Learning Coordinator

When to Use#

• Need to check learning stage of a pattern or correction
• Want to identify emerging patterns from repeated corrections
• Need to coordinate promotion/demotion of patterns across memory tiers
• Integrating with correction‑logger and preference‑tracker for learning workflows

Architecture#

NeverOnce 增强功能#

• ✅ 有效性反馈集成：从增强correction-logger获取有效性分数，跟踪修正使用历史

• ✅ 动态阶段转换算法：基于有效性的自动阶段提升/降级

  • 高有效性模式 → 加速确认
  • 低有效性模式 → 自动降级或标记

• ✅ 反馈循环监控：跟踪模式有效性趋势，识别高/低效学习模式

• ✅ 学习速度计算：基于有效性和反馈趋势的学习速度评估

• ✅ 增强报告生成：模式有效性报告、反馈循环统计、学习进度跟踪

• ✅ 自动调整规则：基于置信度的自动阶段调整，减少人工干预

增强算法#

1. 阶段置信度计算： text

   confidence = (repetition_count * 0.4) + (effectiveness_score * 0.4) + (time_factor * 0.2)
   
   

2. 学习速度评估： text

   learning_speed = (help_ratio * 0.6) + (effectiveness_trend * 0.4)
   
   

3. 自动调整阈值：

   • 自动提升: confidence ≥ 0.8
   • 自动降级: effectiveness ≤ 0.2

集成说明#

• 依赖: 增强correction-logger v2.0.0+（可选，但推荐）
• 数据源: 从纠正记录器获取有效性分数和反馈历史
• 兼容性: 原有API完全兼容，新增增强方法可选使用

The plugin provides a LearningCoordinator class that:

1. Monitors learning signals – watches corrections and preferences via their respective adapters.
2. Manages learning stages – tracks patterns through stages: tentative, emerging, pending, confirmed, archived.
3. Coordinates promotion/demotion – applies rules for when to move patterns between stages and tiers.
4. Exposes learning statistics – reports on learning progress and pattern evolution.

The plugin does not store its own data; it relies on existing adapters (correction‑logger, preference‑tracker) and the learning‑rules file ( learning.md).

Installation#

bash

clawhub install learning-coordinator

Or manually copy the plugin directory to your workspace skills folder.

Configuration#

Default configuration loads the learning rules file and references other adapters:

yaml

learning_rules_file: ~/self-improving/learning.md
correction_adapter: "correction_logger"
preference_adapter: "preference_tracker"
auto_create: true

API Reference#

LearningCoordinator Class#

python

from learning_coordinator import LearningCoordinator

coordinator = LearningCoordinator(config=None)

# Get learning statistics
stats = coordinator.get_learning_stats()

# Check emerging patterns
emerging = coordinator.get_emerging_patterns(threshold=2)

# Promote a pattern (after user confirmation)
result = coordinator.promote_pattern(correction_ids=[1, 2, 3], new_status="confirmed")

# Get stage counts
stage_counts = coordinator.get_stage_counts()

# Health check
health = coordinator.health_check()

Adapter Interface#

The plugin includes a LearningCoordinatorAdapter that conforms to the star‑architecture MemoryAdapter base class, providing:

• health_check() – reports availability of required adapters and rule file
• get_stats() – returns learning statistics (stage counts, promotion rates, etc.)
• search(query, limit=10) – searches across learning rules and pattern descriptions
• sync() – ensures coordinator state is in sync (no‑op for this plugin)
• get_learning_stats(), get_emerging_patterns(), promote_pattern() – convenience methods

Integration with Star Architecture#

Once installed and its adapter is registered in the star‑architecture registry, other plugins can query learning coordination via the adapter factory:

python

from integration.adapter_factory import AdapterFactory

factory = AdapterFactory()
coordinator_adapter = factory.get_adapter("learning_coordinator")
if coordinator_adapter:
    stats = coordinator_adapter.get_learning_stats()
    emerging = coordinator_adapter.get_emerging_patterns(threshold=2)

Learning Rules#

The plugin reads the learning.md file (see SIPA skill) to obtain:

• Trigger definitions – what counts as a learning signal
• Confirmation flow – how and when to ask for user confirmation
• Stage evolution – rules for moving between stages
• Anti‑patterns – what not to learn

The file is treated as read‑only; modifications must be made manually.

Troubleshooting#

Missing adapters – If correction‑logger or preference‑tracker adapters are unavailable, the coordinator will operate with limited functionality.

Rule file not found – If learning.md does not exist, the plugin will create a minimal version based on the SIPA skill's default content.

Permission errors – Ensure the process has read access to the learning rules file.

Related Plugins#

• correction‑logger – logs user corrections and system improvements
• preference‑tracker – manages user preferences and patterns
• heartbeat‑manager – manages heartbeat state and logs
• reflection‑logger – logs self‑reflection entries

Version History#

• v0.1.0 – Initial split from SIPA skill, basic coordination, star‑architecture adapter.

错误码#

错误码描述解决方案E001未知错误检查日志，联系开发者E002配置错误验证配置文件格式E003依赖缺失安装所需依赖包