| name | opc-pattern-recognition |
| description | OPC Journal Suite Pattern Recognition Module - Behavioral analysis and habit identification. Use when: (1) analyzing work patterns and rhythms, (2) identifying decision-making styles, (3) detecting productivity trends. NOT for: predicting future outcomes with certainty → patterns are probabilistic. |
| metadata | {"openclaw":{"emoji":"🧠","requires":{}}} |
opc-pattern-recognition
Version: 2.3.0
Status: Production Ready
When to Use
✅ Use this skill for:
- Analyzing work rhythms and productive hours
- Identifying decision-making patterns
- Detecting stress triggers and recovery patterns
- Tracking learning velocity and skill acquisition
- Understanding emotional patterns in work
When NOT to Use
❌ Don't use when:
- Need guaranteed predictions of future behavior
- Require clinical psychological analysis
- Want to compare with other users (privacy-respecting, no cross-user data)
Description
OPC Journal Suite Pattern Recognition Module - Analyzes user behavioral patterns, work habits, decision styles, and provides personalized insights and recommendations.
When to use
- User asks "Why do I always...", "Analyze my habits"
- Auto-generate weekly/monthly insights
- Predict potential difficulties user may encounter
- Optimize Agent collaboration with user
Tools
memory_search - Retrieve historical data for analysisread - Read Journal entrieswrite - Write analysis reports
Analysis Dimensions
1. Work Rhythm
analyze_work_rhythm(
customer_id="OPC-001",
metrics=[
"productive_hours",
"peak_focus_duration",
"break_patterns",
"weekly_energy_curve"
]
)
Output Example:
work_rhythm:
productive_hours:
- day: "wednesday"
time_range: "14:00-17:00"
effectiveness_score: 9.2
- day: "friday"
time_range: "09:00-12:00"
effectiveness_score: 8.7
low_energy_periods:
- day: "monday"
time_range: "09:00-11:00"
pattern: "Monday morning startup difficulty"
recommendations:
- "将重要决策安排在周三下午"
- "周一早晨安排轻松任务作为热身"
- "周五上午适合创造性工作"
2. 决策模式 (Decision Style)
analyze_decision_patterns(
customer_id="OPC-001",
decision_types=["technical", "business", "hiring", "pricing"]
)
输出示例:
decision_patterns:
risk_appetite: "conservative"
common_hesitations:
- topic: "技术选型"
avg_decision_time: "3.2 days"
typical_concerns: ["长期维护", "社区活跃度", "学习成本"]
- topic: "定价策略"
avg_decision_time: "5.7 days"
typical_concerns: ["市场接受度", "利润空间", "竞争定位"]
decision_triggers:
- "收集 3 个以上同类案例"
- "与至少 1 人讨论"
- "设定明确截止日期"
improvement_suggestions:
- "技术选型: 创建标准化评估框架"
- "定价策略: 准备 3 套方案供选择"
3. 求助模式 (Help Seeking)
analyze_help_seeking(
customer_id="OPC-001",
metrics=["timing", "topics", "resolution_rate"]
)
输出示例:
help_seeking:
average_delay: "2.3 days"
common_topics:
- "technical_blockers": 45%
- "business_strategy": 30%
- "emotional_support": 15%
- "tool_usage": 10%
patterns:
- "倾向于独立尝试 2-3 次后才求助"
- "深夜时段更容易表达焦虑"
- "成功解决问题后会主动分享经验"
early_intervention_opportunities:
- "检测到技术关键词 + 长时间无进展 → 主动提供资源"
- "连续 2 天能量水平 < 5 → 主动询问状态"
4. 成长轨迹 (Growth Trajectory)
analyze_growth(
customer_id="OPC-001",
time_range="0-90_days",
dimensions=["technical", "business", "ai_collaboration"]
)
输出示例:
growth_trajectory:
baseline:
technical_confidence: 4/10
ai_collaboration_skill: 2/10
business_acumen: 3/10
current:
technical_confidence: 7/10 (+75%)
ai_collaboration_skill: 8/10 (+300%)
business_acumen: 5/10 (+67%)
growth_velocity:
fastest: "ai_collaboration"
steady: "technical"
needs_attention: "business"
key_inflection_points:
- day: 12
event: "首次独立部署"
impact: "技术自信心跃升"
- day: 28
event: "完成首笔销售"
impact: "商业模式验证"
- day: 45
event: "多 Agent 协作首次成功"
impact: "AI 协作能力质变"
Pattern Detection Algorithms
1. 重复模式检测
recurring_issues = detect_cycles(
data=journal_entries,
pattern_types=["technical_debt", "procrastination", "scope_creep"],
min_occurrences=3,
time_window="30_days"
)
2. 异常检测
anomalies = detect_anomalies(
data=journal_entries,
baseline=historical_patterns,
sensitivity="medium"
)
3. 预测模型
predictions = predict_challenges(
customer_id="OPC-001",
upcoming_milestones=["product_launch", "first_marketing_campaign"],
based_on_similar_customers=True
)
Weekly Pattern Report
自动生成的周模式报告:
# 📊 第 12 周模式报告 (Day 78-84)
## 工作节奏
本周您的工作时间分布:
Mon ████████░░ 4.2h (低于平均)
Tue ██████████████ 7.8h (正常)
Wed ████████████████████ 10.5h (高效日)
Thu ██████████████ 7.5h (正常)
Fri ██████████ 5.1h (提前结束)
Sat ████ 2.0h (周末工作)
Sun ██ 1.0h (规划)
**发现**: 周三仍然是您最高效的时段
**建议**: 将下周的重要会议安排在周三下午
## 决策回顾
本周做了 5 个重要决定:
- 3 个技术决策 (平均耗时 1.2 天) ← 比之前更快！
- 2 个业务决策 (平均耗时 3.5 天)
**模式**: 您在技术决策上变得更加果断
**建议**: 可以考虑建立业务决策的 SOP
## 情绪曲线
能量水平 (1-10):
Mon: 6 ████████░░
Tue: 7 █████████░░
Wed: 9 ████████████ 🌟
Thu: 6 ████████░░
Fri: 4 ██████░░░░ 😰
Sat: 7 █████████░░ 🎉
Sun: 8 ██████████░░
**观察**: 周五下午出现能量低谷，可能是周积累的压力
**建议**: 尝试在周五上午完成最重要的事，下午安排轻松任务
## 与 AI 的协作
- 主动委托任务: 12 次 (+50% vs 上周)
- 平均任务复杂度: 7.2/10 (↑ 说明您在挑战更难的事)
- AI 建议采纳率: 85% (↑ 信任度提升)
## 下周预测
基于您的模式，预测下周可能:
- 在周一早晨遇到启动困难 (概率 78%)
- 周三下午适合进行重要产品决策
- 可能需要在技术文档上寻求帮助
## 个性化建议
1. **利用周三**: 安排最重要的产品决策在周三下午
2. **预防周一低谷**: 周日晚上花 15 分钟规划周一任务
3. **扩展舒适区**: 您已准备好尝试更复杂的 AI 协作模式
Configuration
子 skill 配置继承自主 config.yml 的 pattern_recognition 部分。完整配置参见：
~/.openclaw/skills/opc-journal-suite/config.yml
pattern_recognition:
analysis_schedule:
daily: "22:00"
weekly: "sunday_20:00"
monthly: "last_day"
dimensions:
work_rhythm: { enabled: true }
decision_patterns: { enabled: true }
predictions:
enabled: true
lookahead_days: 7
Privacy Considerations
模式识别涉及敏感的个人行为数据:
- 数据最小化 - 只收集分析必需的数据
- 本地优先 - 原始数据存储在客户本地
- 脱敏共享 - 只有脱敏后的模式可用于比较
- 用户控制 - 用户可查看、导出、删除自己的模式数据
- 透明度 - 所有分析逻辑开源可审计
Best Practices
- 避免标签化 - 模式是参考，不是定义
- 关注变化 - 重点检测模式的变化而非模式本身
- 结合上下文 - 考虑外部因素（健康、家庭等）
- 提供行动 - 每个洞察都应有可执行的建议
- 尊重边界 - 不过度解读，保持专业距离
