Evaluate whether a task is worth converting into an AI Skill using a three-dimensional assessment model (Cognitive Friction, Process Structure, Reuse Value). Use when the user wants to decide if a workflow should be automated as a Skill, asks questions like "Should I make this a Skill?", "Is this worth automating?", or explicitly invokes with /assess-skill or /skill-eval. Provides guided questioning, automatic scoring, and concrete recommendations.
Resources
1Install
npx skillscat add qingchunwuhui/xianfengaiskills/skill-value-assessor Install via the SkillsCat registry.
Skill Value Assessor
Core Purpose
Help users make informed decisions about whether a manual workflow should be converted into an AI Skill by applying a structured three-dimensional assessment model.
When This Skill Is Used
This skill triggers when users:
- Ask if a task should be made into a Skill
- Want to evaluate automation value before investing time
- Need guidance on prioritizing Skill development
- Use commands like
/assess-skillor/skill-eval
Assessment Workflow
Phase 1: Task Understanding
First, clearly identify the task being evaluated:
- Extract the task description from user input
- Clarify the workflow if unclear:
- "Can you describe the steps you currently take manually?"
- "What makes this task difficult or time-consuming?"
Phase 2: Guided Three-Dimensional Assessment
Evaluate the task across three dimensions using guided questioning. Present one dimension at a time to avoid overwhelming the user.
Dimension A: Cognitive Friction (认知摩擦力)
"How mentally draining is this task?"
Present the scoring scale:
【维度 1/3:认知摩擦力】— 人脑有多抗拒这个任务?
1分 🟢 顺手就做,不费脑子
示例:回复"收到",简单的复制粘贴
2分 🟢 略微繁琐,但还好
示例:发送常规邮件
3分 🟡 需要停下来想一想,或查资料
示例:写复杂SQL,查API文档
4分 🟠 很烦,总想拖延,容易遗漏
示例:代码审查,写技术文档
5分 🔴 极度消耗脑力,让人心累
示例:全景盲点扫描,多语言翻译保持格式
基于你的描述 [复述任务],我初步判断可能是 [X] 分。
你同意吗?或者请告诉我你的实际感受:Scoring logic:
- If user provides a number (1-5), use it directly
- If user describes feelings, map to appropriate score:
- "easy", "simple", "quick" → 1-2
- "annoying", "tedious", "need to think" → 3
- "hate doing this", "always procrastinate" → 4
- "exhausting", "error-prone", "overwhelming" → 5
Dimension B: Process Structure (结构化程度)
"How clear and repeatable is the workflow?"
Present the scoring scale:
【维度 2/3:结构化程度】— 这个流程有多清晰?
1分 🔴 完全依赖灵感,每次都不一样
示例:写诗,画抽象画,创意设计
2分 🟠 有大致框架,但中间步骤模糊
示例:写读后感,整理书签(无固定分类标准)
3分 🟡 有框架,部分步骤需要判断
示例:写项目总结,代码重构
4分 🟢 步骤比较清晰,有明确的检查点
示例:根据模板写文档,格式化数据
5分 🟢 完全标准化,有明确的 if-then 逻辑
示例:提取摘要,格式化JSON,5W1H检查清单
关键问题:你能用"如果...那么..."的规则描述这个流程吗?
如果流程每次都差不多,且步骤明确 → 分数高
如果需要大量创意或每次都不同 → 分数低
基于 [任务描述],我认为结构化程度是 [X] 分。
你同意吗?或者描述一下你的流程:Scoring logic:
- Ask: "Can you describe the process as a series of if-then rules?"
- If workflow varies significantly each time → 1-2
- If there's a rough template but needs judgment → 3
- If steps are mostly clear with minor variations → 4
- If fully deterministic and can be written as checklist → 5
Critical check: If score is ≤2, proactively suggest:
⚠️ 注意:你的流程结构化程度较低([X]分)。
这可能意味着:
- 每次执行流程都需要创意或大量判断
- 难以用明确的步骤描述
建议:
• 如果可以先优化流程(建立标准化步骤),结构化程度可能提升到4-5分
• 如果流程本质上依赖创意,可能不适合做成Skill
是否需要我帮你分析如何优化流程?[Y/N]Dimension C: Reuse Value (复用价值)
"How often will this be used, and what's the cost of errors?"
Present the scoring scale:
【维度 3/3:复用价值】— 值得折腾吗?
1分 🔴 低频低值:一年做一次,做错了也无所谓
示例:年终总结的开头寒暄
2分 🟠 低频中值:偶尔需要,但不太重要
示例:清理临时文件
3分 🟡 高频低值:每天都做,但很简单
示例:整理桌面文件
4分 🟢 低频高值:不常做,但出错代价很大
示例:服务器部署配置,合同审核
5分 🟢 高频高值:每天都做,且直接影响产出质量
示例:代码提交规范检查,每日复盘,需求分析
请回答:
1. 这个任务多久做一次?(每天/每周/每月/偶尔)
2. 如果做错了,会有什么后果?(无所谓/有点麻烦/可能导致严重问题)
基于你的回答,我判断复用价值是 [X] 分。Scoring logic:
- Daily + high impact → 5
- Daily + low impact → 3
- Rare + high impact → 4
- Rare + low impact → 1-2
Phase 3: Calculation & Recommendation
After collecting all three scores:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
📊 评估结果汇总
任务:[任务名称]
维度评分:
A. 认知摩擦力:[X]/5 分
B. 结构化程度:[X]/5 分
C. 复用价值: [X]/5 分
总分:[XX]/15 分
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━Apply decision thresholds:
Score 12-15: 立即封装 (Must Have) ✅
🎯 判定:立即封装 (Must Have)
这是 AI Skill 的甜蜜区!
• 既能极大减轻你的负担
• AI 又能执行得很好
• 投资回报率高
💡 行动建议:
1. 马上创建 Skill(使用 /skill-creator)
2. 这将成为你的核心生产力工具
3. 建议在3个月后复盘:实际使用频率是否符合预期
📚 相似案例:
[匹配相似的高分案例]Score 8-11: 优化后封装 (Should Have) ⚠️
⚠️ 判定:优化后封装 (Should Have)
当前得分:[XX]/15
瓶颈分析:
[识别最低分的维度]
• 如果 B (结构化程度) 低:
→ 先优化流程,建立清晰的SOP
→ 手工执行2-3次验证步骤
→ 然后再考虑自动化
• 如果 C (复用价值) 低:
→ 观察1-2周,记录实际使用频率
→ 如果频率上升,重新评估
• 如果 A (认知摩擦力) 低:
→ 手动执行可能更快
→ 除非未来频率提高
💡 建议的优化路径:
[具体的优化建议]
优化后预计得分:[估算]Score 0-7: 保持人工 (Won't Do) ❌
❌ 判定:保持人工 (Won't Do)
原因分析:
[说明为什么不适合自动化]
• 如果任务太简单(A=1-2, C=1-2)
→ 手动更快,自动化是杀鸡用牛刀
• 如果流程不清晰(B=1-2)
→ AI无法处理高度依赖创意/直觉的任务
→ 除非能先标准化流程
• 如果频率太低(C=1-2)
→ 维护Skill的成本 > 节省的时间
💡 替代方案:
[建议其他工具或方法]Phase 4: Case Matching (Optional Enhancement)
Load references/case_library.md to find similar cases:
📚 相似案例参考:
你的任务与以下案例相似:
案例:[案例名称]
评分:[X+X+X = XX分]
判定:[结果]
关键学习:[一句话总结]
查看详情 → [[references/case_library.md#案例名称]]Phase 5: Save Assessment (Optional)
Ask user if they want to save this assessment:
💾 是否保存此次评估记录?
保存后可以:
• 季度复盘时查看决策模式
• 追踪哪些高分Skill实际有用
• 避免重复评估相似任务
[Y] 保存到评估历史
[N] 不保存
如果选择 Y,将记录保存到评估历史文件。If user confirms, use the template from references/assessment_template.md to create/append to the assessment history file.
Key Principles
- One dimension at a time - Don't overwhelm with all three dimensions at once
- Proactive suggestions - If you notice red flags (e.g., low structure score), suggest optimizations
- Concrete examples - Use examples from case library to help calibration
- Clear thresholds - Apply the 12/8/7 scoring thresholds consistently
- Context-aware - If the task description is vague, ask clarifying questions before scoring
Reference Materials
- Detailed evaluation model →
references/evaluation_model.md - Case library →
references/case_library.md - Assessment template →
references/assessment_template.md
Load these references as needed during the assessment process.
Anti-Patterns to Avoid
❌ Don't ask all three dimensions simultaneously
❌ Don't give scores without explanation
❌ Don't skip the recommendation phase
❌ Don't ignore low structure scores (proactively suggest optimization)
❌ Don't force users to use exact numbers (accept descriptions)