The description names the domain (agent-tty evals) and mentions several concepts (trials, parallelism, A/B comparison, non-determinism baseline, sample sizes, result interpretation), but these read more as topics covered than concrete actions the skill performs. It says 'Guide for running' which is somewhat vague about what actions Claude actually takes.

The description covers 'what' (a guide for running evals with various statistical features) but completely lacks a 'Use when...' clause or any explicit trigger guidance for when Claude should select this skill. Per the rubric, a missing 'Use when...' clause caps completeness at 2, and the 'what' itself is more of a topic list than clear capability description, warranting a 1.

Includes some relevant keywords like 'evals', 'A/B comparison', 'sample sizes', and 'trials', but 'agent-tty' is very specific jargon that most users might not use. Missing common variations like 'benchmarks', 'testing', 'evaluation runs', or 'statistical significance'.

The description targets a very specific niche — 'agent-tty evals' with statistical methodology — which is unlikely to conflict with other skills. The combination of agent-tty, A/B comparison, and statistical eval concepts creates a clear and distinct identity.

The content is mostly efficient and information-dense, but includes some unnecessary elaboration—e.g., Section 1's detailed retelling of empirical findings (flip rates, cross-provider checks) could be condensed to a few bullet points. Some sections like the concurrency safety rationale repeat points. However, it largely avoids explaining things Claude already knows.

Excellent actionability throughout. Every section provides concrete, copy-paste-ready bash commands with specific flags, trial counts, and concurrency settings. The A/B comparison workflow in Section 3 is fully executable with real CLI invocations and jq piping. Recommended parameter values are specific (e.g., --trials 5, --concurrency 4, 0.05 score delta cutoffs).

The A/B comparison workflow in Section 3 is clearly sequenced (run baseline → save path → make change → run candidate with --compare-baseline → read verdicts). Section 4 provides explicit interpretation rules including what 'inconclusive' means and when to add more trials. The guide covers the full lifecycle from running evals to interpreting results with clear decision criteria and feedback loops (add more trials if noise-dominated).

The content is well-structured with numbered sections and clear headings, making navigation easy. However, it's a fairly long monolithic document (~170 lines of substantive content) with no references to external files for deeper topics like authoring APIs, reporter details, or workspace presets—these could benefit from being split out. For a standalone skill with no bundle files, the organization is decent but the length pushes against ideal progressive disclosure.