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Get Started Free →分析健康目标数据、识别目标模式、评估目标进度,并提供个性化目标管理建议。支持与营养、运动、睡眠等健康数据的关联分析。
.claude/skills/goal-analyzer/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-15 | ✗→✓ | ▲ Improved | — | — |
| case-18 | ✗→✓ | ▲ Improved | — | — |
| case-05 | ✗→✓ | ▲ Improved | — | — |
| case-01 | ✗→✓ | ▲ Improved | — | — |
| case-11 | ✗→✓ | ▲ Improved | — | — |
分析健康目标数据,识别目标模式和进度,评估目标达成情况,并提供个性化目标管理建议。
验证设定的新目标是否符合SMART原则。
验证维度:
输出:
示例评估:
json{ "goal": "6个月内减重5公斤", "smart_scores": { "specific": 5, "measurable": 5, "achievable": 4, "relevant": 5, "time_bound": 5 }, "overall_score": 4.8, "grade": "A", "assessment": "优秀的SMART目标", "suggestions": [ "建议设定阶段性里程碑(每2个月减重1.5-2公斤)", "建议配合运动计划和饮食调整" ] }
追踪和分析目标的完成进度。
追踪内容:
输出:
进度评级:
分析习惯的养成情况和连续性。
分析内容:
习惯养成阶段:
输出:
示例分析:
json{ "habit": "morning-stretch", "current_streak": 21, "longest_streak": 21, "completion_rate": 95.2, "strength_score": 7.5, "stage": "巩固期", "assessment": "习惯即将形成,继续保持!", "next_milestone": 30, "suggestions": [ "继续保持,即将达到30天里程碑", "可以尝试添加新的相关习惯" ] }
评估和管理用户的动机水平。
评估内容:
动机提升策略:
输出:
激励建议示例:
管理基础成就系统的解锁和进度。
成就类型:
成就追踪:
输出:
识别阻碍目标达成的因素,提供解决方案。
障碍类型:
输出:
将健康目标与其他健康数据进行关联分析。
关联维度:
分析方法:
输出:
示例关联:
json{ "goal": "weight-loss", "correlations": [ { "factor": "daily_calories", "correlation": -0.75, "strength": "强负相关", "insight": "每日卡路里摄入与减重进度呈强负相关,降低摄入加速进度" }, { "factor": "exercise_frequency", "correlation": 0.68, "strength": "强正相关", "insight": "运动频率与减重进度呈强正相关,建议保持每周4次以上" }, { "factor": "sleep_duration", "correlation": 0.45, "strength": "中等正相关", "insight": "睡眠时长影响减重,建议保证7-8小时睡眠" } ], "recommendations": [ "重点控制卡路里摄入,保持当前运动频率", "优化睡眠时长,以提升减重效果" ] }
生成包含ECharts图表的HTML交互式报告。
报告类型:
报告特点:
ECharts图表配置:
javascript// 进度趋势折线图 { type: 'line', xAxis: { type: 'category', data: ['1月', '2月', '3月', ...] }, yAxis: { type: 'value', name: '完成%' }, series: [{ name: '目标进度', type: 'line', data: [0, 15, 35, 50, 70, 85, 100], smooth: true, markLine: { data: [{ yAxis: 50, name: '50%里程碑' }] } }] } // 习惯热图 { type: 'heatmap', xAxis: { type: 'category', data: ['周一', '周二', ...] }, yAxis: { type: 'category', data: ['第1周', '第2周', ...] }, visualMap: { min: 0, max: 1, inRange: { color: ['#ebedf0', '#216e39'] } }, series: [{ type: 'heatmap', data: [[0, 0, 1], [1, 0, 1], [2, 0, 0], ...] }] } // 目标达成率环形图 { type: 'pie', radius: ['50%', '70%'], series: [{ type: 'pie', radius: ['50%', '70%'], data: [ { value: 70, name: '已完成' }, { value: 30, name: '未完成' } ], label: { formatter: '{b}: {c}%' } }] }
输出:
极端目标警告:
不健康行为迹象:
转介建议:
markdown# 健康目标分析报告 ## 目标概览 - 目标: 6个月内减重5公斤 - 开始日期: 2025-01-01 - 目标日期: 2025-06-30 - 当前日期: 2025-03-20 ## SMART评估 - 具体性: ⭐⭐⭐⭐⭐ (5/5) - 可衡量性: ⭐⭐⭐⭐⭐ (5/5) - 可实现性: ⭐⭐⭐⭐ (4/5) - 相关性: ⭐⭐⭐⭐⭐ (5/5) - 有时限: ⭐⭐⭐⭐⭐ (5/5) **总体评分: A (4.8/5)** ## 进度分析 - 当前进度: 70% - 已完成: 3.5公斤 / 5.0公斤 - 时间进度: 27% (79天/180天) - 进度评级: 🟢 优秀 (进度超前) ### 趋势分析 - 平均速度: 0.77公斤/月 - 预计完成: 2025-05-20 (提前40天) - 进度趋势: 稳定上升 ## 习惯追踪 ### 早上拉伸习惯 - 当前连续: 21天 🔥 - 历史最长: 21天 - 完成率: 95.2% - 习惯阶段: 巩固期 - 下一个里程碑: 30天 ⭐ ## 动机评估 - 当前动机: 8/10 - 动机趋势: 稳定 - 动机状态: 良好 ## 数据关联分析 ### 强相关因素(影响度>60%) 1. 每日卡路里摄入 (负相关 -0.75) 2. 每周运动频次 (正相关 +0.68) 3. 睡眠时长 (正相关 +0.45) ### 建议 - 保持当前卡路里摄入水平 - 继续保持每周4次运动频率 - 优化睡眠时长至7-8小时 ## 障碍识别 主要障碍: 社交活动饮食控制 解决方案: - 社交活动前提前规划饮食 - 选择健康餐厅 - 适量控制份量 ## 成就解锁 🔥 连续21天 - 早上拉伸习惯达成! 🎯 半程达成 - 减重目标完成50%! ## 下一步行动 1. 保持当前进度 2. 关注社交活动饮食控制 3. 继续养成早操习惯 4. 准备达成30天里程碑
data-example/health-goals-tracker.jsondata-example/health-goals-logs/YYYY-MM/YYYY-MM-DD.jsondata-example/nutrition-tracker.json, fitness-tracker.json 等(current_value / target_value) * 100(days_elapsed / total_days) * 100(completed_days / total_days) * 100pythondef validate_smart_goal(goal): scores = { 'specific': check_specificity(goal), 'measurable': check_measurability(goal), 'achievable': check_achievability(goal), 'relevant': check_relevance(goal), 'time_bound': check_time_bound(goal) } overall = sum(scores.values()) / len(scores) grade = get_grade(overall) return scores, overall, grade
使用此技能时,始终优先考虑用户的健康和安全!
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-14 | pass→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-15 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-18 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-05 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-04 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-03 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-01 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-13 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-11 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-09 | pass→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-02 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-22 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-20 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-07 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-17 | pass→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-19 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-06 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-16 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-10 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-08 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-12 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-21 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted. The headline lift of +50 percentage points is the difference between those two pass rates over the 22 comparable cases.
The per-case answers from this run were removed by the retention sweep, so the case table below shows the verdicts without the text either arm produced. The counts above were recorded at the time and are unaffected. Answers are now kept for 180 days.
Other measured skills in the registry, with their headline benchmark lift.