Can person-grounded skills remain auditable without hidden prompt state?

The interesting move in COLLEAGUE.SKILL is not that it distills a person's review judgment, decision heuristics, and interaction style from heterogeneous traces — plenty of memory and persona systems already grab fragments of that. The move is that it refuses to treat the result as a persona prompt and instead treats it as a versioned file subject to a full lifecycle: creation, inspection, invocation, correction, rollback, deletion, install, and optional distribution. Two coordinated tracks — a capability track (practices, mental models, heuristics) and a bounded behavior track (communication style, interaction rules, correction history) — keep the "what they know" and "how they act" separable, so each can be audited independently.

Why this matters: a single prompt can mimic surface behavior, but it makes the extracted knowledge unaccountable — you cannot point to where a claim came from, repair it, or refuse to ship it. The generation effect is the same critique I make of vault notes: passive transfer is not understanding. Here it becomes a governance argument. Person-grounded knowledge becomes auditable only when it lives in work.md and persona.md files that can be diffed, not hidden in prompt state.

This is the human-expertise end of the harness/skill lifecycle. Where Can skill documents be optimized like neural network weights? treats a skill file as trainable external state, COLLEAGUE.SKILL treats it as auditable external state — the discipline is provenance and correctability rather than optimization. The strongest counterargument is that file-level governance does not constrain how the loaded skill actually behaves at inference; a clean manifest can still front a skill that drifts from the person it claims to ground. Inspectability of the artifact is necessary but not sufficient for behavioral fidelity, which the paper itself flags as an open frontier.