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Traffic Lights

Create multi-criteria comparison charts using traffic lights or Harvey balls. Use for option evaluation, competitive comparison, and executive dashboards.
使用红绿灯或哈维球制作多标准对比图,用于方案评估、竞品对比和高管仪表盘。
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未分类 clawhub v1.0.0 1 版本 99729.7 Key: 无需
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概述

Traffic Light Charts

Metadata

  • Name: traffic-lights
  • Description: Multi-criteria assessment visualization
  • Triggers: traffic light, harvey ball, stoplight, RAG status, multi-criteria

Instructions

You are creating a traffic light chart to evaluate $ARGUMENTS.

Your task is to compare options across multiple criteria using a simple, visual format.

Framework

Visual Options

Traffic Lights (RAG)

🟢 Green = Good / On track / Above target
🟡 Yellow/Amer = Caution / At risk / Near target
🔴 Red = Bad / Off track / Below target

Harvey Balls (Half-moons)

○  Empty    = 0% / None / Very poor
◔  Quarter  = 25% / Below average
◑  Half     = 50% / Average
◕  Three-Q  = 75% / Above average
●  Full     = 100% / Excellent

Arrows

↑↑ Strong positive
↑   Positive
→   Neutral
↓   Negative
↓↓  Strong negative

When to Use Which

Chart TypeBest For
----------------------
Traffic LightsStatus, progress, alerts
Harvey BallsGradual comparison, rating
ArrowsTrends, momentum
StarsCustomer ratings, reviews
NumbersPrecision needed

Standard Applications

  1. Competitive Comparison - Us vs. competitors on key criteria
  2. Option Evaluation - Compare alternatives for decision
  3. Status Dashboard - Project/portfolio health
  4. Gap Analysis - Current vs. desired state
  5. Vendor Selection - Compare suppliers on requirements

Output Process

  1. Define criteria - What dimensions matter?
  2. Set scale - What does each color/symbol mean?
  3. Gather data - Assess each option on each criterion
  4. Apply ratings - Consistent methodology
  5. Calculate overall - Summary score
  6. Visualize - Create the chart
  7. Annotate - Add context and insights
  8. Interpret - Draw conclusions

Output Format

## Traffic Light Chart: [Subject]

### Assessment Criteria

| # | Criterion | Weight | Definition |
|---|-----------|--------|------------|
| 1 | [Criterion 1] | 20% | 🟢=X, 🟡=Y, 🔴=Z |
| 2 | [Criterion 2] | 15% | 🟢=X, 🟡=Y, 🔴=Z |
| 3 | [Criterion 3] | 25% | 🟢=X, 🟡=Y, 🔴=Z |
| 4 | [Criterion 4] | 15% | 🟢=X, 🟡=Y, 🔴=Z |
| 5 | [Criterion 5] | 10% | 🟢=X, 🟡=Y, 🔴=Z |
| 6 | [Criterion 6] | 15% | 🟢=X, 🟡=Y, 🔴=Z |
|   | **Total** | **100%** | |

---

### Traffic Light Matrix

| Criterion | Option A | Option B | Option C | Option D |
|-----------|----------|----------|----------|----------|
| **1. [Criterion 1]** | 🟢 | 🟡 | 🟢 | 🔴 |
| **2. [Criterion 2]** | 🟡 | 🟢 | 🟡 | 🟡 |
| **3. [Criterion 3]** | 🟢 | 🔴 | 🟢 | 🟢 |
| **4. [Criterion 4]** | 🟢 | 🟢 | 🟡 | 🟡 |
| **5. [Criterion 5]** | 🟡 | 🟢 | 🔴 | 🟢 |
| **6. [Criterion 6]** | 🔴 | 🟡 | 🟢 | 🟡 |
| **OVERALL** | 🟢 | 🟡 | 🟢 | 🟡 |

**Legend:**
- 🟢 Green = Strong / Meets requirements
- 🟡 Yellow = Moderate / Partially meets
- 🔴 Red = Weak / Does not meet

---

### Scoring (Optional Quantitative)

| Criterion | Weight | Option A | Option B | Option C | Option D |
|-----------|--------|----------|----------|----------|----------|
| 1. [Criterion] | 20% | 3 (0.6) | 2 (0.4) | 3 (0.6) | 1 (0.2) |
| 2. [Criterion] | 15% | 2 (0.3) | 3 (0.45) | 2 (0.3) | 2 (0.3) |
| 3. [Criterion] | 25% | 3 (0.75) | 1 (0.25) | 3 (0.75) | 3 (0.75) |
| 4. [Criterion] | 15% | 3 (0.45) | 3 (0.45) | 2 (0.3) | 2 (0.3) |
| 5. [Criterion] | 10% | 2 (0.2) | 3 (0.3) | 1 (0.1) | 3 (0.3) |
| 6. [Criterion] | 15% | 1 (0.15) | 2 (0.3) | 3 (0.45) | 2 (0.3) |
| **WEIGHTED TOTAL** | **100%** | **2.45** | **2.15** | **2.50** | **2.15** |
| **RANK** | | **2nd** | **3rd** | **1st** | **3rd** |

*Scale: 1=Red, 2=Yellow, 3=Green*

---

### Alternative: Harvey Ball Format

| Criterion | Option A | Option B | Option C | Option D |
|-----------|----------|----------|----------|----------|
| **1. [Criterion]** | ● | ◑ | ● | ◔ |
| **2. [Criterion]** | ◑ | ● | ◑ | ◑ |
| **3. [Criterion]** | ● | ○ | ● | ◑ |
| **4. [Criterion]** | ● | ● | ◑ | ◑ |
| **5. [Criterion]** | ◑ | ● | ◔ | ● |
| **6. [Criterion]** | ◔ | ◑ | ● | ◑ |
| **OVERALL** | ◕ | ◑ | ● | ◑ |

**Legend:**
- ○ None (0%) | ◔ Quarter (25%) | ◑ Half (50%) | ◕ Three-Q (75%) | ● Full (100%)

---

### Pattern Analysis

**Strengths by Option**

| Option | Key Strengths | Key Weaknesses |
|--------|---------------|----------------|
| Option A | [Criterion 1, 3, 4] | [Criterion 6] |
| Option B | [Criterion 2, 4, 5] | [Criterion 3] |
| Option C | [Criterion 1, 3, 6] | [Criterion 5] |
| Option D | [Criterion 1, 3, 5] | [Criterion 1] |

**Patterns Observed**
1. [Pattern 1 - e.g., "All options score well on Criterion 4"]
2. [Pattern 2 - e.g., "Criterion 3 shows largest differentiation"]
3. [Pattern 3 - e.g., "No option scores green on all criteria"]

---

### Recommendation

**Top Choice: [Option C]**
- Rationale: [Why this option]
- Trade-offs: [What we give up]

**Runner-up: [Option A]**
- When to consider: [Situations where this is better]

**Not Recommended: [Option D]**
- Why not: [Key deficiencies]

Tips

  • Define criteria before rating - don't retrofit
  • Be consistent - same assessor or calibrated team
  • Don't have too many criteria (6-10 is optimal)
  • Weight criteria by importance
  • Use supporting data in appendix
  • The overall score should be a guide, not a rule
  • Patterns matter more than individual cells
  • Document the rationale for each rating

References

  • Few, Stephen. Information Dashboard Design. 2006.
  • Tufte, Edward. Beautiful Evidence. 2006.

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  • v1.0.0 当前
    2026-03-31 06:54 安全 安全

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