Classify simplicity/compression as earned, weak, or decorative based on whether it forbids alternatives, compresses independent facts, or merely renames/repackages them.
Installs into .claude/skills of the current project.
Are you the author of Classify Simplicity Evidence?
Add the live security badge to your README. It updates with every re-scan.
[](https://www.skillsdirectory.com/skills/yogsoth-ai-classify-simplicity-evidence)
---
name: classify-simplicity-evidence
description: "Classify simplicity/compression as earned, weak, or decorative based on whether it forbids alternatives, compresses independent facts, or merely renames/repackages them."
---
# classify-simplicity-evidence
## Purpose
Classify explanatory compression as earned, weak, or decorative.
## Input contract
```yaml
required: [explanation, covered_facts, alternatives, predictions]
optional: [evidence]
constraints: [facts and alternatives must be independently listed]
```
## Procedure
1. Check whether independent facts are compressed.
2. Check whether alternatives are forbidden and predictions constrained.
3. Classify earned, weak, or decorative and preserve rationale.
If simplicity provides discriminating support only through a vulnerable consequence, consider `test-risky-prediction` as the next tactic.
## Output contract
```yaml
produces: [simplicity_class, compression_evidence, missing_constraints]
delta_fields: [findings, evidence_updates, uncertainties, decisions]
```
## Quality gates
- Decorative simplicity is a relabeling with no independent compression or risky prediction.
## Failure and counterexamples
Do not classify by description length or elegance alone.
## Provenance map
- resolved: elegance-trap-probe
