-60 seconds
PRD Rework: 30-90 seconds
Question Generation: 20-45 seconds

#### Storage Requirements

PRD Files: 10-100KB per document
Generated Questions: 5-20KB per set
AI Metadata: 1-5KB per operation
Cache Data: 50-200MB for documentation cache

BENCHMARK COMMAND

Primary Command: task-o-matic benchmark

COMMAND SIGNATURE

task-o-matic benchmark [subcommand] [options]

DESCRIPTION

The benchmark command provides comprehensive AI model performance testing and comparison capabilities. It supports individual operation benchmarking, workflow benchmarking, and comparative analysis across multiple AI providers and models. This is your intelligence-gathering tool for optimizing AI operations in the wasteland.

SUBCOMMANDS AND COMPREHENSIVE OPTIONS

#### RUN SUBCOMMAND

task-o-matic benchmark run <operation> [options]

Required Arguments:

operation: Operation to benchmark (prd-parse, task-breakdown, task-create, prd-create, etc.)

Required Options:

--models <list>              # Comma-separated list of models (provider:model[:reasoning=<tokens>])

General Options:

--file <path>                # Input file path (for PRD ops)
--task-id <id>               # Task ID (for Task ops)
--concurrency <number>        # Max concurrent requests (default: 5)
--delay <number>             # Delay between requests in ms (default: 250)
--prompt <prompt>             # Override prompt
--message <message>           # User message
--tools                      # Enable filesystem tools
--feedback <feedback>         # Feedback (for prd-rework)

Task Creation Options:

--title <title>              # Task title (for task-create)
--content <content>           # Task content (for task-create)
--parent-id <id>             # Parent task ID (for task-create)
--effort <effort>            # Effort estimate: small, medium, large (for task-create)
--force                      # Force operation (for task-document)

PRD Creation Options:

--description <desc>          # Project/PRD description (for prd-create, prd-combine)
--output-dir <dir>           # Output directory (for prd-create, prd-combine)
--filename <name>            # Output filename (for prd-create, prd-combine)

PRD Combine Options:

--prds <list>                # Comma-separated list of PRD file paths (for prd-combine)

PRD Refine Options:

--question-mode <mode>        # Question mode: user or ai (for prd-refine)
--answers <json>              # JSON string of answers (for prd-refine user mode)

Model Format Examples:

# Basic model format
anthropic:claude-3.5-sonnet
openai:gpt-4
openrouter:anthropic/claude-3.5-sonnet

# With reasoning tokens
openrouter:anthropic/claude-3.5-sonnet:reasoning=2048
openai:o1-preview:reasoning=4096

Benchmark Run Examples:

# Simple benchmark
task-o-matic benchmark run prd-parse \
  --models "anthropic:claude-3.5-sonnet,openai:gpt-4" \
  --file ./requirements.md

# Advanced benchmark with custom settings
task-o-matic benchmark run task-create \
  --models "openrouter:anthropic/claude-3.5-sonnet:reasoning=2048,openrouter:openai/gpt-4o" \
  --title "Create user authentication" \
  --content "Implement JWT-based authentication system" \
  --concurrency 3 \
  --delay 500

# PRD creation benchmark
task-o-matic benchmark run prd-create \
  --models "anthropic:claude-3.5-sonnet,openai:gpt-4,google:gemini-pro" \
  --description "Emergency shelter management system" \
  --output-dir ./benchmark-results

# Task breakdown with tools
task-o-matic benchmark run task-breakdown \
  --models "openrouter:anthropic/claude-3.5-sonnet" \
  --task-id task-123 \
  --tools

#### LIST SUBCOMMAND

task-o-matic benchmark list

Description: Lists all past benchmark runs with timestamps and commands.

Example Output:

Benchmark Runs:
- 2024-01-15_14-30-22_prd-parse (2024-01-15 2:30:22 PM) - prd-parse
- 2024-01-15_13-45-10_task-create (2024-01-15 1:45:10 PM) - task-create
- 2024-01-14_16-20-45_workflow-full (2024-01-14 4:20:45 PM) - workflow-full

#### OPERATIONS SUBCOMMAND

task-o-matic benchmark operations

Description: Lists all available benchmark operations grouped by category.

Example Output:

📊 Available Benchmark Operations

Task Operations:
  task-create          - Create a new task with AI enhancement
  task-enhance        - Enhance an existing task with AI
  task-breakdown      - Break task into subtasks using AI
  task-document       - Fetch and analyze documentation for task

PRD Operations:
  prd-create          - Generate PRD from product description
  prd-parse           - Parse PRD into structured tasks
  prd-rework          - Rework PRD based on feedback
  prd-combine         - Combine multiple PRDs into master PRD

Workflow Operations:
  workflow-full       - Complete workflow execution
  workflow-init       - Project initialization step
  workflow-prd        - PRD creation and refinement

#### SHOW SUBCOMMAND

task-o-matic benchmark show <id>

Arguments:

id: Run ID to display details for

Description: Shows comprehensive details of a specific benchmark run including configuration, results, and performance metrics.

Example Usage:

task-o-matic benchmark show 2024-01-15_14-30-22_prd-parse

#### COMPARE SUBCOMMAND

task-o-matic benchmark compare <id>

Arguments:

id: Run ID to compare results for

Description: Provides comparative analysis of results within a benchmark run, showing performance differences across models.

#### WORKFLOW SUBCOMMAND

task-o-matic benchmark workflow [options]

Required Options:

--models <list>              # Comma-separated list of models (provider:model[:reasoning=<tokens>])

Workflow Configuration Options:

--concurrency <number>        # Max concurrent requests (default: 3)
--delay <number>             # Delay between requests in ms (default: 1000)
--stream                     # Show streaming AI output
--skip-all                   # Skip all optional steps (use defaults)
--auto-accept                # Auto-accept all AI suggestions
--config-file <path>         # Load workflow options from JSON file

Step-Specific Options (same as workflow command):

# Initialization
--skip-init --project-name <name> --init-method <method> --project-description <desc>

# PRD Definition  
--skip-prd --prd-method <method> --prd-file <path> --prd-description <desc>

# PRD Refinement
--skip-refine --refine-method <method> --refine-feedback <feedback>

# Task Generation
--skip-generate --generate-method <method> --generate-instructions <instructions>

# Task Splitting
--skip-split --split-tasks <ids> --split-all --split-method <method>

Workflow Benchmark Examples:

# Basic workflow benchmark
task-o-matic benchmark workflow \
  --models "anthropic:claude-3.5-sonnet,openai:gpt-4" \
  --project-name "Shelter System" \
  --init-method ai \
  --project-description "Emergency shelter management"

# Advanced workflow benchmark
task-o-matic benchmark workflow \
  --models "openrouter:anthropic/claude-3.5-sonnet:reasoning=2048,openrouter:openai/gpt-4o,openrouter:google/gemini-2.0-flash-exp" \
  --concurrency 2 \
  --delay 2000 \
  --auto-accept \
  --skip-refine \
  --generate-method ai \
  --split-all

BENCHMARK PERFORMANCE METRICS

#### Measured Metrics

Duration: Total execution time in milliseconds
TTFT (Time to First Token): Response latency
Tokens: Total token usage (prompt + completion)
TPS (Tokens Per Second): Processing speed
BPS (Bytes Per Second): Data transfer rate
Response Size: Output size in bytes
Cost: Estimated API cost in USD

#### Result Analysis

# Example benchmark output table
Model                                     | Duration | TTFT    | Tokens   | TPS     | BPS     | Size     | Cost
-------------------------------------------|----------|----------|----------|---------|---------|----------|----------
anthropic:claude-3.5-sonnet                | 2340ms   | 120ms    | 1250     | 0.53    | 89      | 208      | $0.004250
openai:gpt-4                              | 3120ms   | 180ms    | 980      | 0.31    | 67      | 195      | $0.039200
openrouter:anthropic/claude-3.5-sonnet     | 2450ms   | 135ms    | 1180     | 0.48    | 85      | 202      | $0.004720

BENCHMARK ERROR HANDLING

#### Common Benchmark Errors

# Invalid model format
Error: Invalid model format: invalid-model. Expected provider:model[:reasoning=<tokens>]
Solution: Use correct format with provider and model

# Operation not found
Error: Benchmark operation 'invalid-op' not found
Solution: Use 'benchmark operations' to see available operations

# Concurrency limits
Error: Too many concurrent requests
Solution: Reduce --concurrency value or increase --delay

# API rate limits
Error: Rate limit exceeded for provider
Solution: Increase delay between requests or reduce concurrency

#### Benchmark Recovery Strategies

Model Validation: Verify all model strings before running
Operation Check: Confirm operation exists with benchmark operations
Rate Limit Handling: Adjust concurrency and delay parameters
Partial Failure Handling: Continue with successful models, retry failed ones

TECHNICAL SPECIFICATIONS

#### Benchmark Architecture

Configuration Parsing: Validate models and options
Concurrent Execution: Run models in parallel with rate limiting
Progress Tracking: Real-time status updates
Result Collection: Gather metrics and outputs
Analysis: Compare and analyze results
Storage: Save results for later review

#### Performance Characteristics

Concurrent Models: Up to 10 models simultaneously
Request Rate: Configurable delay between requests
Memory Usage: 50-200MB per concurrent model
Storage: 1-10MB per benchmark run

#### Resource Requirements

Minimum: 2GB RAM, stable internet connection
Recommended: 4GB RAM, high-speed internet
API Limits: Respect provider rate limits
Disk Space: 100MB for benchmark history

CONFIG COMMAND

Primary Command: task-o-matic config

COMMAND SIGNATURE

task-o-matic config [subcommand] [options]

DESCRIPTION

The config command provides comprehensive configuration management for Task-O-Matic projects. It handles AI provider settings, project information, and system configuration. This is your control panel for customizing Task-O-Matic behavior in the wasteland.

SUBCOMMANDS AND OPTIONS

#### GET-AI-CONFIG SUBCOMMAND

task-o-matic config get-ai-config

Description: Displays current AI configuration including provider, model, and settings.

Example Output:

{
  "provider": "openrouter",
  "model": "anthropic/claude-3.5-sonnet",
  "maxTokens": 32768,
  "temperature": 0.5,
  "apiKey": "***hidden***",
  "baseURL": "https://openrouter.ai/api/v1"
}

#### SET-AI-PROVIDER SUBCOMMAND

task-o-matic config set-ai-provider <provider> [model]

Arguments:

provider: AI provider (openrouter, openai, anthropic, custom)
model: AI model (optional)

Examples:

# Set provider only
task-o-matic config set-ai-provider openrouter

# Set provider and model
task-o-matic config set-ai-provider anthropic claude-3.5-sonnet

# Set custom provider
task-o-matic config set-ai-provider custom gpt-4

#### INFO SUBCOMMAND

task-o-matic config info

Description: Displays comprehensive project information including directory structure, configuration status, and system details.

Example Output:

Task-o-matic Project Info:
  Project Directory: /home/user/wasteland-shelter
  .task-o-matic dir: /home/user/wasteland-shelter/.task-o-matic
  ✓ Config file found: /home/user/wasteland-shelter/.task-o-matic/config.json
  {
    "ai": {
      "provider": "openrouter",
      "model": "anthropic/claude-3.5-sonnet",
      "maxTokens": 32768,
      "temperature": 0.5
    }
  }

CONFIGURATION FILE STRUCTURE

#### Main Config File (.task-o-matic/config.json)

{
  "ai": {
    "provider": "openrouter",
    "model": "anthropic/claude-3.5-sonnet",
    "maxTokens": 32768,
    "temperature": 0.5,
    "apiKey": "sk-or-v1-...",
    "baseURL": "https://openrouter.ai/api/v1"
  },
  "project": {
    "name": "Wasteland Shelter",
    "version": "1.0.0",
    "description": "Emergency shelter management system"
  },
  "storage": {
    "type": "filesystem",
    "path": ".task-o-matic"
  }
}

#### MCP Config File (.task-o-matic/mcp.json)

{
  "context7": {
    "apiKey": "c7-...",
    "cache": {
      "enabled": true,
      "ttl": 86400
    }
  }
}

CONFIGURATION ERROR HANDLING

#### Common Configuration Errors

# Config file not found
Error: Not a task-o-matic project
Solution: Run 'task-o-matic init init' first

# Invalid provider
Error: Unknown AI provider: invalid-provider
Solution: Use valid provider: openrouter, openai, anthropic, custom

# Missing API key
Error: API key not configured
Solution: Set API key via environment variable or config

# Invalid JSON
Error: Invalid configuration file format
Solution: Validate JSON syntax and structure

#### Configuration Recovery

Reinitialization: Run task-o-matic init init to recreate config
Manual Repair: Edit config.json with valid JSON
Environment Override: Use environment variables for temporary fixes
Provider Switch: Change to working AI provider

TECHNICAL SPECIFICATIONS

#### Configuration Hierarchy

Environment Variables: Highest priority
Project Config: .task-o-matic/config.json
Global Config: ~/.task-o-matic/config.json
Default Values: Fallback settings

#### Supported AI Providers

OpenRouter: Multi-provider access with extensive model selection
Anthropic: Direct Claude model access
OpenAI: Direct GPT model access
Custom: Configurable endpoints for self-hosted or special providers

#### Configuration Validation

Provider Validation: Check provider availability
Model Validation: Verify model exists for provider
API Key Validation: Test authentication
Network Connectivity: Verify endpoint accessibility

INIT COMMAND

Primary Command: task-o-matic init

COMMAND SIGNATURE

task-o-matic init [subcommand] [options]

DESCRIPTION

The init command provides comprehensive project initialization and bootstrapping capabilities. It supports both Task-O-Matic project setup and full-stack application bootstrapping using Better-T-Stack. This is your foundation-building tool for establishing new project bases in the wasteland.

SUBCOMMANDS AND COMPREHENSIVE OPTIONS

#### INIT SUBCOMMAND

task-o-matic init init [options]

AI Configuration Options:

--ai-provider <provider>     # AI provider (openrouter/anthropic/openai/custom, default: openrouter)
--ai-model <model>           # AI model (default: z-ai/glm-4.6)
--ai-key <key>               # AI API key
--ai-provider-url <url>      # AI provider URL
--max-tokens <tokens>        # Max tokens for AI (min 32768 for 2025, default: 32768)
--temperature <temp>         # AI temperature (default: 0.5)

Bootstrap Options:

--no-bootstrap               # Skip bootstrap after initialization
--project-name <name>        # Project name for bootstrap
--frontend <frontends...>    # Frontend framework(s) - space/comma-separated (default: next)
--backend <backend>           # Backend framework for bootstrap (default: convex)
--database <database>         # Database for bootstrap
--auth <auth>                # Authentication for bootstrap (default: better-auth)
--context7-api-key <key>     # Context7 API key
--directory <dir>            # Working directory for the project
--package-manager <pm>       # Package manager (npm/pnpm/bun, default: npm)
--runtime <runtime>           # Runtime (bun/node, default: node)
--payment <payment>           # Payment provider (none/polar, default: none)
--cli-deps <level>           # CLI dependency level (minimal/standard/full/task-o-matic, default: standard)
--tui-framework <framework>  # TUI framework (solid/vue/react, default: solid)

Initialization Examples:

# Basic initialization
task-o-matic init init

# Initialization with custom AI settings
task-o-matic init init \
  --ai-provider anthropic \
  --ai-model claude-3.5-sonnet \
  --max-tokens 65536 \
  --temperature 0.3

# Full project setup with bootstrap
task-o-matic init init \
  --project-name "wasteland-shelter" \
  --ai-provider openrouter \
  --ai-model anthropic/claude-3.5-sonnet \
  --frontend next \
  --backend hono \
  --database postgres \
  --auth better-auth

# Custom directory setup
task-o-matic init init \
  --directory ./projects/shelter-system \
  --project-name "shelter-system" \
  --no-bootstrap

#### BOOTSTRAP SUBCOMMAND

task-o-matic init bootstrap <name> [options]

Required Arguments:

name: Project name

Framework Options:

--frontend <frontends...>    # Frontend framework(s) - multiple values supported
--backend <backend>           # Backend framework (hono/express/elysia/convex)
--database <database>         # Database (sqlite/postgres/mysql/mongodb)
--orm <orm>                  # ORM (drizzle/prisma/none, default: drizzle)
--no-auth                    # Exclude authentication
--addons <addons...>          # Additional addons (pwa/tauri/starlight/biome/husky/turborepo)
--examples <examples...>       # Examples to include (todo/ai)
--no-git                     # Skip git initialization
--package-manager <pm>       # Package manager (npm/pnpm/bun, default: npm)
--no-install                 # Skip installing dependencies
--db-setup <setup>           # Database setup (turso/neon/prisma-postgres/mongodb-atlas)
--runtime <runtime>           # Runtime (bun/node, default: node)
--api <type>                 # API type (trpc/orpc)
--payment <payment>           # Payment provider (none/polar, default: none)
--cli-deps <level>           # CLI dependency level (minimal/standard/full/task-o-matic)
--tui-framework <framework>  # TUI framework (solid/vue/react, default: solid)

Frontend Framework Options:

Web: next, tanstack-router, react-router, nuxt, svelte, solid
Native: native-bare, native-uniwind, native-unistyles
Custom: cli, tui

Bootstrap Examples:

# Simple web application
task-o-matic init bootstrap shelter-app \
  --frontend next \
  --backend hono \
  --database sqlite

# Full-stack monorepo with multiple frontends
task-o-matic init bootstrap "wasteland-platform" \
  --frontend "next native-bare cli tui" \
  --backend hono \
  --database postgres \
  --orm drizzle \
  --addons "pwa tauri biome" \
  --examples "todo ai"

# CLI application
task-o-matic init bootstrap "shelter-cli" \
  --frontend cli \
  --cli-deps full \
  --package-manager bun \
  --runtime bun

# TUI application
task-o-matic init bootstrap "shelter-tui" \
  --frontend tui \
  --tui-framework solid \
  --package-manager pnpm

INITIALIZATION WORKFLOW

#### Step 1: Directory Setup

Create target directory if specified
Set working directory in ConfigManager
Verify directory permissions

#### Step 2: Task-O-Matic Structure

Create .task-o-matic/ directory
Create subdirectories: tasks/, prd/, logs/, docs/
Initialize configuration files

#### Step 3: Configuration Setup

Create config.json with AI settings
Create mcp.json with Context7 configuration
Validate configuration syntax

#### Step 4: Bootstrap (Optional)

Call Better-T-Stack CLI with specified options
Handle bootstrap results
Update configuration for new project structure

BOOTSTRAP INTEGRATION

#### Better-T-Stack Integration

The init command integrates with Better-T-Stack CLI for full-stack application bootstrapping:

Supported Features:

Multi-framework frontend support
Multiple backend options
Database setup and configuration
Authentication integration
Addon system for additional features
Example project templates

Bootstrap Process:

Validate Better-T-Stack CLI installation
Prepare configuration options
Execute bootstrap command
Handle results and errors
Update Task-O-Matic configuration

INITIALIZATION ERROR HANDLING

#### Common Initialization Errors

# Directory already initialized
Error: This project is already initialized with task-o-matic
Solution: Use existing project or delete .task-o-matic directory

# Permission denied
Error: EACCES: permission denied, mkdir '.task-o-matic'
Solution: Check directory permissions or use different location

# Bootstrap CLI not found
Error: better-t-stack-cli not found
Solution: Install Better-T-Stack CLI: npm install -g better-t-stack-cli

# Invalid framework combination
Error: Invalid frontend/backend combination
Solution: Check supported combinations in Better-T-Stack documentation

#### Recovery Procedures

Directory Cleanup: Remove partial initialization
Permission Fix: Use appropriate user permissions
Dependency Installation: Install missing CLI tools
Configuration Repair: Manual config file creation

TECHNICAL SPECIFICATIONS

#### Directory Structure

project-root/
├── .task-o-matic/
│   ├── config.json          # Main configuration
│   ├── mcp.json           # MCP configuration
│   ├── tasks/             # Task storage
│   ├── prd/               # PRD storage
│   ├── logs/              # Operation logs
│   └── docs/              # Documentation cache
├── src/                   # Source code (from bootstrap)
├── package.json           # Dependencies (from bootstrap)
└── ...                   # Other bootstrap files

#### Configuration Defaults

AI Provider: openrouter
AI Model: z-ai/glm-4.6
Max Tokens: 32768
Temperature: 0.5
Package Manager: npm
Runtime: node

#### Performance Characteristics

Initialization Time: 5-15 seconds
Bootstrap Time: 1-5 minutes (depends on complexity)
Disk Usage: 10-100MB (depends on selected frameworks)
Memory Usage: 50-200MB during initialization

PROMPT COMMAND

Primary Command: task-o-matic prompt

COMMAND SIGNATURE

task-o-matic prompt [name] [options]

DESCRIPTION

The prompt command provides advanced AI prompt building capabilities with variable replacement and context enhancement. It supports system and user prompts, automatic content detection, and integration with external AI tools. This is your communication toolkit for crafting precise AI instructions in the wasteland.

COMPREHENSIVE OPTIONS

#### Basic Options

name                        # Prompt name (prd-parsing, task-enhancement, etc.)
-t, --type <type>           # Prompt type: system or user (default: user)
-l, --list                   # List all available prompts and exit
-m, --metadata <name>        # Show metadata for specific prompt and exit

#### Content Options

--prd-content <content>       # PRD content (for PRD-related prompts)
--prd-file <filepath>        # Load PRD content from file
--task-title <title>         # Task title (for task-related prompts)
--task-description <description> # Task description (for task-related prompts)
--task-file <filepath>       # Load task description from file
--stack-info <info>          # Technology stack information
--context-info <info>        # Additional context information
--user-feedback <feedback>    # User feedback (for prd-rework prompt)

#### Advanced Options

--var <key=value>           # Custom variable (can be used multiple times)
--full-context               # Include comprehensive project context
--executor <type>            # Format output for specific executor: opencode, claude, gemini, codex

AVAILABLE PROMPTS

#### PRD-Related Prompts

prd-parsing: Parse PRD into structured tasks
prd-generation: Generate PRD from description
prd-rework: Rework PRD based on feedback
prd-combination: Combine multiple PRDs
prd-question: Generate clarifying questions

#### Task-Related Prompts

task-enhancement: Enhance task with additional details
task-breakdown: Break task into subtasks
task-documentation: Generate documentation for task
task-planning: Create implementation plan
task-execution: Generate execution instructions

#### System Prompts

documentation-detection: Detect documentation needs
workflow-assistance: General workflow guidance
error-analysis: Analyze and suggest fixes

PROMPT USAGE EXAMPLES

#### Basic Prompt Building

# List all available prompts
task-o-matic prompt --list

# Show metadata for specific prompt
task-o-matic prompt --metadata prd-parsing

# Build basic PRD parsing prompt
task-o-matic prompt prd-parsing --prd-file ./requirements.md

#### Advanced Prompt Building

# Build task enhancement with full context
task-o-matic prompt task-enhancement \
  --task-title "Implement user authentication" \
  --task-file ./task-desc.md \
  --stack-info "Next.js, TypeScript, Prisma" \
  --full-context \
  --executor claude

# Build PRD rework prompt with custom variables
task-o-matic prompt prd-rework \
  --prd-content "$(cat requirements.md)" \
  --user-feedback "Add security requirements" \
  --var FOCUS="security" \
  --var COMPLEXITY="high" \
  --executor opencode

# System prompt generation
task-o-matic prompt prd-parsing --type system \
  --prd-file ./complex-requirements.md \
  --var STRICT_MODE="true"

#### Context-Aware Prompt Building

# Auto-detect PRD and stack information
task-o-matic prompt task-enhancement \
  --task-title "Build API endpoints" \
  --full-context

# Manual content override
task-o-matic prompt prd-parsing \
  --prd-content "## Emergency Shelter System\n### Overview\n..." \
  --stack-info "React, Node.js, PostgreSQL"

# Multi-variable prompt
task-o-matic prompt task-breakdown \
  --task-title "Create user management" \
  --var ESTIMATED_EFFORT="large" \
  --var PRIORITY="high" \
  --var DEPENDENCIES="authentication,database" \
  --executor gemini

AUTOMATIC CONTENT DETECTION

#### Detection Priority Order

Explicit Options: Directly provided content
File Detection: Auto-detect PRD files in project
Stack Detection: Analyze package.json and dependencies
Context Building: Combine all available information

#### Auto-Detection Features

PRD Content: Searches for common PRD files (README.md, requirements.md, etc.)
Stack Information: Analyzes package.json, tsconfig.json, and other config files
Task Context: Builds rich context from task files and project structure
Full Context: Includes file structure, dependencies, and project metadata

VARIABLE SYSTEM

#### Built-in Variables

PRD_CONTENT: PRD document content
TASK_TITLE: Task title
TASK_DESCRIPTION: Task description
TASK_CONTEXT: Rich task context with metadata
STACK_INFO: Technology stack information
CONTEXT_INFO: Combined context information
USER_FEEDBACK: User feedback for refinement

#### Custom Variables

# Single custom variable
--var FOCUS="security"

# Multiple custom variables
--var FOCUS="security" --var COMPLEXITY="high" --var DEADLINE="urgent"

# Complex values with spaces
--var DESCRIPTION="Build a secure authentication system with JWT"

#### Variable Precedence

Custom Variables (--var): Highest priority
Explicit Options (--prd-content, etc.): Override auto-detection
Auto-Detection: Default behavior
Default Values: Fallback when no content found

EXECUTOR FORMATTING

#### Supported Executors

opencode: Format for OpenCode AI assistant
claude: Format for Anthropic Claude
gemini: Format for Google Gemini
codex: Format for GitHub Copilot/Codex

#### Formatting Differences

# Claude formatting
task-o-matic prompt task-enhancement --executor claude

# OpenCode formatting  
task-o-matic prompt task-enhancement --executor opencode

# Gemini formatting
task-o-matic prompt task-enhancement --executor gemini

PROMPT ERROR HANDLING

#### Common Prompt Errors

# Prompt not found
Error: Prompt not found: invalid-prompt
Solution: Use --list to see available prompts

# Missing required variables
Error: Missing required variables: PRD_CONTENT
Solution: Provide --prd-content or --prd-file

# Invalid variable format
Error: Invalid variable format: invalid-var. Expected key=value
Solution: Use format --var KEY=value

# File not found
Error: ENOENT: no such file or directory, open 'requirements.md'
Solution: Verify file path and permissions

#### Prompt Recovery Strategies

Variable Validation: Check required variables with --metadata
File Verification: Ensure all referenced files exist
Format Checking: Use correct variable format
Executor Validation: Verify executor is supported

TECHNICAL SPECIFICATIONS

#### Prompt Building Pipeline

Variable Collection: Gather all variables from options and auto-detection
Content Detection: Auto-detect PRD, stack, and context information
Variable Resolution: Replace placeholders with actual values
Formatting: Apply executor-specific formatting
Validation: Verify all required variables are present

#### Performance Characteristics

Prompt Building: 50-200ms
Auto-Detection: 100-500ms
File Processing: 10-50ms per file
Variable Resolution: 5-20ms

#### Storage Requirements

Prompt Templates: 1-5KB per prompt
Auto-Detection Cache: 10-50MB
Context Building: 5-20MB per project

FIELD OPERATIONS PROTOCOLS

#### Command Integration Patterns

All main commands integrate with core services through standardized protocols:

Service Access: Commands access services through dependency injection
Configuration Management: Unified configuration system with project-local overrides
Error Handling: Standardized error codes and user-friendly messages
Progress Tracking: Real-time progress updates for long-running operations
Result Formatting: Consistent output formatting across all commands

#### AI Provider Integration

Provider Abstraction: Unified interface for all AI providers
Model Selection: Flexible model configuration per operation
Authentication: Secure API key management
Rate Limiting: Built-in rate limiting and retry logic
Streaming: Real-time response streaming when supported

#### Storage Integration

Local Storage: File-based storage in .task-o-matic/ directory
Data Organization: Logical separation of tasks, PRDs, plans, and documentation
Metadata Management: Rich metadata for all stored objects
Backup Support: Easy backup and restore capabilities

SURVIVAL SCENARIOS

#### Scenario 1: New Project Setup

# Initialize new project with AI assistance
task-o-matic init init \
  --project-name "wasteland-shelter" \
  --ai-provider anthropic \
  --ai-model claude-3.5-sonnet

# Run complete workflow
task-o-matic workflow \
  --project-name "Wasteland Shelter" \
  --init-method ai \
  --project-description "Emergency shelter management system" \
  --auto-accept

#### Scenario 2: Task Management Operations

# Create enhanced task
task-o-matic tasks create \
  --title "Build radiation detector" \
  --content "Design and implement a radiation detection system" \
  --ai-enhance \
  --stream

# Split complex task
task-o-matic tasks split \
  --task-id task-123 \
  --stream \
  --tools

# Execute task with planning
task-o-matic tasks execute \
  --id task-123 \
  --plan \
  --review \
  --auto-commit

#### Scenario 3: PRD Management

# Generate PRD with multiple models
task-o-matic prd create "Emergency communication system" \
  --ai "anthropic:claude-3.5-sonnet,openai:gpt-4,google:gemini-pro" \
  --combine-ai anthropic:claude-3.5-sonnet

# Parse PRD into tasks
task-o-matic prd parse \
  --file ./emergency-communication-prd.md \
  --tools \
  --stream

# Refine PRD with questions
task-o-matic prd refine \
  --file ./draft-prd.md \
  --stream

#### Scenario 4: Performance Optimization

# Benchmark AI models for task creation
task-o-matic benchmark run task-create \
  --models "openrouter:anthropic/claude-3.5-sonnet,openrouter:openai/gpt-4o" \
  --title "Create user authentication" \
  --content "Implement JWT-based authentication" \
  --concurrency 3

# Benchmark complete workflow
task-o-matic benchmark workflow \
  --models "anthropic:claude-3.5-sonnet,openai:gpt-4" \
  --project-name "Performance Test" \
  --init-method quick \
  --auto-accept

#### Scenario 5: Advanced Prompt Engineering

# Build custom prompt with full context
task-o-matic prompt task-enhancement \
  --task-title "Optimize database queries" \
  --full-context \
  --var FOCUS="performance" \
  --var DATABASE="postgresql" \
  --executor claude

# List and analyze available prompts
task-o-matic prompt --list

# Get prompt metadata for customization
task-o-matic prompt --metadata prd-parsing --type system

TECHNICAL SPECIFICATIONS

#### System Architecture

Command Pattern: Each command implements the Command pattern with standardized interfaces
Service Layer: Business logic separated from CLI interface
Configuration Management: Hierarchical configuration with environment variable support
Error Handling: Comprehensive error system with error codes and recovery suggestions
Plugin Architecture: Extensible system for adding new commands and providers

#### Performance Characteristics

Command Execution: 10-100ms for simple commands, 1-60s for AI operations
Memory Usage: 50-500MB depending on operation complexity
Disk I/O: Local file operations, optimized for SSD storage
Network Usage: AI API calls, typically 1-10MB per operation

#### Security Considerations

API Key Management: Secure storage with environment variable support
Input Validation: Comprehensive validation of all user inputs
Path Traversal Prevention: Secure file access controls
Dependency Security: Regular security updates for all dependencies

Remember: In the post-deadline wasteland, these main commands are your survival tools. Master them, respect their power, and they will guide you through the most complex project challenges. The wasteland is unforgiving, but with Task-O-Matic, you have the technology to thrive.

DOCUMENT STATUS: Complete

NEXT REVIEW: After next major version update

CONTACT: Task-O-Matic Technical Documentation Team

TECHNICAL ARCHIVES - CLI REFERENCE

COMPLETE DOCUMENTATION

TECHNICAL BULLETIN NO. 001

MAIN COMMANDS - CORE FIELD OPERATIONS

⚠️ CRITICAL SURVIVAL NOTICE

COMMAND ARCHITECTURE OVERVIEW

COMPLETE COMMAND DOCUMENTATION

TASKS COMMAND

COMMAND SIGNATURE

DESCRIPTION

SUBCOMMANDS

INTEGRATION EXAMPLES

RETURN VALUES

WORKFLOW COMMAND

COMMAND SIGNATURE

DESCRIPTION

COMPREHENSIVE OPTIONS

WORKFLOW EXECUTION EXAMPLES

WORKFLOW STEPS BREAKDOWN

ERROR CONDITIONS AND EXCEPTIONS

TECHNICAL SPECIFICATIONS

PRD COMMAND

COMMAND SIGNATURE

DESCRIPTION

SUBCOMMANDS AND COMPREHENSIVE OPTIONS

PRD COMMAND ERROR HANDLING

TECHNICAL SPECIFICATIONS

BENCHMARK COMMAND

COMMAND SIGNATURE

DESCRIPTION

SUBCOMMANDS AND COMPREHENSIVE OPTIONS

BENCHMARK PERFORMANCE METRICS

BENCHMARK ERROR HANDLING

TECHNICAL SPECIFICATIONS

CONFIG COMMAND

COMMAND SIGNATURE

DESCRIPTION

SUBCOMMANDS AND OPTIONS

CONFIGURATION FILE STRUCTURE

CONFIGURATION ERROR HANDLING

TECHNICAL SPECIFICATIONS

INIT COMMAND

COMMAND SIGNATURE

DESCRIPTION

SUBCOMMANDS AND COMPREHENSIVE OPTIONS

INITIALIZATION WORKFLOW

BOOTSTRAP INTEGRATION

INITIALIZATION ERROR HANDLING

TECHNICAL SPECIFICATIONS

PROMPT COMMAND

COMMAND SIGNATURE

DESCRIPTION

COMPREHENSIVE OPTIONS

AVAILABLE PROMPTS

PROMPT USAGE EXAMPLES

AUTOMATIC CONTENT DETECTION

VARIABLE SYSTEM

EXECUTOR FORMATTING

PROMPT ERROR HANDLING

TECHNICAL SPECIFICATIONS

FIELD OPERATIONS PROTOCOLS

SURVIVAL SCENARIOS

TECHNICAL SPECIFICATIONS