Skill: whoisme007/learning-coordinator (version 2.0.0)

--- name: learning-coordinator version: 2.0.0 layer: processing function_type: learning_coordination health: healthy adapter: learning_coordinator_adapter dependencies: [] slug: learning-coordinator homepage: https://clawhub.com/skills/learning-coordinator description: Coordinates learning signals, pattern promotion, and stage management for self-improving memory. Monitors corrections and preferences to identify emerging patterns and manage learning stages. Integrates with Memory Sync Enhanced star architecture via adapter. changelog: Initial release – single-function plugin split from SIPA skill. metadata: clawdbot: emoji: 🎓 requires: bins: [] os: - linux - darwin - win32 configPaths: - ~/self-improving/ configPaths.optional: [] --- ## 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**： ``` confidence = (repetition_count * 0.4) + (effectiveness_score * 0.4) + (time_factor * 0.2) ``` 2. **学习速度评估**： ``` 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 |


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)