Vis Lens Antipattern

Anti-Pattern Detection Visualization Lens

Philosophical Mode: Diagnostic Primary Question: "Which visualization anti-patterns are present?" Focus: Severity-Tiered Anti-Pattern Catalog, Evidence-Backed Findings, Remediation Guidance

Arguments

/autoskillit:vis-lens-antipattern [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

• Reviewing a figure plan or existing figures for visualization quality issues
• Pre-submission audit of all planned and existing figures
• Checking whether a specific anti-pattern (e.g., single random seed) is present
• Diagnosing why a reviewer rejected or criticized figure choices
• User invokes /autoskillit:vis-lens-antipattern

Anti-Pattern Catalog

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-antipattern/
• Skip checking figures that appear only in planning documents — anti-patterns in planned figures must be caught before implementation

ALWAYS:

• Check every identified figure against ALL 16 anti-patterns in the catalog

• Sort findings critical-first, then warning, then info

• Populate the anti_patterns field in each yaml:figure-spec with the IDs of matched patterns

• 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-antipattern/vis_spec_antipattern_{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-antipattern/vis_spec_antipattern_{...}.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: Scan for Chart Type and Visualization Clues

Scan experiment plan, context file, and codebase for evidence of chart type choices:

Code Patterns

• Look for: plot3D, bar3d, Axes3D → ap-3d-bar
• Look for: twinx, twin_y, secondary_y → ap-dual-axis
• Look for: cmap='jet', cmap='rainbow', cmap='hsv' → ap-rainbow
• Look for: seed =, np.random.seed, torch.manual_seed (single occurrence) → ap-single-seed
• Look for: ylim(0.9, ylim(0.8, non-zero bottom on bar axes → ap-truncated-bar
• Look for: radar, spider, polar plot → ap-spider-radar
• Look for: n_lines >= 5, many ax.plot calls in same axes → ap-spaghetti
• Look for: ax.bar without yerr or ax.errorbar → ap-bar-no-error
• Look for: smooth, rolling, savgol_filter, gaussian_filter on line data → ap-smoothed-hidden
• Look for: violinplot with n < 10 samples → ap-violin-small-n
• Look for: TSNE, t-SNE, tsne → ap-tsne-distance, ap-tsne-no-perplexity
• Look for: UMAP, umap, PCA single embedding → ap-embedding-single-seed
• Look for: plt.scatter with s= encoding data values → ap-area-encoding

Planning Document Patterns

• Look for descriptions like "3D bar", "dual axis", "radar chart", "single run"
• Look for baseline selection that seems hand-picked or unpublished

Step 2: Check Each Figure Against All Anti-Patterns

For each figure identified, create a finding record:

Figure: {figure_id}
Anti-patterns checked: all 16
Matches found:
  - {ap-id}: {evidence excerpt} → {severity}
  - {ap-id}: {evidence excerpt} → {severity}
Clean: [{ap-ids not found}]

Step 3: Build Severity-Sorted Finding List

Aggregate all findings and sort:

1. critical — must fix before submission
2. warning — should fix; reviewer will notice
3. info — consider fixing; minor perceptual improvement

For each critical finding, produce a one-line remediation instruction.

Step 4: Emit yaml:figure-spec Blocks and Mermaid Diagram

For each figure, emit one yaml:figure-spec fenced block with anti_patterns field populated with matched ap-* IDs. Then LOAD /autoskillit:mermaid and create the severity-bucketed diagram.

Output Template

# Anti-Pattern Detection Spec: {System / Experiment Name}

**Lens:** Anti-Pattern Detection (Diagnostic)
**Question:** Which visualization anti-patterns are present?
**Date:** {YYYY-MM-DD}
**Scope:** {What was analyzed}

## Findings Summary

| Severity | Count | Anti-Pattern IDs |
|----------|-------|-----------------|
| critical | {N} | {ap-ids} |
| warning | {N} | {ap-ids} |
| info | {N} | {ap-ids} |

## Detailed Findings

### CRITICAL: {ap-id} — {Name}

**Figure:** {figure_id}
**Evidence:** {code or planning doc excerpt}
**Remediation:** {one-line fix}

---

## Figure Specs

```yaml
# yaml:figure-spec — canonical schema (spec_version: "1.0")
figure_id: "fig-01-main-results"
figure_title: "Main Results"
spec_version: "1.0"
chart_type: "grouped-bar"
chart_type_fallback: "dot-plot"
perceptual_justification: "Position encoding; anti-patterns ap-3d-bar and ap-bar-no-error actively avoided."
data_source: "results/main.csv"
data_mapping:
  x: "method"
  y: "score"
  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: []
anti_patterns: ["ap-bar-no-error"]
palette: "wong"
format: "pdf"
target_dpi: 300
library: "matplotlib"
report_section: "Section 4"
priority: "P0"
placement_tier: "main"
conflicts: []
metadata:
  created_by: "vis-lens-antipattern"
  reviewed_by: ""
  last_updated: "{YYYY-MM-DD}"

Anti-Pattern Severity 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 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 Critical ["CRITICAL FINDINGS"]
        C1["{ap-id}<br/>━━━━━━━━━━<br/>{figure_id}"]
    end

    subgraph Warning ["WARNING FINDINGS"]
        W1["{ap-id}<br/>━━━━━━━━━━<br/>{figure_id}"]
    end

    subgraph Info ["INFO FINDINGS"]
        I1["{ap-id}<br/>━━━━━━━━━━<br/>{figure_id}"]
    end

    VERDICT["{Total: N critical, M warning}<br/>━━━━━━━━━━<br/>{overall assessment}"]

    C1 --> VERDICT
    W1 --> VERDICT
    I1 --> VERDICT

    class C1 detector;
    class W1 gap;
    class I1 handler;
    class VERDICT cli;

Color Legend:

---

## 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 16 anti-patterns were checked for each figure
- [ ] Findings are sorted critical-first
- [ ] Each yaml:figure-spec has `anti_patterns` field populated