qingchunwuhui

skill-value-assessor

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.

qingchunwuhui 0 Updated 6mo ago

Resources

1
GitHub

Install

npx skillscat add qingchunwuhui/xianfengaiskills/skill-value-assessor

Install via the SkillsCat registry.

About this skill

This skill evaluates whether a manual workflow should be converted into an AI Skill by assessing cognitive friction, process structure, and reuse value. It helps users decide if automation is worthwhile before investing development time. Use it when someone questions whether a task should become a Skill or when prioritizing which workflows to automate.

SKILL.md

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-skill or /skill-eval

Assessment Workflow

Phase 1: Task Understanding

First, clearly identify the task being evaluated:

  1. Extract the task description from user input
  2. 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

  1. One dimension at a time - Don't overwhelm with all three dimensions at once
  2. Proactive suggestions - If you notice red flags (e.g., low structure score), suggest optimizations
  3. Concrete examples - Use examples from case library to help calibration
  4. Clear thresholds - Apply the 12/8/7 scoring thresholds consistently
  5. Context-aware - If the task description is vague, ask clarifying questions before scoring

Reference Materials

  • Detailed evaluation modelreferences/evaluation_model.md
  • Case libraryreferences/case_library.md
  • Assessment templatereferences/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)