Conciseness

The body assumes Claude's intelligence (no primers on what LoRA or fine-tuning are) and is dense with skill-specific conventions, but the routing preamble and DGX Spark OOM aside could be tightened, and inline time-sensitive version/date references ("Unsloth 2026.7.x", "Thinking Machines/Schulman, 2025-09") sit outside any old-patterns/deprecated section. Efficient with minor trimmable instances — the 4 anchor, not the every-token-earns-its-place 5.

Actionability

Fully executable guidance throughout: a concrete target_modules list, the complete FastLanguageModel.get_peft_model code block with the 2*r alpha derivation shown in-line, the 2e-4 QLoRA starting LR, rank-by-task tables, and a runnable `torch.cuda.is_bf16_supported()` check command. Copy-paste ready and covering the common cases — the 5 anchor; not 4 because there are no meaningful gaps.

Workflow Clarity

The decision flow is clearly sequenced (input contract → method-choice table → rank selection → concrete config → failure modes) with a diagnostic feedback loop ("Check configuration against this skill before debugging the training loop itself") and a hardware validation check for bf16. Not 5 because there is no explicit validation step for the emitted adapter config itself; not 3 because checkpoints are present and explicit, not merely implied.

Progressive Disclosure

Both referenced files (references/hyperparameters.md, references/unsloth-trl-mapping.md) exist, are one level deep, and are clearly signaled both inline at the point of need and in a dedicated References section with per-file descriptions. The body keeps only summaries and decisions inline while tables and full worked configs live in the references — matching the 5 anchor's clear overview with well-signaled one-level-deep references.

Specificity

Lists several specific actions — "Configure LoRA and QLoRA supervised fine-tuning", "writing or reviewing a LoRA/QLoRA training configuration", "choosing rank/alpha/target modules" — but "current best-practice hyperparameters" is a mildly generic qualifier, leaving minor coverage gaps versus the comprehensive 5 anchor.

Completeness

Explicitly answers both: what ("Configure LoRA and QLoRA supervised fine-tuning with current best-practice hyperparameters") and when ("Use when writing or reviewing a LoRA/QLoRA training configuration, choosing rank/alpha/target modules, or deciding between LoRA, QLoRA, and full fine-tuning") with concrete trigger phrases. Matches the 5 anchor exactly; the 4 anchor's weaker/less-explicit 'when' does not apply.

Trigger Term Quality

Natural keywords users would say are well covered ("LoRA", "QLoRA", "fine-tuning", "training configuration", "rank/alpha", "target modules", "full fine-tuning"), but common synonyms like "SFT" spelled out as an abbreviation users say, "PEFT", or "adapter" are absent. Fits the 4 anchor (good coverage, a few natural terms missing), not 5 (no synonym/extension-level breadth) and clearly above 3.

Distinctiveness Conflict Risk

The adapter-configuration niche is clear and specific, but "deciding between LoRA, QLoRA, and full fine-tuning" invites minor overlap with a sibling method-selection skill in the same plugin family. Mostly distinct with minor overlap risk — the 4 anchor — rather than the minimal-conflict 5 anchor.