The content is lean and efficient. It avoids explaining what bandits are or how algorithms work conceptually — it assumes Claude already knows. Every section serves a routing or decision-making purpose. The tables are dense with information and no filler.

The decision framework tables are concrete and useful for algorithm selection, but the skill itself contains no executable code, no concrete commands, and no copy-paste examples. It delegates all implementation details to reference files. The routing and build phases are directional rather than executable.

The Build Phases section provides a clear 3-step sequence, and the routing section gives clear entry paths. However, there are no validation checkpoints, no feedback loops for error recovery, and no verification steps — which matters for a skill involving implementation and deployment of algorithms.

Excellent progressive disclosure structure. The SKILL.md serves as a concise overview and routing hub, with clearly signaled one-level-deep references to six well-organized reference files. The reference table at the bottom provides clear navigation with descriptions of each file's contents.

Lists multiple specific concrete actions and algorithms: 'Thompson Sampling, UCB, epsilon-greedy, LinUCB, EXP3, contextual bandits' along with specific activities like 'algorithm selection, experiment harnesses, offline evaluation (IPS, Doubly Robust), infrastructure patterns, and correctness verification.'

Clearly answers both 'what' (implements, evaluates, and deploys bandit algorithms with specific techniques listed) and 'when' (explicit 'Use when...' clause with multiple trigger scenarios covering a broad range of user intents).

Excellent coverage of natural terms users would say: 'multi-armed bandits', 'exploration-exploitation tradeoffs', 'adaptive experiments', 'A/B testing alternatives', 'online optimization', 'bandit-based recommendation or personalization systems', 'contextual bandits'. These cover both technical and more casual phrasings.

Highly distinctive niche — multi-armed bandits and exploration-exploitation is a very specific domain. The named algorithms (Thompson Sampling, UCB, LinUCB, EXP3) and evaluation methods (IPS, Doubly Robust) make it clearly distinguishable from general ML, A/B testing, or optimization skills.