Mostly code-dense and actionable, but repeats material: Wanda and SparseGPT criteria are shown in full Quick Start implementations then re-explained in 'Core Concepts', and magnitude pruning appears across several blocks, with tutorial-style comments like '# Better than magnitude alone (considers usage)'. Not 3 because the re-explanation and repetition could be tightened; not 1 because it is not padded with concepts Claude doesn't need at length.

Provides substantial executable code (wanda_prune, nm_prune, production_pruning_pipeline), but also pseudocode and undefined stubs: 'importance = weight^2 / diag(Hessian)' (invalid Python), and bare calls to load_calibration_data(), prune_model, fine_tune, train_step, prune_layer, finetune_dataset. Not 3 because several blocks are not copy-paste ready; not 1 because much of the guidance is genuinely concrete and runnable.

The 'Complete Pruning Pipeline' lists a numbered sequence (load, calibrate, prune, fine-tune, save) but lacks validation checkpoints for a destructive batch operation, and the separate Evaluation block is not integrated as a checkpoint and contains broken code ('(original_results - pruned_results)' on dicts). Per the rubric, missing verification steps in destructive/batch workflows caps this at 2; not 1 because a clear sequence is present.

The body is a monolithic ~480-line file with all implementations, concepts, strategies, and comparisons inline, and the provided references/wanda.md bundle is never linked or signaled from the body (no 'references/' or 'wanda.md' references appear). Not 1 because sections are organized with clear headings rather than deeply nested references; not 3 because content that should be split is inline and the one reference is orphaned.

Lists multiple concrete actions and named techniques — 'Reduce LLM size and accelerate inference using pruning techniques like Wanda and SparseGPT' plus 'Covers unstructured pruning, structured pruning, N:M sparsity, magnitude pruning, and one-shot methods'. Not 2 because it goes well beyond naming a domain and a couple actions; it enumerates specific capabilities.

Explicitly states what it does ('Reduce LLM size and accelerate inference...') and when to use it via a clear 'Use when compressing models without retraining...' clause, matching the both-what-and-when anchor. Not 2 because the 'when' is explicit, not merely implied.

The 'Use when' clause surfaces natural user phrasing — 'compressing models without retraining', 'achieving 50% sparsity with minimal accuracy loss', 'enabling faster inference on hardware accelerators' — alongside named methods users would cite. Not 2 because coverage of common natural variations is strong rather than partial.

Targets a clear niche (LLM pruning/compression) with named, distinctive triggers (Wanda, SparseGPT, N:M sparsity) unlikely to fire for unrelated skills. Not 2 because it is far more specific than 'Works with document files'-style overlap-prone phrasing.