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/memory:load

Delegate to a universal-executor agent to analyze the project and return a structured "Core Content Pack" for memory optimization.

Overview​

The /memory:load command analyzes the project and returns a structured content package loaded directly into main thread memory, providing essential context for subsequent agent operations while minimizing token consumption.

Core Philosophy:

  • Agent-Driven: Fully delegates execution to universal-executor agent
  • Read-Only Analysis: Does not modify code, only extracts context
  • Structured Output: Returns standardized JSON content package
  • Memory Optimization: Package loaded directly into main thread memory
  • Token Efficiency: CLI analysis executed within agent to save tokens

Features​

  • Project Analysis - Comprehensive codebase understanding
  • Core Content Extraction - Identifies key components and patterns
  • Structured Output - JSON format for easy integration
  • Memory Optimization - Reduces token usage for subsequent operations
  • Tech Stack Detection - Automatic identification of technologies used

Usage​

# Load project context into memory
/memory:load

# Load with custom scope
/memory:load --path src/features

# Load with specific depth
/memory:load --depth 3

Execution Flow​

User Input
    ↓
Delegate to universal-executor agent
    ↓
Phase 1: Project Analysis
    ├─ Analyze project structure
    ├─ Identify tech stack
    ├─ Detect key components
    └─ Extract architectural patterns
    ↓
Phase 2: Core Content Extraction
    ├─ Extract main components
    ├─ Identify data structures
    ├─ Find key interfaces
    └─ Document important patterns
    ↓
Phase 3: Structured Output Generation
    ├─ Format as JSON
    ├─ Organize by categories
    └─ Add metadata
    ↓
Phase 4: Memory Loading
    ├─ Load into main thread memory
    ├─ Make available for subsequent operations
    └─ Display summary

Output Format​

The command returns a structured "Core Content Pack":

{
  "project_name": "string",
  "tech_stack": ["list", "of", "technologies"],
  "architecture": {
    "overview": "string",
    "key_components": ["list"],
    "patterns": ["list"]
  },
  "core_content": {
    "components": [
      {
        "name": "string",
        "purpose": "string",
        "location": "path",
        "dependencies": ["list"]
      }
    ],
    "data_structures": [
      {
        "name": "string",
        "purpose": "string",
        "location": "path"
      }
    ],
    "interfaces": [
      {
        "name": "string",
        "methods": ["list"],
        "location": "path"
      }
    ]
  },
  "metadata": {
    "total_modules": number,
    "depth_analyzed": number,
    "timestamp": "ISO8601"
  }
}

Examples​

Basic Usage​

# Load full project context
/memory:load

# Output:
# Delegating to universal-executor agent...
# Analyzing project structure...
# Detected: TypeScript, React, Node.js
# Extracting core content...
# Core Content Pack loaded:
# - 45 components identified
# - 12 data structures
# - 28 interfaces
# Memory load complete

Scoped Analysis​

# Load specific directory
/memory:load --path src/features/auth

# Only analyzes auth feature

Core Content Pack Categories​

Components​

  • Main application components
  • UI elements
  • Service classes
  • Utility functions

Data Structures​

  • Type definitions
  • Interfaces
  • Enums
  • Configuration schemas

Patterns​

  • Architectural patterns
  • Design patterns
  • Coding conventions
  • Best practices

Benefits​

  1. Token Efficiency - Reduces token usage for subsequent operations
  2. Faster Context Loading - Pre-loaded content available immediately
  3. Improved Accuracy - Better context for agent decisions
  4. Structured Knowledge - Organized project information
  5. Quick Navigation - Easy access to key components
  • /memory:update-full - Full project documentation update
  • /memory:update-related - Changed module documentation update
  • /memory:compact - Compact session memory

Notes​

  • Read-only operation - Does not modify any files
  • Agent-driven - Fully delegates to universal-executor agent
  • Memory optimization - Reduces token consumption for subsequent operations
  • Project agnostic - Works with any project type and structure
  • Automatic tech stack detection - Identifies technologies in use
  • Structured output - JSON format for easy integration