Enterprise Context & Session Management

Quick Reference (30 seconds)

Enterprise Context & Session Management

Unified context optimization and session state management for Claude Code with 200K token budget management, session persistence, and multi-agent handoff protocols.

Core Capabilities:

• 200K token budget allocation and monitoring
• Session state tracking with persistence
• Context-aware token optimization
• Multi-agent handoff protocols
• Progressive disclosure and memory management
• Session forking for parallel exploration

When to Use:

• Session initialization and cleanup
• Long-running workflows (>10 minutes)
• Multi-agent orchestration
• Context window approaching limits (>150K tokens)
• Model switches (Haiku ↔ Sonnet)
• Workflow phase transitions

Key Principles (2025):

1. Avoid Last 20% - Performance degrades in final fifth of context
2. Aggressive Clearing - /​clear every 1-3 messages for SPEC workflows
3. Lean Memory Files - Keep each file < 500 lines
4. Disable Unused MCPs - Minimize tool definition overhead
5. Quality > Quantity - 10% relevant context beats 90% noise

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Implementation Guide (5 minutes)

Features

• Intelligent context window management for Claude Code sessions
• Progressive file loading with priority-based caching
• Token budget tracking and optimization alerts
• Selective context preservation across /​clear boundaries
• MCP integration context persistence

When to Use

• Managing large codebases exceeding 150K token limits
• Optimizing token usage in long-running development sessions
• Preserving critical context across session resets
• Coordinating multi-agent workflows with shared context
• Debugging context-related issues in Claude Code

Core Patterns

Pattern 1: Progressive File Loading

# Load files by priority tiers
Tier 1: CLAUDE.md, config.json (always loaded)
Tier 2: Current SPEC and implementation files
Tier 3: Related modules and dependencies
Tier 4: Reference documentation (on-demand)

Pattern 2: Context Checkpointing

1. Monitor token usage: warn at 150K, critical at 180K
2. Identify essential context to preserve
3. Execute /​clear to reset session
4. Reload Tier 1 and Tier 2 files automatically
5. Resume work with preserved context

Pattern 3: MCP Context Continuity

# Preserve MCP agent context across /​clear
agent_id = mcp_agent.get_id()
# After /​clear:
resume_mcp_agent(agent_id)  # Restores full context

5 Core Patterns (5-10 minutes each)

Pattern 1: Token Budget Management (200K Context)

Concept: Strategic allocation and monitoring of 200K token context window.

Allocation Strategy:

200K Token Budget Breakdown:
├── System Prompt & Instructions: ~15K tokens (7.5%)
│   ├── CLAUDE.md: ~8K
│   ├── Command definitions: ~4K
│   └── Skill metadata: ~3K
├── Active Conversation: ~80K tokens (40%)
│   ├── Recent messages: ~50K
│   ├── Context cache: ~20K
│   └── Active references: ~10K
├── Reference Context (Progressive Disclosure): ~50K (25%)
│   ├── Project structure: ~15K
│   ├── Related Skills: ~20K
│   └── Tool definitions: ~15K
└── Reserve (Emergency Recovery): ~55K tokens (27.5%)
    ├── Session state snapshot: ~10K
    ├── TAGs and cross-references: ~15K
    ├── Error recovery context: ~20K
    └── Free buffer: ~10K

Monitoring Thresholds:

def monitor_token_budget(context_usage: int):
    """Real-time token budget monitoring with automatic actions."""

    usage_percent = (context_usage / 200000) * 100

    if usage_percent > 85:
        # Critical: Emergency compression
        trigger_emergency_compression()
        execute_clear_command()
    elif usage_percent > 75:
        # Warning: Start progressive disclosure
        defer_non_critical_context()
        warn_user_approaching_limit()
    elif usage_percent > 60:
        # Monitor: Track growth patterns
        track_context_growth()

Use Case: Prevent context overflow in long-running SPEC-First workflows.

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Pattern 2: Aggressive /​clear Strategy

Concept: Proactive context clearing at strategic checkpoints to maintain efficiency.

Clear Execution Rules:

MANDATORY /​clear Points:
├── After /​moai:1-plan completion (saves 45-50K tokens)
├── Context > 150K tokens (prevents overflow)
├── Conversation > 50 messages (removes stale history)
├── Before major phase transitions (clean slate)
└── Model switches (Haiku ↔ Sonnet handoffs)

Implementation:

def should_execute_clear(context: dict) -> bool:
    """Determine if /​clear should be executed."""

    triggers = {
        "post_spec_creation": context.get("spec_created", False),
        "token_threshold": context.get("token_usage", 0) > 150000,
        "message_count": context.get("message_count", 0) > 50,
        "phase_transition": context.get("phase_changed", False)
    }

    return any(triggers.values())

Use Case: Maximize token efficiency across SPEC-Run-Sync cycles.

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Pattern 3: Session State Persistence

Concept: Maintain session continuity across interruptions with state snapshots.

Session State Architecture:

Session State Layers:
├── L1: Context-Aware Layer (Claude 4.5+ feature)
│   ├── Token budget tracking
│   ├── Context window position
│   ├── Auto-summarization triggers
│   └── Model-specific optimizations
├── L2: Active Context (current task, variables, scope)
├── L3: Session History (recent actions, decisions)
├── L4: Project State (SPEC progress, milestones)
├── L5: User Context (preferences, language, expertise)
└── L6: System State (tools, permissions, environment)

State Snapshot Structure:

{
  "session_id": "sess_uuid_v4",
  "model": "claude-sonnet-4-5-20250929",
  "created_at": "2025-11-24T10:30:00Z",
  "context_window": {
    "total": 200000,
    "used": 85000,
    "available": 115000,
    "position_percent": 42.5
  },
  "persistence": {
    "auto_load_history": true,
    "context_preservation": "critical_only",
    "cache_enabled": true
  },
  "work_state": {
    "current_spec": "SPEC-001",
    "phase": "implementation",
    "completed_steps": ["spec_complete", "architecture_defined"]
  }
}

Use Case: Resume long-running tasks after interruptions without context loss.

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Pattern 4: Multi-Agent Handoff Protocols

Concept: Seamless context transfer between agents with minimal token overhead.

Handoff Package:

{
  "handoff_id": "uuid-v4",
  "from_agent": "spec-builder",
  "to_agent": "tdd-implementer",
  "session_context": {
    "session_id": "sess_uuid",
    "model": "claude-sonnet-4-5-20250929",
    "context_position": 42.5,
    "available_tokens": 115000,
    "user_language": "ko"
  },
  "task_context": {
    "spec_id": "SPEC-001",
    "current_phase": "implementation",
    "completed_steps": ["spec_complete", "architecture_defined"],
    "next_step": "write_tests"
  },
  "recovery_info": {
    "last_checkpoint": "2025-11-24T10:25:00Z",
    "recovery_tokens_reserved": 55000,
    "session_fork_available": true
  }
}

Handoff Validation:

def validate_handoff(handoff_package: dict) -> bool:
    """Validate handoff package integrity."""

    # Token budget check
    available = handoff_package['session_context']['available_tokens']
    if available < 30000:  # Minimum safe buffer
        trigger_context_compression()

    # Agent compatibility check
    if not can_agents_cooperate(
        handoff_package['from_agent'],
        handoff_package['to_agent']
    ):
        raise AgentCompatibilityError("Agents cannot cooperate")

    return True

Use Case: Efficient Plan → Run → Sync workflow execution.

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Pattern 5: Progressive Disclosure & Memory Optimization

Concept: Load context progressively based on relevance and need.

Progressive Summarization:

def progressive_summarization(context: str, target_ratio: float = 0.3):
    """Compress context while preserving key information."""

    # Step 1: Extract key sentences (50K → 15K)
    summary = extract_key_sentences(context, ratio=target_ratio)

    # Step 2: Add pointers to original content
    summary_with_refs = add_content_pointers(summary, context)

    # Step 3: Store original for recovery
    store_original_context(context, "session_archive")

    return summary_with_refs  # 35K tokens saved

Context Tagging:

# ❌ Bad (high token cost):
"The user configuration from the previous 20 messages..."

# ✅ Good (efficient reference):
"Refer to @CONFIG-001 for user preferences"

Use Case: Maintain context continuity while minimizing token overhead.

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Advanced Documentation

For detailed patterns and implementation strategies:

• Token Budget Allocation - Budget breakdown, allocation strategies, monitoring thresholds
• Session State Management - State layers, persistence, resumption patterns
• Context Optimization - Progressive disclosure, summarization, memory management
• Handoff Protocols - Inter-agent communication, package format, validation
• Memory & MCP Optimization - Memory file structure, MCP server configuration
• Reference Guide - API reference, troubleshooting, best practices

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Best Practices

✅ DO

• Execute /​clear immediately after SPEC creation
• Monitor token usage and plan accordingly
• Use context-aware token budget tracking
• Create checkpoints before major operations
• Apply progressive summarization for long workflows
• Enable session persistence for recovery
• Use session forking for parallel exploration
• Keep memory files < 500 lines each
• Disable unused MCP servers to reduce overhead

❌ DON'T

• Accumulate unlimited context history
• Ignore token budget warnings (>150K)
• Skip state validation on recovery
• Lose session IDs without saving
• Mix multiple sessions without clear boundaries
• Assume session continuity without checkpoint
• Load entire codebase at once
• Include non-critical context in handoffs
• Exceed 85% context window usage without action

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Works Well With

• moai-cc-memory - Memory management and context persistence
• moai-cc-configuration - Session configuration and preferences
• moai-core-workflow - Workflow state persistence and recovery
• moai-cc-agents - Agent state management across sessions
• moai-foundation-trust - Quality gate integration

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Workflow Integration

Session Initialization:

1. Initialize token budget (Pattern 1)
2. Load session state (Pattern 3)
3. Setup progressive disclosure (Pattern 5)
4. Configure handoff protocols (Pattern 4)

SPEC-First Workflow:

1. /​moai:1-plan execution
   ↓
2. /​clear (mandatory - saves 45-50K tokens)
   ↓
3. /​moai:2-run SPEC-XXX
   ↓
4. Multi-agent handoffs (Pattern 4)
   ↓
5. /​moai:3-sync SPEC-XXX
   ↓
6. Session state persistence (Pattern 3)

Context Monitoring:

Continuous:
  ├─ Track token usage (Pattern 1)
  ├─ Apply progressive disclosure (Pattern 5)
  ├─ Execute /​clear at thresholds (Pattern 2)
  └─ Validate handoffs (Pattern 4)

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Success Metrics

• Token Efficiency: 60-70% reduction through aggressive clearing
• Context Overhead: <15K tokens for system/​skill metadata
• Handoff Success Rate: >95% with validation
• Session Recovery: <5 seconds with state persistence
• Memory Optimization: <500 lines per memory file

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Changelog

• v2.0.0 (2025-11-24): Unified moai-core-context-budget and moai-core-session-state into single skill with 5 core patterns
• v1.0.0 (2025-11-22): Original individual skills

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Status: Production Ready (Enterprise) Modular Architecture: SKILL.md + 6 modules Integration: Plan-Run-Sync workflow optimized Generated with: MoAI-ADK Skill Factory