健康目标分析器技能 workflow skill. Use this skill when the user needs 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。 and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
This public intake copy packages plugins/antigravity-awesome-skills/skills/goal-analyzer from https://github.com/sickn33/antigravity-awesome-skills into the native Omni Skills editorial shape without hiding its origin.
Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.
This intake keeps the copied upstream files intact and uses EXTERNAL_SOURCE.json plus ORIGIN.md as the provenance anchor for review.
Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: 功能, 医学安全边界, 输出格式, 目标概览, SMART评估, 进度分析.
Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.
| Situation | Start here | Why it matters |
|---|---|---|
| First-time use | EXTERNAL_SOURCE.json | Confirms repository, branch, commit, and imported path before touching the copied workflow |
| Provenance review | ORIGIN.md | Gives reviewers a plain-language audit trail for the imported source |
| Workflow execution | SKILL.md | Starts with the smallest copied file that materially changes execution |
| Supporting context | SKILL.md | Adds the next most relevant copied source file without loading the entire package |
| Handoff decision | ## Related Skills | Helps the operator switch to a stronger native skill when the task drifts |
This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.
验证设定的新目标是否符合SMART原则。
验证维度:
Specific(具体性)
Measurable(可衡量性)
Achievable(可实现性)
Relevant(相关性)
Time-bound(有时限)
输出:
示例评估:
{
"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:
当前进度
时间进度
速度分析
趋势识别
输出:
进度评级:
分析习惯的养成情况和连续性。
分析内容:
连续天数追踪
完成率统计
习惯强度评估
习惯模式识别
习惯养成阶段:
输出:
示例分析:
{
"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:
动机评分追踪
动机因素分析
动机低谷识别
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:
目标相关成就
习惯相关成就
综合成就
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:
时间障碍
动机障碍
环境障碍
能力障碍
身体障碍
输出:
将健康目标与其他健康数据进行关联分析。
关联维度:
减重目标关联
运动目标关联
饮食目标关联
睡眠目标关联
分析方法:
输出:
示例关联:
{
"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:
// 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:
Use @goal-analyzer-v2 to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.
Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.
Review @goal-analyzer-v2 against EXTERNAL_SOURCE.json and ORIGIN.md, then explain which copied upstream files you would load first and why.
Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.
Use @goal-analyzer-v2 for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.
Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.
Review @goal-analyzer-v2 using the copied upstream files plus provenance, then summarize any gaps before merge.
Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.
Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.
Symptoms: The result ignores the upstream workflow in plugins/antigravity-awesome-skills/skills/goal-analyzer, fails to mention provenance, or does not use any copied source files at all.
Solution: Re-open EXTERNAL_SOURCE.json, ORIGIN.md, and the most relevant copied upstream files. Load only the files that materially change the answer, then restate the provenance before continuing.
Symptoms: Reviewers can see the generated SKILL.md, but they cannot quickly tell which references, examples, or scripts matter for the current task.
Solution: Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it.
Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.
@game-design-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.@gdb-cli-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.@gdpr-data-handling-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.@gemini-api-dev-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.
| Resource family | What it gives the reviewer | Example path |
|---|---|---|
references | copied reference notes, guides, or background material from upstream | references/n/a |
examples | worked examples or reusable prompts copied from upstream | examples/n/a |
scripts | upstream helper scripts that change execution or validation | scripts/n/a |
agents | routing or delegation notes that are genuinely part of the imported package | agents/n/a |
assets | supporting assets or schemas copied from the source package | assets/n/a |
✅ 辅助设定健康目标
✅ 追踪和分析目标进度
✅ 识别健康行为模式
✅ 提供一般性健康改善建议
✅ 生成可视化报告
❌ 不提供医疗诊断
❌ 不开具治疗处方
❌ 不替代专业医疗建议
❌ 不处理进食障碍或强迫行为
极端目标警告:
不健康行为迹象:
转介建议:
# 健康目标分析报告
#### Imported: 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
#### Imported: 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)**
#### Imported: 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
#### Imported: 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 ⭐
#### Imported: 动机评估
- 当前动机: 8/10
- 动机趋势: 稳定
- 动机状态: 良好
#### Imported: 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
#### Imported: 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
#### Imported: 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%!
#### Imported: 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) * 100def 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!