Agent skill

decision-visualization

Decision-specific visualization skill for creating clear, actionable visual representations of analyses

View SKILL.md on GitHub Repository
Stars 514
Forks 31

Install this agent skill to your Project

npx add-skill https://github.com/a5c-ai/babysitter/tree/main/library/specializations/domains/business/decision-intelligence/skills/decision-visualization

Metadata

Additional technical details for this skill

domain
business
category
visualization
priority
high
specialization
decision-intelligence
tools libraries 
[
    "plotly",
    "bokeh",
    "matplotlib",
    "d3.js"
]

SKILL.md

Decision Visualization

Overview

The Decision Visualization skill provides specialized visualization capabilities for decision support, creating clear, actionable visual representations that communicate analysis results effectively to decision-makers and stakeholders.

Capabilities

  • Decision tree diagrams
  • Strategy tables and consequence matrices
  • Trade-off scatter plots
  • Value-of-information graphs
  • Confidence/uncertainty bands
  • Waterfall charts for sensitivity
  • Heat maps for MCDA
  • Interactive dashboards

Used By Processes

  • Executive Dashboard Development
  • Structured Decision Making Process
  • Multi-Criteria Decision Analysis (MCDA)
  • Decision Documentation and Learning

Usage

Decision Tree Visualization

python
# Decision tree diagram configuration
decision_tree_viz = {
    "type": "decision_tree",
    "data": decision_tree_structure,
    "options": {
        "node_shapes": {
            "decision": "square",
            "chance": "circle",
            "terminal": "triangle"
        },
        "show_probabilities": True,
        "show_payoffs": True,
        "highlight_optimal_path": True,
        "color_scheme": "sequential",
        "orientation": "horizontal"
    }
}

Strategy Table

python
# Strategy comparison table
strategy_table = {
    "type": "strategy_table",
    "alternatives": ["Strategy A", "Strategy B", "Strategy C"],
    "criteria": ["Cost", "Time", "Quality", "Risk"],
    "data": performance_matrix,
    "options": {
        "color_coding": "performance_based",
        "show_weights": True,
        "show_scores": True,
        "highlight_winner": True
    }
}

Trade-off Scatter Plot

python
# Multi-objective trade-off visualization
tradeoff_plot = {
    "type": "scatter",
    "data": alternatives_data,
    "x_axis": {"variable": "cost", "label": "Total Cost ($)"},
    "y_axis": {"variable": "benefit", "label": "Expected Benefit"},
    "options": {
        "show_pareto_frontier": True,
        "label_alternatives": True,
        "size_by": "probability",
        "color_by": "risk_category",
        "show_dominated_region": True
    }
}

Tornado Diagram

python
# Sensitivity tornado diagram
tornado = {
    "type": "tornado",
    "base_value": 1000000,
    "sensitivities": {
        "Price": {"low": 800000, "high": 1300000},
        "Volume": {"low": 900000, "high": 1150000},
        "Cost": {"low": 950000, "high": 1100000},
        "Market Share": {"low": 850000, "high": 1200000}
    },
    "options": {
        "sort_by": "swing",
        "show_base_line": True,
        "color_scheme": ["red", "green"],
        "show_values": True
    }
}

Uncertainty Visualization

python
# Distribution and confidence visualization
uncertainty_viz = {
    "type": "distribution",
    "data": simulation_results,
    "options": {
        "show_histogram": True,
        "show_density": True,
        "show_percentiles": [5, 25, 50, 75, 95],
        "show_mean": True,
        "confidence_band": 0.90,
        "highlight_threshold": 0  # e.g., breakeven
    }
}

Visualization Types

Type Use Case Key Features
Decision Tree Structure visualization Nodes, branches, payoffs
Strategy Table Alternative comparison Color-coded performance
Tornado Diagram Sensitivity ranking Horizontal bars, swing
Spider/Radar Multi-criteria profile Polygon overlay
Heat Map Matrix data Color intensity
Waterfall Value decomposition Sequential bars
Scatter Trade-offs Points, Pareto frontier
Box Plot Uncertainty Quartiles, outliers
Fan Chart Forecast uncertainty Widening confidence bands

Input Schema

json
{
  "visualization_type": "string",
  "data": "object",
  "axes": {
    "x": {"variable": "string", "label": "string"},
    "y": {"variable": "string", "label": "string"}
  },
  "options": {
    "title": "string",
    "color_scheme": "string",
    "interactive": "boolean",
    "annotations": ["object"],
    "export_format": "png|svg|pdf|html"
  }
}

Output Schema

json
{
  "visualization_path": "string",
  "interactive_url": "string (if applicable)",
  "metadata": {
    "type": "string",
    "dimensions": {"width": "number", "height": "number"},
    "data_summary": "object"
  },
  "accessibility": {
    "alt_text": "string",
    "data_table": "object"
  }
}

Design Principles

  1. Clarity: Remove chart junk, maximize data-ink ratio
  2. Accuracy: No distortion, appropriate scales
  3. Efficiency: Quick comprehension, key insights prominent
  4. Actionability: Clear implications for decisions
  5. Accessibility: Color-blind friendly, alt text provided

Best Practices

  1. Match visualization type to data and message
  2. Use consistent color schemes across related charts
  3. Include clear titles and axis labels
  4. Highlight key takeaways with annotations
  5. Provide interactive features for exploration
  6. Export to multiple formats for different uses
  7. Include data tables for accessibility

Integration Points

  • Receives data from all analysis skills
  • Feeds into Data Storytelling for narratives
  • Supports Executive Dashboard Development
  • Connects with Decision Journal for documentation

Recommended Agent Skills

Expand your agent's capabilities with these related and highly-rated skills.

a5c-ai/babysitter

gsd-tools

Central utility skill for GSD operations. Provides config parsing, slug generation, timestamps, path operations, and orchestrates calls to other specialized skills. Acts as the unified entry point that the original gsd-tools.cjs provided via its lib/ modules (commands, config, core, init).

514 31
Explore
a5c-ai/babysitter

model-profile-resolution

Resolve model profile (quality/balanced/budget) at orchestration start and map agents to specific models. Enables cost/quality tradeoffs by selecting appropriate AI models for each agent role.

514 31
Explore
a5c-ai/babysitter

verification-suite

Plan structure validation, phase completeness checks, reference integrity verification, and artifact existence confirmation. Provides the structured verification layer ensuring GSD artifacts are well-formed and complete.

514 31
Explore
a5c-ai/babysitter

state-management

STATE.md reading, writing, and field-level updates. Provides cross-session state persistence via .planning/STATE.md with structured fields for current task, completed phases, blockers, decisions, and quick tasks.

514 31
Explore
a5c-ai/babysitter

git-integration

Git commit patterns, formats, and conventions for GSD methodology. Provides atomic commits per task, structured commit messages, planning file commits, branch management, and milestone tag operations.

514 31
Explore
a5c-ai/babysitter

frontmatter-parsing

YAML frontmatter parsing and manipulation for .planning/ documents. Provides read, write, update, query, and validation operations on frontmatter blocks in GSD markdown artifacts.

514 31
Explore

Didn't find tool you were looking for?

Be as detailed as possible for better results