From thinking-frameworks-skills
Audits charts, dashboards, reports, presentations for visual misleads, cognitive biases, data integrity violations. Diagnoses misinterpretation causes and provides fixes.
npx claudepluginhub lyndonkl/claude --plugin thinking-frameworks-skillsThis skill uses the workspace's default tool permissions.
- [Overview](#overview)
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Visualizations are persuasive — common mistakes cause systematic misinterpretation, not just aesthetic failures. This skill scans for visual misleads (chartjunk, truncated axes, 3D distortion), checks for cognitive bias exploitation (confirmation bias reinforcement, anchoring, framing manipulation), and verifies data integrity (honest axes, complete data, fair comparisons).
Related skills: design-evaluation-audit for general design evaluation, cognitive-design for cognitive foundations, d3-visualization for creating visualizations, visual-storytelling-design for data stories.
Time: 15-30 minutes
Copy this checklist and track your progress:
Fallacy Audit Progress:
- [ ] Step 1: Scan for Visual Misleads
- [ ] Step 2: Check for Cognitive Biases
- [ ] Step 3: Verify Data Integrity
Check for chartjunk, 3D effects, truncated axes, volume illusions, and inappropriate chart types. These are the most common and visible fallacies.
Resource: Fallacies Catalog — Sections 1-2 (Visual Noise, Perceptual Distortion)
Look for confirmation bias reinforcement, anchoring effects, and framing manipulation. These are subtler but can significantly influence interpretation.
Resource: Fallacies Catalog — Section 3 (Cognitive Bias Exploitation)
Confirm honest axes, complete data, fair comparisons, proper context, and no spurious correlations. This is the most critical layer.
Resource: Detection Patterns — Integrity Principles and Quick Scan Checklist
Choose this when: Checking for chartjunk, 3D effects, truncated axes, and encoding problems.
→ Go to Fallacies Catalog — Sections 1-2
Choose this when: Looking for bias reinforcement in dashboard design, presentation framing, or data selection.
→ Go to Fallacies Catalog — Section 3
Choose this when: Verifying completeness, honesty, and context of data presentation.
Scope boundaries: This skill detects visual misleads, identifies cognitive bias exploitation, verifies data integrity, and provides specific fixes for each fallacy found. It does not create designs, evaluate general usability, teach cognitive theory, or assess aesthetic quality.