Alfred Session State Management Skill (Enterprise v4.0.0)

Skill Metadata

Field	Value
Skill Name	moai-alfred-session-state
Version	4.0.0 (Enterprise)
Updated	2025-11-12
Status	Active
Tier	Alfred
Supported Models	Claude Sonnet 4.5, Claude Haiku 4.5
Context Window	200K tokens (Sonnet/Haiku), 500K tokens (Enterprise), 1M tokens (beta)
Key Features	Context Awareness, Token Budget Tracking, Session Persistence, Adaptive Recovery

---

What It Does

Provides enterprise-grade session state management for extended workflows, token budget optimization, runtime tracking, and handoff protocols to maintain context continuity across Alfred workflows and session boundaries.

Enterprise v4.0.0 Capabilities:

• ✅ Context-aware token budget management (November 2025 Claude API features)
• ✅ Session persistence with automatic history loading
• ✅ Session forking for parallel exploration
• ✅ Incremental multi-pack index optimization (Git 2.47+ integration)
• ✅ Context continuity across handoffs with state snapshots
• ✅ Progressive disclosure for memory efficiency
• ✅ Adaptive recovery checkpoints
• ✅ Multi-agent coordination protocols
• ✅ Memory file state synchronization
• ✅ Token budget awareness callbacks (Sonnet/Haiku 4.5 feature)

---

When to Use

Automatic triggers:

• Session start/end events
• Long-running task execution (>10 minutes)
• Multi-agent handoffs
• Context window approaching limits
• Model switches (Haiku ↔ Sonnet)
• Workflow phase transitions

Manual reference:

• Session state debugging and recovery
• Token budget optimization strategies
• Handoff protocol design
• Context continuity planning
• Multi-session workflow design

---

Token Budget Management (November 2025)

Context Awareness Feature

Claude Sonnet 4.5 and Haiku 4.5 feature built-in context awareness, enabling these models to:

• Track remaining context window ("token budget") throughout conversation
• Understand current position within 200K token limit (Sonnet/Haiku)
• Execute adaptive strategies based on available tokens
• Automatically manage context without manual intervention

Key Advantage: Models now self-regulate context usage in real time.

Token Budget Optimization Framework

Token Allocation Strategy (200K Sonnet context):
├── 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

Optimization Techniques (v4.0.0)

Technique 1: Progressive Summarization

Step 1: Original context (50K tokens)
Step 2: Compress to summary (15K tokens)
Step 3: Add pointers to original → 35K tokens saved
Step 4: Carry forward summary only across handoffs

Technique 2: Context Tagging with Unique Identifiers

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

✅ Good (efficient reference):

Technique 3: Structured Context Architecture

├── Critical Context (ALWAYS keep)
│   ├── Current task objectives
│   ├── User preferences & expertise level
│   └── Active constraints
├── Supporting Context (keep if space allows)
│   ├── Related history
│   ├── Reference documentation
│   └── Tool availability
└── Temporary Context (discard when not needed)
    ├── Raw tool outputs
    ├── Intermediate calculations
    └── Debug information

Technique 4: MCP Server Context Budget

# Check MCP server context consumption
/context

# Result: Each enabled MCP server adds tool definitions
# Example: context7 MCP = ~2K tokens for tool definitions

# Optimization: Disable unused servers before critical tasks
# Typical savings: 5-10K tokens per unused MCP server

Technique 5: Task-Based Session Management

Strategy: Start new conversation for distinct tasks

Benefits:
- Fresh 200K token budget per task
- Eliminates stale context accumulation
- Enables parallel session forking
- Improves recovery speed

Implementation:
1. Complete current task in Session A
2. Save session snapshot to .moai/sessions/
3. Start Session B for new task with fresh context
4. Resume Session A later if needed via session ID

---

Session State Architecture (Enterprise v4.0.0)

State Layers

Session State Stack (Enterprise v4.0.0):
├── 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)

Session Creation & Persistence

Agent SDK Session Management (November 2025 API):

{
  "session_id": "sess_uuid_v4",
  "model": "claude-sonnet-4-5-20250929",
  "created_at": "2025-11-12T10:30:00Z",
  "context_window": {
    "total": 200000,
    "used": 85000,
    "available": 115000,
    "position_percent": 42.5
  },
  "persistence": {
    "auto_load_history": true,
    "context_preservation": "critical_only",
    "cache_enabled": true
  },
  "forking": {
    "enabled": true,
    "fork_session_id": "sess_fork_uuid",
    "checkpoint_timestamp": "2025-11-12T10:30:00Z"
  }
}

Session Resumption Pattern

# Capture session ID from initial response
session_id = extract_session_id(response)

# Save for later use
save_session_checkpoint({
    'session_id': session_id,
    'timestamp': now(),
    'model': 'claude-sonnet-4-5',
    'context_state': current_context_snapshot()
})

# Later: Resume conversation
response = claude.messages.create(
    model="claude-sonnet-4-5-20250929",
    resume=session_id,  # Continue from checkpoint
    messages=[new_message]
)

# Or: Fork session for parallel exploration
response = claude.messages.create(
    model="claude-sonnet-4-5-20250929",
    fork_session=session_id,  # Branch from checkpoint
    messages=[alternative_message]
)

---

Runtime State Tracking

Task State Machine (Enterprise v4.0.0)

Workflow State Transitions:

pending → in_progress → blocked (waiting) → completed/failed
             ↓                                    ↓
         [monitor token budget]          [save checkpoint]
         [track elapsed time]            [update history]
         [check for recovery]            [archive state]

Task Lifecycle States:

• pending

  - Queued but not started

• in_progress

  - Currently executing (monitor tokens)

• blocked

  - Waiting for dependencies or input

• token_warning

  - Approaching context limit (v4.0.0)

• context_switch

  - Model change or session fork

• completed

  - Finished successfully

• failed

  - Error occurred, initiating recovery

• recovered

  - Resumed from checkpoint

Token Budget Callbacks (Haiku/Sonnet 4.5 Feature)

def token_budget_callback(context):
    """
    Called automatically when token budget changes.
    Model provides real-time context awareness.
    """
    
    remaining_tokens = context.available_tokens
    used_percent = context.token_usage_percent
    
    if used_percent > 85:
        # Activate emergency summarization
        compress_context_window()
        archive_old_context()
        
    elif used_percent > 75:
        # Start progressive disclosure
        defer_non_critical_context()
        
    elif used_percent > 60:
        # Monitor for safety
        track_context_growth()

---

Session Handoff Protocols

Inter-Agent Handoff Package (Enterprise v4.0.0)

{
  "handoff_id": "uuid-v4",
  "timestamp": "2025-11-12T10:30:00Z",
  "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",
    "expertise_level": "intermediate",
    "current_project": "MoAI-ADK"
  },
  "task_context": {
    "spec_id": "SPEC-001",
    "current_phase": "implementation",
    "completed_steps": ["spec_complete", "architecture_defined"],
    "next_step": "write_tests",
    "constraints": ["must_use_pytest", "coverage_85"]
  },
  "context_snapshot": {
    "critical_context": "...compressed...",
    "session_checkpoints": [...],
    "active_todos": [...],
    "token_budget_strategy": "progressive_summarization"
  },
  "recovery_info": {
    "last_checkpoint": "2025-11-12T10:25:00Z",
    "recovery_tokens_reserved": 55000,
    "session_fork_available": true
  }
}

Handoff Validation (Enterprise v4.0.0)

def validate_handoff(handoff_package):
    """Enterprise validation with token budget check"""
    
    required_fields = [
        'handoff_id', 'from_agent', 'to_agent',
        'session_context', 'task_context', 'context_snapshot'
    ]
    
    for field in required_fields:
        if field not in handoff_package:
            raise HandoffError(f"Missing required field: {field}")
    
    # NEW v4.0.0: Validate token budget
    context = handoff_package['session_context']
    available = context['available_tokens']
    if available < 30000:  # Minimum safe buffer
        trigger_context_compression()
    
    # Validate agent compatibility
    if not can_agents_cooperate(
        handoff_package['from_agent'],
        handoff_package['to_agent']
    ):
        raise AgentCompatibilityError("Agents cannot cooperate")
    
    return True

---

Session Recovery (Enterprise v4.0.0)

Recovery Checkpoints

Checkpoint Triggers:

• Task phase boundaries (before RED, GREEN, REFACTOR)
• Agent handoffs
• User interruptions
• Token budget thresholds
• Error conditions
• Session timeouts

Checkpoint Structure:

{
  "checkpoint_id": "ckpt_uuid",
  "timestamp": "2025-11-12T10:30:00Z",
  "phase": "GREEN",
  "token_usage": {
    "used": 85000,
    "available": 115000
  },
  "context_snapshot": "...compressed snapshot...",
  "session_id": "sess_uuid",
  "recovery_tokens_reserved": 55000
}

Recovery Process (Enterprise v4.0.0)

1. State Restoration - Reload last valid checkpoint
2. Context Validation - Verify token budget sufficient
3. Session Resumption - Use Agent SDK resume feature
4. Progress Assessment - Determine what was completed
5. Continuation Planning - Decide next steps with updated token budget
6. User Notification - Inform user of recovery status

---

Memory State Synchronization

Memory Files (v4.0.0)

Files:

• .moai/sessions/session-state.json

  - Current session metadata

• .moai/sessions/context-cache.json

  - Cached context for performance

• .moai/sessions/checkpoints/

  - Saved recovery checkpoints

• .moai/sessions/token-usage.log

  - Token budget history

• active-tasks.md

  - TodoWrite task tracking

Synchronization Protocol:

def sync_memory_files(session_state):
    """Ensure memory files reflect current session state"""
    
    # Update session metadata with token info
    update_session_metadata({
        'session_id': session_state.id,
        'token_usage': session_state.token_budget,
        'context_position': session_state.context_position,
        'last_sync': timestamp()
    })
    
    # Sync TodoWrite tasks
    sync_todowrite_tasks(session_state.active_tasks)
    
    # Update context cache (compressed)
    update_context_cache(compress_context(session_state.context))
    
    # Log token usage for analytics
    log_token_usage({
        'timestamp': timestamp(),
        'used': session_state.tokens_used,
        'available': session_state.tokens_available,
        'percent': session_state.usage_percent
    })
    
    # Archive old checkpoints (>7 days)
    archive_old_checkpoints()

---

Best Practices (Enterprise v4.0.0)

Context Management

✅ DO:

• Use context-aware token budget tracking (Sonnet/Haiku 4.5 feature)
• Create checkpoints before major operations
• Apply progressive summarization for long workflows
• Enable session persistence for recovery
• Monitor token usage and plan accordingly
• Use session forking for parallel exploration

❌ DON'T:

• Accumulate unlimited context history
• Ignore token budget warnings
• Skip state validation on recovery
• Lose session IDs without saving
• Mix multiple sessions without clear boundaries
• Assume session continuity without checkpoint

Token Budget Optimization

✅ DO:

• Start new session per distinct task (fresh 200K tokens)
• Use /context to identify expensive MCP servers
• Compress context before handoffs
• Keep reserve buffer (25-30% of tokens)
• Monitor usage percent, not absolute tokens
• Enable auto-summarization at 75% threshold

❌ DON'T:

• Let single conversation exceed 150K tokens
• Keep all MCP servers enabled if not needed
• Reuse sessions across fundamentally different tasks
• Ignore available_tokens feedback
• Store uncompressed context in memory files

---

Configuration

Location:

.moai/config/config.json

{
  "session": {
    "persistence_enabled": true,
    "auto_checkpoint": true,
    "checkpoint_interval_minutes": 10,
    "recovery_strategy": "progressive_summarization",
    "context_budget": {
      "warning_threshold_percent": 75,
      "emergency_threshold_percent": 85,
      "reserve_tokens": 55000
    },
    "forking_enabled": true,
    "max_parallel_sessions": 3
  }
}

---

Debugging & Troubleshooting

Inspection Tools

# View current session state
/alfred:debug --show-session-state

# Check context window position
/alfred:debug --show-token-budget

# View all checkpoints
/alfred:debug --list-checkpoints

# Validate memory synchronization
/alfred:debug --check-memory-sync

# Show token usage history
/alfred:debug --show-token-usage-log

Common Issues

Issue	Symptoms	Solution
Lost session	Cannot resume conversation	Check .moai/sessions/ for session IDs
Token budget exceeded	Model stops responding	Use /context to identify heavy consumers, create new session
Handoff failed	Agent has wrong context	Verify handoff package completeness before transfer
Recovery stuck	Cannot continue after interruption	Restore from earlier checkpoint or start new session
Memory drift	Inconsistent information	Run sync_memory_files() or check cache integrity

---

Version History

Version	Date	Key Changes
4.0.0	2025-11-12	Enterprise context awareness, token budget tracking, Session SDK integration, Git 2.47+ support
1.1.0	2025-11-05	Session state management foundation
1.0.0	2025-10-01	Initial release

---

Related Skills

• moai-alfred-context-budget

  - Token optimization deep dive

• moai-alfred-agent-guide

  - Multi-agent coordination

• moai-foundation-trust

  - State validation principles

• moai-foundation-git

  - Git session state tracking

---

Learn more in

reference.md

for detailed implementation guides, recovery procedures, advanced coordination patterns, and November 2025 API examples.

Skill Status: Production Ready | Last Updated: 2025-11-12 | Model Support: Sonnet 4.5, Haiku 4.5 | Enterprise v4.0.0