The body is dense with GRDB-specific executable specifics (regex patterns, PRAGMA values, version 7.11.0, 0xDEAD10CC) and avoids explaining concepts Claude already knows; its length reflects genuine multi-pattern audit complexity rather than padding, and every section earns its tokens.

It provides fully executable guidance — exact Glob/Grep patterns, concrete fix code (e.g., execute(sql: "WHERE id = ?", arguments: [id]), PRAGMA optimize=0x10002, observesSuspensionNotifications = true), specific file globs, and a copy-paste output template.

A clearly sequenced five-phase workflow with explicit gating checkpoints (e.g., 'If Library is Neither: stop'), a mandatory Framework Map presented before proceeding, and per-detector Search/Verify/Fix loops with re-validation guidance.

Operational logic is kept inline and well-organized into named phases, while deep detail is deferred to clearly signaled one-level external references (axiom-data (skills/grdb-performance.md) §N) with no nested reference chains and easy navigation.

The description names a concrete domain via specific trigger scenarios ('GRDB performance review', 'slow GRDB queries', 'a ValueObservation that stopped updating') but lists no action verbs describing what the skill actually does, so it names the domain without enumerating actions.

It has an explicit, strong 'when' clause ('Use when the user mentions...') but the 'what does this do' is only implied through the domain nouns rather than stated as an action, so it does not clearly answer both what and when.

Phrases like 'slow GRDB queries', 'a ValueObservation that stopped updating', and 'pre-release GRDB scan' are exactly the symptom-driven terms a GRDB developer would naturally say, giving good coverage of natural trigger language.

GRDB is a narrow, specific SQLite-for-Swift niche and the triggers (ValueObservation staleness, app-group setup audit, pre-release scan) are distinctive enough to be unlikely to fire for the wrong skill.