moai-core-session-state

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Alfred Session State Management Skill (Enterprise )

Alfred 会话状态管理技能(企业版)

Skill Metadata

技能元数据

FieldValue
Skill Namemoai-core-session-state
Version4.0.0 (Enterprise)
Updated2025-11-12
StatusActive
TierAlfred
Supported ModelsClaude Sonnet 4.5, Claude Haiku 4.5
Context Window200K tokens (Sonnet/Haiku), 500K tokens (Enterprise), 1M tokens (beta)
Key FeaturesContext Awareness, Token Budget Tracking, Session Persistence, Adaptive Recovery

字段
技能名称moai-core-session-state
版本4.0.0 (企业版)
更新时间2025-11-12
状态活跃
层级Alfred
支持模型Claude Sonnet 4.5, Claude Haiku 4.5
上下文窗口200K tokens (Sonnet/Haiku), 500K tokens (企业版), 1M tokens (测试版)
核心功能上下文感知, Token预算追踪, 会话持久化, 自适应恢复

What It Does

功能说明

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 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)

为长流程工作流提供企业级会话状态管理、Token预算优化、运行时追踪,以及会话切换协议,保障Alfred工作流和会话边界之间的上下文连续性。
企业版能力:
  • ✅ 上下文感知Token预算管理(2025年11月Claude API新特性)
  • ✅ 会话持久化,自动加载历史记录
  • ✅ 会话分叉,支持并行探索
  • ✅ 增量多包索引优化(集成Git 2.47+)
  • ✅ 基于状态快照的切换上下文连续性保障
  • ✅ 渐进式披露,提升内存效率
  • ✅ 自适应恢复检查点
  • ✅ 多Agent协调协议
  • ✅ 内存文件状态同步
  • ✅ Token预算感知回调(Sonnet/Haiku 4.5特性)

When to Use

适用场景

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

自动触发场景:
  • 会话启动/结束事件
  • 长时间运行任务执行(>10分钟)
  • 多Agent切换
  • 上下文窗口接近上限
  • 模型切换(Haiku ↔ Sonnet)
  • 工作流阶段转换
手动参考场景:
  • 会话状态调试与恢复
  • Token预算优化策略制定
  • 切换协议设计
  • 上下文连续性规划
  • 多会话工作流设计

Token Budget Management (November 2025)

Token预算管理(2025年11月更新)

Context Awareness Feature

上下文感知特性

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.
Claude Sonnet 4.5和Haiku 4.5具备内置上下文感知能力,支持模型实现以下功能:
  • 会话全程追踪剩余上下文窗口(即"Token预算")
  • 感知自身在200K Token上限内的当前位置(Sonnet/Haiku)
  • 基于可用Token执行自适应策略
  • 无需人工干预自动管理上下文
核心优势: 模型现在可以实时自我调节上下文使用。

Token Budget Optimization Framework

Token预算优化框架

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
Token分配策略(200K Sonnet上下文):
├── 系统提示与指令: ~15K tokens (7.5%)
│   ├── CLAUDE.md: ~8K
│   ├── 命令定义: ~4K
│   └── 技能元数据: ~3K
├── 活跃会话: ~80K tokens (40%)
│   ├── 近期消息: ~50K
│   ├── 上下文缓存: ~20K
│   └── 活跃引用: ~10K
├── 参考上下文(渐进式披露): ~50K (25%)
│   ├── 项目结构: ~15K
│   ├── 相关技能: ~20K
│   └── 工具定义: ~15K
└── 预留(紧急恢复): ~55K tokens (27.5%)
    ├── 会话状态快照: ~10K
    ├── 标签与交叉引用: ~15K
    ├── 错误恢复上下文: ~20K
    └── 空闲缓冲区: ~10K

Optimization Techniques ( .0)

优化技术(4.0版本)

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
bash
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技术1:渐进式总结
步骤1:原始上下文(50K tokens)
步骤2:压缩为摘要(15K tokens)
步骤3:添加原始内容指针 → 节省35K tokens
步骤4:会话切换时仅传递摘要
技术2:带唯一标识的上下文标记

❌ 错误示例(Token消耗高):
"The user configuration from the previous 20 messages..."

✅ 正确示例(高效引用):
技术3:结构化上下文架构
├── 关键上下文(永久保留)
│   ├── 当前任务目标
│   ├── 用户偏好与专业水平
│   └── 活跃约束
├── 支持上下文(空间充足时保留)
│   ├── 相关历史
│   ├── 参考文档
│   └── 可用工具
└── 临时上下文(无需时丢弃)
    ├── 原始工具输出
    ├── 中间计算结果
    └── 调试信息
技术4:MCP服务器上下文预算优化
bash
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Check MCP server context consumption

查看MCP服务器上下文消耗

/context
/context

Result: Each enabled MCP server adds tool definitions

结果:每个启用的MCP服务器都会增加工具定义占用

Example: context7 MCP = ~2K tokens for tool definitions

示例:context7 MCP = 工具定义占用约2K tokens

Optimization: Disable unused servers before critical tasks

优化方案:执行关键任务前禁用未使用的服务器

Typical savings: 5-10K tokens per unused MCP server

典型收益:每个未使用的MCP服务器节省5-10K tokens


**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

---

**技术5:基于任务的会话管理**
策略:不同任务开启独立新会话
优势:
  • 每个任务享有全新200K Token预算
  • 避免过时上下文累积
  • 支持并行会话分叉
  • 提升恢复速度
实现方式:
  1. 在会话A中完成当前任务
  2. 将会话快照保存到 .moai/sessions/
  3. 为新任务开启会话B,使用全新上下文
  4. 后续需要时可通过会话ID恢复会话A

---

Session State Architecture (Enterprise )

会话状态架构(企业版)

State Layers

状态层级

Session State Stack (Enterprise ):
├── 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)
会话状态栈(企业版):
├── L1: 上下文感知层(Claude 4.5+ 特性)
│   ├── Token预算追踪
│   ├── 上下文窗口位置
│   ├── 自动摘要触发
│   └── 模型专属优化
├── L2: 活跃上下文(当前任务、变量、范围)
├── L3: 会话历史(近期操作、决策)
├── L4: 项目状态(SPEC进度、里程碑)
├── L5: 用户上下文(偏好、语言、专业水平)
└── L6: 系统状态(工具、权限、环境)

Session Creation & Persistence

会话创建与持久化

Agent SDK Session Management (November 2025 API):
json
{
  "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"
  }
}
Agent SDK 会话管理(2025年11月API):
json
{
  "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

会话恢复模式

python
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python
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Capture session ID from initial response

从初始响应中提取会话ID

session_id = extract_session_id(response)
session_id = extract_session_id(response)

Save for later use

保存供后续使用

save_session_checkpoint({ 'session_id': session_id, 'timestamp': now(), 'model': 'claude-sonnet-4-5', 'context_state': current_context_snapshot() })
save_session_checkpoint({ 'session_id': session_id, 'timestamp': now(), 'model': 'claude-sonnet-4-5', 'context_state': current_context_snapshot() })

Later: Resume conversation

后续:恢复会话

response = claude.messages.create( model="claude-sonnet-4-5-20250929", resume=session_id, # Continue from checkpoint messages=[new_message] )
response = claude.messages.create( model="claude-sonnet-4-5-20250929", resume=session_id, # 从检查点继续 messages=[new_message] )

Or: Fork session for parallel exploration

或:分叉会话进行并行探索

response = claude.messages.create( model="claude-sonnet-4-5-20250929", fork_session=session_id, # Branch from checkpoint messages=[alternative_message] )

---
response = claude.messages.create( model="claude-sonnet-4-5-20250929", fork_session=session_id, # 从检查点分支 messages=[alternative_message] )

---

Runtime State Tracking

运行时状态追踪

Task State Machine (Enterprise )

任务状态机(企业版)

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 ( .0)
  • context_switch
    - Model change or session fork
  • completed
    - Finished successfully
  • failed
    - Error occurred, initiating recovery
  • recovered
    - Resumed from checkpoint
工作流状态转换:

pending → in_progress → blocked (waiting) → completed/failed
             ↓                                    ↓
         [监控Token预算]                      [保存检查点]
         [追踪运行时长]                      [更新历史记录]
         [检查恢复条件]                      [归档状态]
任务生命周期状态:
  • pending
    - 已排队未启动
  • in_progress
    - 执行中(监控Token消耗)
  • blocked
    - 等待依赖或用户输入
  • token_warning
    - 接近上下文上限(4.0版本新特性)
  • context_switch
    - 模型切换或会话分叉
  • completed
    - 执行成功
  • failed
    - 出现错误,启动恢复流程
  • recovered
    - 从检查点恢复成功

Token Budget Callbacks (Haiku/Sonnet 4.5 Feature)

Token预算回调(Haiku/Sonnet 4.5特性)

python
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()

python
def token_budget_callback(context):
    """
    Token预算变化时自动调用
    模型提供实时上下文感知能力
    """
    
    remaining_tokens = context.available_tokens
    used_percent = context.token_usage_percent
    
    if used_percent > 85:
        # 启动紧急摘要压缩
        compress_context_window()
        archive_old_context()
        
    elif used_percent > 75:
        # 启动渐进式披露
        defer_non_critical_context()
        
    elif used_percent > 60:
        # 安全监控
        track_context_growth()

Session Handoff Protocols

会话切换协议

Inter-Agent Handoff Package (Enterprise )

Agent间切换数据包(企业版)

json
{
  "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
  }
}
json
{
  "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 )

切换校验(企业版)

python
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 : 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

python
def validate_handoff(handoff_package):
    """包含Token预算检查的企业级校验"""
    
    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}")
    
    # 新特性:校验Token预算
    context = handoff_package['session_context']
    available = context['available_tokens']
    if available < 30000:  # 最低安全缓冲区
        trigger_context_compression()
    
    # 校验Agent兼容性
    if not can_agents_cooperate(
        handoff_package['from_agent'],
        handoff_package['to_agent']
    ):
        raise AgentCompatibilityError("Agents cannot cooperate")
    
    return True

Session Recovery (Enterprise )

会话恢复(企业版)

Recovery Checkpoints

恢复检查点

Checkpoint Triggers:
  • Task phase boundaries (before RED, GREEN, REFACTOR)
  • Agent handoffs
  • User interruptions
  • Token budget thresholds
  • Error conditions
  • Session timeouts
Checkpoint Structure:
json
{
  "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
}
检查点触发条件:
  • 任务阶段边界(RED、GREEN、REFACTOR阶段前)
  • Agent切换
  • 用户中断
  • Token预算阈值触发
  • 错误场景
  • 会话超时
检查点结构:
json
{
  "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 )

恢复流程(企业版)

  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

  1. 状态还原 - 加载最近的有效检查点
  2. 上下文校验 - 验证Token预算充足
  3. 会话恢复 - 使用Agent SDK恢复功能
  4. 进度评估 - 确认已完成的工作内容
  5. 后续规划 - 基于更新后的Token预算制定下一步计划
  6. 用户通知 - 告知用户恢复状态

Memory State Synchronization

内存状态同步

Memory Files ( .0)

内存文件(4.0版本)

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:
python
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()

文件列表:
  • .moai/sessions/session-state.json
    - 当前会话元数据
  • .moai/sessions/context-cache.json
    - 用于提升性能的上下文缓存
  • .moai/sessions/checkpoints/
    - 保存的恢复检查点
  • .moai/sessions/token-usage.log
    - Token预算历史记录
  • active-tasks.md
    - TodoWrite任务追踪
同步协议:
python
def sync_memory_files(session_state):
    """确保内存文件与当前会话状态一致"""
    
    # 用Token信息更新会话元数据
    update_session_metadata({
        'session_id': session_state.id,
        'token_usage': session_state.token_budget,
        'context_position': session_state.context_position,
        'last_sync': timestamp()
    })
    
    # 同步TodoWrite任务
    sync_todowrite_tasks(session_state.active_tasks)
    
    # 更新上下文缓存(压缩后)
    update_context_cache(compress_context(session_state.context))
    
    # 记录Token使用情况用于分析
    log_token_usage({
        'timestamp': timestamp(),
        'used': session_state.tokens_used,
        'available': session_state.tokens_available,
        'percent': session_state.usage_percent
    })
    
    # 归档7天以上的旧检查点
    archive_old_checkpoints()

Best Practices (Enterprise )

最佳实践(企业版)

Context Management

上下文管理

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
推荐做法:
  • 使用上下文感知Token预算追踪(Sonnet/Haiku 4.5特性)
  • 重大操作前创建检查点
  • 长工作流使用渐进式总结
  • 开启会话持久化用于恢复
  • 监控Token使用并提前规划
  • 使用会话分叉进行并行探索
不推荐做法:
  • 无限制累积上下文历史
  • 忽略Token预算警告
  • 恢复时跳过状态校验
  • 未保存就丢失会话ID
  • 无明确边界混合多个会话
  • 无检查点就假设会话连续

Token Budget Optimization

Token预算优化

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

推荐做法:
  • 不同任务开启独立新会话(全新200K Token)
  • 使用/context命令识别高消耗MCP服务器
  • 会话切换前压缩上下文
  • 保留25-30%的Token作为预留缓冲区
  • 监控使用率百分比而非绝对Token数
  • 75%使用率阈值时开启自动摘要
不推荐做法:
  • 单会话对话超过150K Token
  • 不需要时仍保持所有MCP服务器开启
  • 完全不同的任务复用同一会话
  • 忽略available_tokens反馈
  • 内存文件中存储未压缩的上下文

Configuration

配置

Location:
.moai/config/config.json
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
  }
}

配置文件路径:
.moai/config/config.json
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

调试与故障排查

Inspection Tools

检查工具

bash
undefined
bash
undefined

View current session state

查看当前会话状态

/alfred:debug --show-session-state
/alfred:debug --show-session-state

Check context window position

检查上下文窗口位置

/alfred:debug --show-token-budget
/alfred:debug --show-token-budget

View all checkpoints

查看所有检查点

/alfred:debug --list-checkpoints
/alfred:debug --list-checkpoints

Validate memory synchronization

校验内存同步状态

/alfred:debug --check-memory-sync
/alfred:debug --check-memory-sync

Show token usage history

查看Token使用历史

/alfred:debug --show-token-usage-log
undefined
/alfred:debug --show-token-usage-log
undefined

Common Issues

常见问题

IssueSymptomsSolution
Lost sessionCannot resume conversationCheck .moai/sessions/ for session IDs
Token budget exceededModel stops respondingUse /context to identify heavy consumers, create new session
Handoff failedAgent has wrong contextVerify handoff package completeness before transfer
Recovery stuckCannot continue after interruptionRestore from earlier checkpoint or start new session
Memory driftInconsistent informationRun sync_memory_files() or check cache integrity

问题症状解决方案
会话丢失无法恢复对话检查.moai/sessions/目录下的会话ID
Token预算超出模型停止响应使用/context命令识别高消耗项,创建新会话
切换失败Agent获取到错误上下文传输前校验切换数据包完整性
恢复卡住中断后无法继续从更早的检查点恢复或开启新会话
内存漂移信息不一致执行sync_memory_files()或检查缓存完整性

Version History

版本历史

VersionDateKey Changes
4.0.02025-11-12Enterprise context awareness, token budget tracking, Session SDK integration, Git 2.47+ support
1.1.02025-11-05Session state management foundation
1.0.02025-10-01Initial release

版本日期关键更新
4.0.02025-11-12企业级上下文感知、Token预算追踪、Session SDK集成、Git 2.47+支持
1.1.02025-11-05会话状态管理基础能力
1.0.02025-10-01首次发布

Related Skills

相关技能

  • moai-core-context-budget
    - Token optimization deep dive
  • moai-core-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**: 2025-11-18 | Model Support: Sonnet 4.5, Haiku 4.5 | Enterprise
  • moai-core-context-budget
    - Token优化深度指南
  • moai-core-agent-guide
    - 多Agent协调
  • moai-foundation-trust
    - 状态校验原则
  • moai-foundation-git
    - Git会话状态追踪

查看
reference.md
获取详细实现指南、恢复流程、高级协调模式和2025年11月API示例。
技能状态: 可用于生产环境 | 最后更新**: 2025-11-18 | 支持模型: Sonnet 4.5, Haiku 4.5 | 企业版