state_management（状态管理）

State Management Patterns Skill

Standardized state management and persistence patterns for the autonomous-dev plugin ecosystem. Ensures reliable, crash-resistant state persistence across Claude restarts and system failures.

When This Skill Activates

• Implementing state persistence
• Managing crash recovery
• Handling concurrent state access
• Versioning state schemas
• Tracking batch operations
• Managing user preferences
• Keywords: "state", "persistence", "JSON", "atomic", "crash recovery", "checkpoint"

Core Patterns

1. JSON Persistence with Atomic Writes

Definition: Store state in JSON files with atomic writes to prevent corruption on crash.

Pattern:

import json
from pathlib import Path
from typing import Dict, Any
import tempfile
import os

def save_state_atomic(state: Dict[str, Any], state_file: Path) -> None:
    """Save state with atomic write to prevent corruption.

    Args:
        state: State dictionary to persist
        state_file: Target state file path

    Security:
        - Atomic Write: Prevents partial writes on crash
        - Temp File: Write to temp, then rename (atomic operation)
        - Permissions: Preserves file permissions
    """
    # Write to temporary file first
    temp_fd, temp_path = tempfile.mkstemp(
        dir=state_file.parent,
        prefix=f".{state_file.name}.",
        suffix=".tmp"
    )

    try:
        # Write JSON to temp file
        with os.fdopen(temp_fd, 'w') as f:
            json.dump(state, f, indent=2)

        # Atomic rename (overwrites target)
        os.replace(temp_path, state_file)

    except Exception:
        # Clean up temp file on failure
        if Path(temp_path).exists():
            Path(temp_path).unlink()
        raise

See: docs/json-persistence.md, examples/batch-state-example.py

2. File Locking for Concurrent Access

Definition: Use file locks to prevent concurrent modification of state files.

Pattern:

import fcntl
import json
from pathlib import Path
from contextlib import contextmanager

@contextmanager
def file_lock(filepath: Path):
    """Acquire exclusive file lock for state file.

    Args:
        filepath: Path to file to lock

    Yields:
        Open file handle with exclusive lock

    Example:
        >>> with file_lock(state_file) as f:
        ...     state = json.load(f)
        ...     state['count'] += 1
        ...     f.seek(0)
        ...     f.truncate()
        ...     json.dump(state, f)
    """
    with filepath.open('r+') as f:
        fcntl.flock(f.fileno(), fcntl.LOCK_EX)
        try:
            yield f
        finally:
            fcntl.flock(f.fileno(), fcntl.LOCK_UN)

See: docs/file-locking.md, templates/file-lock-template.py

3. Crash Recovery Pattern

Definition: Design state to enable recovery after crashes or interruptions.

Principles:

• State includes enough context to resume operations
• Progress tracking enables "resume from last checkpoint"
• State validation detects corruption
• Migration paths handle schema changes

Example:

@dataclass
class BatchState:
    """Batch processing state with crash recovery support.

    Attributes:
        batch_id: Unique batch identifier
        features: List of all features to process
        current_index: Index of current feature
        completed: List of completed feature names
        failed: List of failed feature names
        created_at: State creation timestamp
        last_updated: Last update timestamp
    """
    batch_id: str
    features: List[str]
    current_index: int = 0
    completed: List[str] = None
    failed: List[str] = None
    created_at: str = None
    last_updated: str = None

    def __post_init__(self):
        if self.completed is None:
            self.completed = []
        if self.failed is None:
            self.failed = []
        if self.created_at is None:
            self.created_at = datetime.now().isoformat()
        self.last_updated = datetime.now().isoformat()

See: docs/crash-recovery.md, examples/crash-recovery-example.py

4. State Versioning and Migration

Definition: Version state schemas to enable graceful upgrades.

Pattern:

STATE_VERSION = "2.0.0"

def migrate_state(state: Dict[str, Any]) -> Dict[str, Any]:
    """Migrate state from old version to current.

    Args:
        state: State dictionary (any version)

    Returns:
        Migrated state (current version)
    """
    version = state.get("version", "1.0.0")

    if version == "1.0.0":
        # Migrate 1.0.0 → 1.1.0
        state = _migrate_1_0_to_1_1(state)
        version = "1.1.0"

    if version == "1.1.0":
        # Migrate 1.1.0 → 2.0.0
        state = _migrate_1_1_to_2_0(state)
        version = "2.0.0"

    state["version"] = STATE_VERSION
    return state

See: docs/state-versioning.md, templates/state-manager-template.py

Real-World Examples

BatchStateManager Pattern

From plugins/autonomous-dev/lib/batch_state_manager.py:

Features:

• JSON persistence with atomic writes
• Crash recovery via --resume flag
• Progress tracking (completed/failed features)
• Automatic context clearing at 150K tokens
• State versioning for schema upgrades

Usage:

# Create batch state
manager = BatchStateManager.create(["feat1", "feat2", "feat3"])
manager.batch_id  # "batch-20251116-123456"

# Process features
for feature in manager.features:
    if manager.should_clear_context():
        # Clear context at 150K tokens
        manager.record_context_clear()

    try:
        # Process feature
        result = process_feature(feature)
        manager.mark_completed(feature)
    except Exception as e:
        manager.mark_failed(feature, str(e))

    manager.save()  # Atomic write

# Resume after crash
manager = BatchStateManager.load("batch-20251116-123456")
next_feature = manager.get_next_feature()  # Skips completed

Checkpoint Integration (Issue #79)

Agents save checkpoints using the portable pattern:

Portable Pattern (Works Anywhere)

from pathlib import Path
import sys

# Portable path detection
current = Path.cwd()
while current != current.parent:
    if (current / ".git").exists():
        project_root = current
        break
    current = current.parent

# Add lib to path
lib_path = project_root / "plugins/autonomous-dev/lib"
if lib_path.exists():
    sys.path.insert(0, str(lib_path))

    try:
        from agent_tracker import AgentTracker
        success = AgentTracker.save_agent_checkpoint(
            agent_name='my-agent',
            message='Task completed - found 5 patterns',
            tools_used=['Read', 'Grep', 'WebSearch']
        )
        print(f"Checkpoint: {'saved' if success else 'skipped'}")
    except ImportError:
        print("ℹ️ Checkpoint skipped (user project)")

Features

• Portable: Works from any directory (user projects, subdirectories, fresh installs)
• No hardcoded paths: Uses dynamic project root detection
• Graceful degradation: Returns False, doesn't block workflow
• Security validated: Path validation (CWE-22), no subprocess (CWE-78)

Design Patterns

• Progressive Enhancement: Works with or without tracking infrastructure
• Non-blocking: Never raises exceptions
• Two-tier: Library imports instead of subprocess calls

See: LIBRARIES.md Section 24 (agent_tracker.py), DEVELOPMENT.md Scenario 2.5, docs/LIBRARIES.md for API

Usage Guidelines

For Library Authors

When implementing stateful features:

1. Use JSON persistence with atomic writes
2. Add file locking for concurrent access protection
3. Design for crash recovery with resumable state
4. Version your state for schema evolution
5. Validate on load to detect corruption

For Claude

When creating or analyzing stateful libraries:

1. Load this skill when keywords match ("state", "persistence", etc.)
2. Follow persistence patterns for reliability
3. Implement crash recovery for long-running operations
4. Use atomic operations to prevent corruption
5. Reference templates in templates/ directory

Token Savings

By centralizing state management patterns in this skill:

• Before: ~50 tokens per library for inline state management docs
• After: ~10 tokens for skill reference comment
• Savings: ~40 tokens per library
• Total: ~400 tokens across 10 libraries (4-5% reduction)

Progressive Disclosure

This skill uses Claude Code 2.0+ progressive disclosure architecture:

• Metadata (frontmatter): Always loaded (~180 tokens)
• Full content: Loaded only when keywords match
• Result: Efficient context usage, scales to 100+ skills

When you use terms like "state management", "persistence", "crash recovery", or "atomic writes", Claude Code automatically loads the full skill content.

Templates and Examples

Templates (reusable code structures)

• templates/state-manager-template.py: Complete state manager class
• templates/atomic-write-template.py: Atomic write implementation
• templates/file-lock-template.py: File locking utilities

Examples (real implementations)

• examples/batch-state-example.py: BatchStateManager pattern
• examples/user-state-example.py: UserStateManager pattern
• examples/crash-recovery-example.py: Crash recovery demonstration

Documentation (detailed guides)

• docs/json-persistence.md: JSON storage patterns
• docs/atomic-writes.md: Atomic write implementation
• docs/file-locking.md: Concurrent access protection
• docs/crash-recovery.md: Recovery strategies

Cross-References

This skill integrates with other autonomous-dev skills:

• library-design-patterns: Two-tier design, progressive enhancement
• error-handling-patterns: Exception handling and recovery
• security-patterns: File permissions and path validation

See: skills/library-design-patterns/, skills/error-handling-patterns/

Maintenance

This skill should be updated when:

• New state management patterns emerge
• State schema versioning needs change
• Concurrency patterns evolve
• Performance optimizations discovered

🔄 Workflow

Kaynak: Redux Documentation - State Management Best Practices & React State Management Guide

Aşama 1: State Scoping & Selection

• Identify State Type: State'in tipini belirle (Server State, UI State, Form State, Global State).
• Tool Selection: Karmaşıklığa göre useState, Context, Zustand veya Redux Toolkit arasından doğru aracı seç.
• Single Source of Truth: Aynı verinin birden fazla yerde state olarak tutulmadığını (Derivable state) doğrula.

Aşama 2: Implementation & Sync

• Action/Mutation Design: State güncellemeleri için net fonksiyonlar veya reducer'lar tanımla.
• Effect Syncing: State değişikliklerinin yan etkilerini (API çağrıları, LocalStorage) kontrollü bir şekilde yönet.
• Persistence Strategy: Gerekli state'leri (örn: User session) persist middleware veya custom logic ile kalıcı hale getir.

Aşama 3: Optimization & Debugging

• Selector Usage: Zustand/Redux kullanılıyorsa, sadece gerekli state parçalarını (selectors) seçerek re-render'ı önle.
• DevTools Audit: State değişimlerini Redux DevTools veya benzeri araçlarla adım adım izle.
• Testing: State değişimlerinin beklenen sonuçları verip vermediğini unit testlerle doğrula.

Kontrol Noktaları

*State Management v1.5 - With Workflow*