Conciseness

The ~170-line algorithm catalog restates well-known properties of Q-Learning, SARSA, curriculum, federated, and multi-task learning (e.g., "Proven, simple, works well for small/medium problems") that Claude already knows, the "CLI Operations" section repeats Quick Start commands verbatim, and version pins ("AgentDB v1.0.7+") plus marketing claims ("10-100x faster", "500x faster") pad the doc. Several unnecessary explanations match anchor 2 rather than anchor 3's "mostly efficient".

Actionability

CLI commands are copy-paste ready, but key code blocks are non-executable pseudocode: `sampleRandomBatch(replayBuffer, batchSize: 32)` and `collectBatch(size: 1000)` use invalid call syntax, `computeEmbedding`, `runEpisode`, and `getNewExperiences` are undefined, and the "Slow training" fix contains only comments with no actual command. This mix of executable guidance and pseudocode matches anchor 3.

Workflow Clarity

The "Collect Experiences → Train Model → Evaluate Performance" sequence is clear, but validation is only implicit (validationSplit buried in a code sample) with no validate-fix-retry feedback loop. Since the workflow involves database writes and batch inserts, the rubric's cap for missing feedback loops in batch/database operations applies, holding this at anchor 3.

Progressive Disclosure

Section headers are well organized, but the skill is monolithic: per-algorithm configuration details and the training guide are inlined when they belong in separate reference files, and the only pointer ("See docs/algorithms/ for detailed papers") references a path with no corresponding file in the bundle. This fits anchor 3's "some structure... content that should be separate is inline" rather than anchor 2, since clear headers and navigation exist.

Specificity

Lists concrete actions ("Create and train AI learning plugins") and names four specific algorithms (Decision Transformer, Q-Learning, SARSA, Actor-Critic), giving several specific capabilities with minor gaps — deploy/evaluate actions from the body are absent. It exceeds the 1-2 concrete actions of anchor 3 but lacks the comprehensive action coverage of anchor 5.

Completeness

Explicitly answers both questions: the "what" ("Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more") and the "when" ("Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience") with three concrete trigger phrases, matching the anchor-5 pattern exactly.

Trigger Term Quality

The "Use when" clause includes natural phrases users would say: "building self-learning agents", "implementing RL", "optimizing agent behavior through experience". Coverage is good but misses common variations like "reward", "policy", or "train an agent", so it falls short of anchor 5's comprehensive synonym coverage.

Distinctiveness Conflict Risk

The "AgentDB's" plugin system and named RL algorithms establish a clear niche with distinct triggers. However, the broad trigger "implementing RL" could overlap with a general reinforcement-learning or ML skill, so it does not fully meet anchor 5's minimal-conflict standard.