A visualization skill for building multi-criteria assessment charts using traffic lights, Harvey balls, and weighted scoring models.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install traffic-lights
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install traffic-lights using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Traffic Lights?
The traffic-lights skill is a powerful framework within Openclaw Skills designed to transform complex decision-making into intuitive visual dashboards. It enables developers and project managers to evaluate multiple options against diverse criteria using standardized symbols like RAG (Red, Amber, Green) status, Harvey balls, and trend arrows.
By synthesizing qualitative assessments into a structured Markdown format, this skill bridges the gap between raw data and executive-level insights. Whether you are conducting a vendor selection or a competitive analysis, it provides the visual vocabulary needed to highlight strengths, weaknesses, and overall rankings at a glance.
Traffic Lights Use Cases
- Competitive Comparison: Benchmarking your product against competitors across key feature sets.
- Option Evaluation: Comparing architectural alternatives or technology stacks for a new project.
- Status Dashboards: Monitoring project or portfolio health using standardized RAG reporting.
- Vendor Selection: Evaluating multiple suppliers based on weighted procurement requirements.
- Gap Analysis: Visualizing the distance between current capabilities and desired future states.
How Traffic Lights Works
- Define Criteria: Establish the specific dimensions and weights that will be used for the evaluation.
- Set Scale: Determine the visual language (Traffic Lights, Harvey Balls, or Arrows) and define what each symbol represents.
- Data Gathering: Assess every option against the established criteria consistently.
- Apply Ratings: Input the qualitative or quantitative ratings into the matrix template.
- Calculate Weighted Totals: (Optional) Use numerical scoring to derive an objective ranking based on criterion importance.
- Generate Visualization: Produce the final Markdown chart including a matrix, legend, and pattern analysis.
- Interpretation: Draw actionable conclusions and provide a final recommendation based on the visual patterns.
Traffic Lights Setup
To use this within your environment, ensure you have the Openclaw Skills framework initialized. You can trigger the skill by referencing the name or triggers in your prompt.
# Example of triggering the skill via CLI if applicable
openclaw run traffic-lights --args "Compare AWS vs Azure vs GCP"
No additional software installation is required as the skill generates standard Markdown tables and Unicode symbols compatible with any Markdown viewer.
Traffic Lights Data Schema & Taxonomy
The skill organizes information into a hierarchical structure to ensure data integrity and clarity. The following table describes the primary data components:
| Component | Format | Description |
|---|---|---|
| Assessment Criteria | Table | Defines criteria names, weights (%), and specific RAG definitions. |
| Matrix | Table | The primary visual grid containing options (columns) and criteria (rows). |
| Scoring Model | Table | Numerical mapping (1-3) multiplied by weights for quantitative ranking. |
| Pattern Analysis | List | Qualitative notes on observed strengths and weaknesses across options. |
| Recommendation | Text | Final choice rationale and trade-off documentation. |
Traffic Lights Advanced Features
- Multi-Symbol Support: Switch between RAG status for alerts, Harvey balls for degree-based ratings, and arrows for trend momentum.
- Weighted Scoring: Automated calculation of weighted totals to provide a mathematical foundation for recommendations.
- Pattern Recognition: Specialized sections for identifying cross-option trends and outliers.
- Quantitative Mapping: Ability to map visual icons to a 1-3 or 1-5 numerical scale for deeper analysis.
- Executive Summaries: Structured output designed for immediate use in professional reports and dashboards.
SKILL.md
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