synthesis-content-quality

# Content Quality

A systematic methodology for evaluating writing quality and identifying slop, with or without AI involvement. The framework targets bad content, not provenance. Ethically authored AI-collaborated content can be excellent; styled empty human content is slop. This skill detects slop.

The methodology is durable. The catalog refreshes as model behavior shifts and as new patterns emerge in production output. v4.0 adds model-family fingerprinting across eight families, a substance and depth section grounded in the Frankfurt-Pennycook-Hicks-Humphries-Slater framework, a cross-cutting causal-and-calibration layer, and zone-conditional detection. The compounding-archive principle means patterns are never deleted: when newer model versions train a pattern out, the catalog tags it Historical and retains it for forensic analysis of older published content.