LLM persona control divides between trait-prompt strings and model-specific activation-steering vectors; neither offers a per-response trace from output to persona contents. We introduce Persona-CAPS, a retrieval-based persona framework grounded in the Cognitive-Affective Personality System (Mischel and Shoda, 1995): a persona is a corpus of if (situation) then (behavior) contingencies mined from public Experience Sampling Method data, retrieved per-situation, each fired atom logged to its source observation. An audit-loop study quantifies three structural properties paragraph baselines lack: retrieval exposes corpus-level corruption that paragraph dilution masks (P²-sanity passes 0/9 on MiniMax low-Openness under identical polarity injection); atom-level repair uses ~1/3 the paragraph-baseline character footprint at comparable fix success (byte-identical on 13/13 paired GPT-4o cross-vendor conditions); and two audit modes (universal-recall sweep, sparse-edit triage) support deployment-time governance. Across six backbones, CAPS beats Simple-Prompt on all three backbones of PersonalityBench (Deng et al., 2025) (mean +0.69/10), delivers monotonic 1-D continuous control via mixture weights π on all 15 (vendor × Big-Five) Mini-IPIP cells, and matches the strongest paragraph baseline (P²) on the non-all-low 5-D profiles (§4.6; backbone-level direction-match ≥ 0.93). Cross-family judge replication (r = +0.951) and a two-rater human pack triangulate the headline. Code, corpus, and logs are released.