Conciseness

The body is mostly code-driven and efficient in the Quick Start, but 'Core Concepts' re-explains magnitude-pruning basics, 'Sparsity Patterns' illustrates [1,0,1,0]-style patterns Claude can infer, and ~485 lines inline material that duplicates the provided references/wanda.md. This is 'mostly efficient but includes some unnecessary explanation' (anchor 3), not the minor trimming of anchor 4, since padding recurs across several sections.

Actionability

The Wanda quick start is near-executable, but the Strategies and Production sections depend on undefined functions (train_step, prune_layer, fine_tune, prune_model, finetune_dataset, load_calibration_data), and 'importance = weight^2 / diag(Hessian)' and the 'if no_retraining_budget:' block are pseudocode. This matches anchor 3 ('pseudocode instead of executable code; missing key details'); it is not anchor 4 because roughly half the code blocks cannot run as written.

Workflow Clarity

The production pipeline lists a numbered sequence (load -> calibrate -> prune -> optionally fine-tune -> save) and an Evaluation section exists, but there is no integrated validation checkpoint or feedback loop (e.g., 'if accuracy degradation exceeds X, lower sparsity and re-prune'). Since pruning plus fine-tuning is a batch operation on a large model, the missing-validation cap of 3 applies.

Progressive Disclosure

The bundle provides references/wanda.md (a Wanda deep-dive), yet the body never links to it — no reference to 'references/', 'wanda.md', or any bundle path appears anywhere in SKILL.md. The reference file is orphaned while its subject matter is inlined monolithically, matching anchor 2 ('content that clearly belongs in separate files is inlined; references buried/absent'); it is not anchor 3 because not a single reference is signaled, despite the bundle existing.

Specificity

Concrete actions ('Reduce LLM size', 'accelerate inference') are backed by named techniques (Wanda, SparseGPT), quantitative targets (50% sparsity), and a coverage list of pruning variants. It falls short of anchor 5 because the action verbs themselves are few, with breadth carried by the technique enumeration rather than a comprehensive set of specific actions.

Completeness

Clearly answers 'what' ('Reduce LLM size and accelerate inference using pruning techniques like Wanda and SparseGPT') and 'when' with an explicit 'Use when compressing models without retraining, achieving 50% sparsity with minimal accuracy loss, or enabling faster inference on hardware accelerators' clause containing concrete trigger phrases. Matches anchor 5 exactly.

Trigger Term Quality

Good natural keyword coverage: 'pruning', 'compressing models', 'sparsity', 'faster inference', 'Wanda', 'SparseGPT'. A few natural user phrasings are missing ('make the model smaller', 'shrink the model', 'speed up inference'), matching anchor 4 rather than the comprehensive synonym coverage of anchor 5.

Distinctiveness Conflict Risk

A clear niche (LLM pruning) with distinctive named techniques (Wanda, SparseGPT, N:M sparsity) gives minimal conflict risk, but the broad phrase 'compressing models' could overlap with sibling compression skills (quantization, distillation). Mostly distinct with minor overlap risk — anchor 4.