Lean, table-driven content with no padding or re-explanation of concepts Claude already knows; every section (overview, decision tree, symbol formats, fallback) earns its place.

Concrete priority lists per market, exact symbol formats, an explicit env-var check (TUSHARE_TOKEN), and a real call (load_skill('akshare')) give copy-applicable guidance; the illustrative fallback block is clearly a behavior description, not pseudocode to run.

The analysis scenario is a clearly numbered sequence (identify market -> pick source by priority -> load skill) with an availability-check feedback loop that switches to a fallback on 'connection timeout', matching the score-3 anchor.

A single-purpose, well-sectioned overview that defers API details to per-source skills via load_skill; no bundle files exist or are needed, and references are one level deep and clearly signaled.

Names the domain ('Data source selection decision tree') and one concrete action ('choose the best available data source'), but does not list multiple specific actions like the score-3 anchor, so it falls short of comprehensive.

Clearly states what it does (data source selection decision tree) and when to use it ('Load this skill BEFORE any backtest or data-fetching task'), satisfying both what and when with an explicit trigger clause.

Includes natural triggers ('backtest', 'data-fetching task') a user would say, but coverage is thin — missing common variations like 'fetch data', 'get market data', or 'historical data'.

The backtest/data-fetching routing niche is distinct from typical data skills, and its triggers are specific enough to avoid firing for unrelated skills.