Vis Lens Chart Select

Chart Type Selection Visualization Lens

Philosophical Mode: Typological Primary Question: "Which chart type is perceptually optimal for this data?" Focus: Encoding Channel Assignments, Cleveland-McGill Perceptual Hierarchy, Data-Type → Chart-Type Matrix

Arguments

/autoskillit:vis-lens-chart-select [context_path] [experiment_plan_path]

• context_path (optional positional arg 1) — Absolute path to a lens context file containing IV/DV tables, H0/H1 hypotheses, controlled variables, and success criteria. If provided, read this file before beginning analysis to obtain structured context. If omitted, discover context by exploring the CWD.
• experiment_plan_path (optional positional arg 2) — Absolute path to the full experiment plan. If provided, read for complete experimental methodology and design. If omitted, locate the experiment plan by exploring the CWD.

When to Use

• Selecting chart types for ML results (accuracy tables, loss curves, ablations)
• Deciding encoding channels (position, length, color, size, angle) for each variable
• Reviewing figure plans before implementation to catch perceptually suboptimal choices
• Building a figure plan from scratch and wanting principled chart-type assignments
• User invokes /autoskillit:vis-lens-chart-select

yaml:figure-spec Schema

Canonical schema definition for a single figure planning specification:

# yaml:figure-spec — canonical schema (spec_version: "1.0")
figure_id: str               # unique slug, e.g. "fig-01-main-accuracy"
figure_title: str            # human-readable title
spec_version: "1.0"          # schema version; increment on breaking change
chart_type: str              # CONTROLLED VOCAB (see below) — excludes "radar" and "pie"
chart_type_fallback: str     # secondary chart if primary unavailable
perceptual_justification: str  # Cleveland-McGill rank or encoding channel rationale
data_source: str             # variable or file that feeds this figure
data_mapping:
  x: str                     # x-axis variable / encoding
  y: str                     # y-axis variable / encoding
  color: str                 # color encoding (optional)
  size: str                  # size encoding (optional)
  facet: str                 # facet/panel variable (optional)
layout:
  width_inches: float
  height_inches: float
  dpi: int
stat_overlay:
  type: str                  # "error_bar" | "ci_band" | "violin" | "none"
  measure: str               # "SD" | "SE" | "CI95" | "PI95"
  n_seeds: int               # number of random seeds used
annotations: list[str]       # text annotations to include
anti_patterns: list[str]     # anti-pattern IDs being actively avoided (ap-* codes)
palette: str                 # colorblind-safe palette name, e.g. "wong", "okabe-ito"
format: str                  # "svg" | "png" | "pdf"
target_dpi: int              # 300 for publication, 150 for slides
library: str                 # "matplotlib" | "seaborn" | "plotly" | "ggplot2" | "vega"
report_section: str          # section of the paper/report this figure appears in
priority: str                # "P0" | "P1" | "P2"
placement_tier: str          # "main" | "appendix" | "supplementary"
conflicts: list[str]         # figure_ids this conflicts with (same data, different view)
metadata:
  created_by: str
  reviewed_by: str
  last_updated: str          # ISO date

Controlled chart_type vocabulary (radar and pie are excluded):

• bar, grouped-bar, stacked-bar
• line, scatter, scatter-matrix
• box, violin, strip
• heatmap, histogram, kde, ecdf
• forest-plot, dot-plot, bubble
• area, ribbon, step
• parallel-coordinates, table, matrix

Critical Constraints

NEVER:

• Modify any source code files
• Do not litter the codebase with useless comments, TODO markers, or explanatory annotations — the skill output and diagram speak for themselves
• Create files outside {{AUTOSKILLIT_TEMP}}/vis-lens-chart-select/
• Use radar or pie chart types — these are perceptually inferior and excluded from the controlled vocabulary

ALWAYS:

• Apply the Cleveland-McGill perceptual hierarchy when ranking chart alternatives: position > length > angle > area > color saturation > color hue

• Assign explicit encoding channels (x, y, color, size, facet) for every figure variable

• Document why alternatives were rejected using the perceptual rank

• Use colorblind-safe palettes (wong, okabe-ito, viridis, cividis)

• BEFORE creating any diagram, LOAD the /autoskillit:mermaid skill using the Skill tool - this is MANDATORY

• If the Skill tool cannot be used (disable-model-invocation) or refuses this invocation, do NOT proceed with diagram creation. Abort this step and omit the diagram from output.

• Write output to {{AUTOSKILLIT_TEMP}}/vis-lens-chart-select/vis_spec_chart_select_{YYYY-MM-DD_HHMMSS}.md (relative to the current working directory)

• After writing the file, emit the structured output token as literal plain text with no markdown formatting on the token name (the adjudicator performs a regex match):

  diagram_path = /absolute/path/to/{{AUTOSKILLIT_TEMP}}/vis-lens-chart-select/vis_spec_chart_select_{...}.md
  

Analysis Workflow

Step 0: Parse optional arguments

If positional arg 1 (context_path) is provided and the file exists, read it to obtain IV/DV tables, H0/H1 hypotheses, controlled variables, and success criteria. If positional arg 2 (experiment_plan_path) is provided and exists, read the experiment plan for full methodology. Use this structured context as the foundation for Steps 1–4; skip the CWD exploration for these fields if the context file supplies them.

Step 1: Parallel Exploration

Spawn parallel exploration tasks to investigate:

Existing Figure Inventory

• Find all existing figures, plots, and visualizations in the project
• Look for: fig, figure, plot, chart, image, png, svg, pdf, matplotlib, seaborn, plotly

Data Types and Variables

• Find all variables, metrics, and data fields to be visualized
• Look for: accuracy, loss, metric, score, embedding, distribution, time, epoch

Current Chart Choices

• Find existing chart-type decisions in code, config, or planning docs
• Look for: bar_plot, scatter, line_chart, heatmap, histogram, boxplot, violinplot

Encoding Channel Usage

• Find existing axis assignments, color mappings, size mappings
• Look for: xlabel, ylabel, hue, color, size, marker, alpha, facet

Step 2: Build the Data-Type → Chart-Type Matrix

For each figure slot identified:

1. Classify the data type of each variable: nominal, ordinal, quantitative-discrete, quantitative-continuous, temporal
2. Classify the relationship to visualize: comparison, distribution, composition, relationship, change-over-time
3. Apply the data-type × relationship matrix to identify candidate chart types
4. Assign encoding channels for all variables: primary (x/y position), secondary (color, size), tertiary (facet, shape)

Step 3d: Perceptual Rank

For each figure slot, rank the candidate chart types by Cleveland-McGill position:

1. Position (highest accuracy): bar, scatter, line, dot-plot — use whenever the data allows
2. Length: bar (horizontal) — good for labeled categories
3. Angle: avoid unless no positional alternative exists
4. Area: bubble, scatter (size encoding) — document the Stevens power law limitation (~0.7)
5. Color saturation: heatmap — acceptable for matrix data where position is already used
6. Color hue (lowest accuracy): nominal encoding only — never encode quantitative data with hue alone

Document the chosen rank for each figure and explicitly state why alternatives were rejected (e.g., "violin rejected: n < 10 → use strip plot; ecdf preferred over histogram: no bin-width sensitivity").

Step 4: Emit Specs and Diagram

For each figure, emit one yaml:figure-spec fenced block. Then LOAD /autoskillit:mermaid and create the mermaid diagram showing the data-type → chart-type assignment flow.

Output Template

# Chart Type Selection Spec: {System / Experiment Name}

**Lens:** Chart Type Selection (Typological)
**Question:** Which chart type is perceptually optimal for this data?
**Date:** {YYYY-MM-DD}
**Scope:** {What was analyzed}

## Figure Specs

```yaml
# yaml:figure-spec — canonical schema (spec_version: "1.0")
figure_id: "fig-01-main-accuracy"
figure_title: "Main Results: Accuracy by Method"
spec_version: "1.0"
chart_type: "grouped-bar"
chart_type_fallback: "dot-plot"
perceptual_justification: "Position encoding (Cleveland-McGill rank 1) for nominal × quantitative comparison; grouped-bar preferred over dot-plot for direct label alignment."
data_source: "results/main_results.csv"
data_mapping:
  x: "method"
  y: "accuracy"
  color: "dataset"
  size: ""
  facet: ""
layout:
  width_inches: 6.5
  height_inches: 4.0
  dpi: 300
stat_overlay:
  type: "error_bar"
  measure: "CI95"
  n_seeds: 5
annotations: ["Baseline at 0.72", "Best result starred"]
anti_patterns: ["ap-3d-bar", "ap-bar-no-error"]
palette: "wong"
format: "pdf"
target_dpi: 300
library: "matplotlib"
report_section: "Section 4.1 Main Results"
priority: "P0"
placement_tier: "main"
conflicts: []
metadata:
  created_by: "vis-lens-chart-select"
  reviewed_by: ""
  last_updated: "{YYYY-MM-DD}"

Chart Type Assignment Diagram

%%{init: {'flowchart': {'nodeSpacing': 50, 'rankSpacing': 60, 'curve': 'basis'}}}%%
flowchart TB
    %% CLASS DEFINITIONS %%
    classDef cli fill:#1a237e,stroke:#7986cb,stroke-width:2px,color:#fff;
    classDef stateNode fill:#004d40,stroke:#4db6ac,stroke-width:2px,color:#fff;
    classDef handler fill:#e65100,stroke:#ffb74d,stroke-width:2px,color:#fff;
    classDef phase fill:#6a1b9a,stroke:#ba68c8,stroke-width:2px,color:#fff;
    classDef newComponent fill:#2e7d32,stroke:#81c784,stroke-width:2px,color:#fff;
    classDef output fill:#00695c,stroke:#4db6ac,stroke-width:2px,color:#fff;
    classDef detector fill:#b71c1c,stroke:#ef5350,stroke-width:2px,color:#fff;
    classDef gap fill:#ff6f00,stroke:#ffa726,stroke-width:2px,color:#000;

    subgraph DataTypes ["DATA TYPES"]
        DT1["{Variable 1}<br/>━━━━━━━━━━<br/>nominal"]
        DT2["{Variable 2}<br/>━━━━━━━━━━<br/>quantitative-continuous"]
    end

    subgraph Relationship ["RELATIONSHIP"]
        R1["{Comparison}<br/>━━━━━━━━━━<br/>nominal × quantitative"]
    end

    subgraph ChartType ["CHART TYPE SELECTED"]
        CT1["{grouped-bar}<br/>━━━━━━━━━━<br/>Cleveland-McGill: position rank 1"]
    end

    subgraph Encoding ["ENCODING CHANNELS"]
        E1["x: {method}<br/>━━━━━━━━━━<br/>position (primary)"]
        E2["y: {accuracy}<br/>━━━━━━━━━━<br/>position (primary)"]
        E3["color: {dataset}<br/>━━━━━━━━━━<br/>hue (nominal only)"]
    end

    DT1 --> R1
    DT2 --> R1
    R1 --> CT1
    CT1 --> E1
    CT1 --> E2
    CT1 --> E3

    class DT1,DT2 stateNode;
    class R1 phase;
    class CT1 cli;
    class E1,E2 output;
    class E3 handler;

Color Legend:

Perceptual Rank Summary

---

## Pre-Diagram Checklist

Before creating the diagram, verify:

- [ ] LOADED `/autoskillit:mermaid` skill using the Skill tool
- [ ] Using ONLY classDef styles from the mermaid skill (no invented colors)
- [ ] Diagram will include a color legend table
- [ ] All chart types are from the controlled vocabulary (no radar, no pie)
- [ ] Each figure spec has `perceptual_justification` filled in