Conciseness

The body is dense and mostly efficient — it never explains concepts Claude already knows (no WGCNA/limma tutorials) — but it is padded by systematic duplication: Hard Rules 2–4, 12–13, 15–17, and 21–23 restate the Step 3/4, Step 4.5, Section G, Section D, Section I, and Section J mandates almost verbatim, and the Dataset Disclaimer text appears in full both in the body and in reference files. A meaningful fraction of the Hard Rules section could be trimmed with no loss of instruction, matching the "mostly efficient but could be tightened" anchor rather than the noticeably-padded level 2, since every duplication is at least procedural rather than concept explanation.

Actionability

Guidance is concrete and executable for an instruction-only skill: pattern-selection and config tables, a data-type-driven DESeq2/limma decision rule, minimum retrieval counts ("2–4 disease/process biology background references"), explicit dependency formulas, and a fully specified 6-element self-critical review. It sits at 4 rather than 5 because there is no worked example of a (even partially) filled-out plan section, which would make the required output format unambiguous rather than derivable.

Workflow Clarity

The 7 steps are always run in order, with an explicit pre-output validation checkpoint (Step 5: "If any dependency inconsistency is found, revise the plan before outputting"), a verbatim out-of-scope redirect script with a stop condition, an unavailable-search fallback (output a search strategy instead of fake references), and mandatory checklists (dependency checks, Section J risk review). This matches the top anchor: clear sequence, explicit validation steps, and error-recovery feedback loops.

Progressive Disclosure

The body is a navigable overview that points to eight well-signaled, one-level-deep references ("→ Detailed pattern logic: references/study-patterns.md" etc.) at exactly the point each is needed. Verified against the bundle: all eight referenced files exist, each is leaf content with no nested references, and there are no orphan files in references/. This matches the top anchor for structure and navigation.

Specificity

The description lists multiple concrete actions and outputs — "Generates complete process-related diagnostic biomarker bioinformatics research designs from a user-provided disease context, gene-family or pathway theme, and validation direction" plus "a recommended primary plan, stepwise workflow, figure plan, validation hierarchy, minimal executable version, publication upgrade path, and strictly verified literature retrieval" — in comprehensive, third-person form. It matches the top anchor (comprehensive coverage) rather than 4, since the action and output coverage has no meaningful gaps.

Completeness

It explicitly answers both questions: the "what" is concrete ("Generates complete... research designs from a user-provided disease context, gene-family or pathway theme, and validation direction") and the "when" is an explicit trigger clause ("Use when a study centers on process-related genes, DEG and WGCNA integration..."). This matches the 5 anchor exactly; it is above 4 because the when-clause is explicit rather than implied.

Trigger Term Quality

Good coverage of natural domain terms users would say: "DEG and WGCNA integration", "machine-learning feature selection", "nomogram-based diagnostic modeling", "immune infiltration", "regulatory-network analysis", "external or experimental validation". It falls short of the 5 anchor because several common variations are missing (e.g., ROC, LASSO/random forest, transcriptome, GEO/bulk RNA-seq) that researchers naturally use when requesting this kind of study.

Distinctiveness Conflict Risk

The phrase "process-related diagnostic biomarker nomogram" carves out a clear niche with distinct triggers, minimizing conflict with unrelated skills. It sits at 4 rather than 5 because generic shared terms (DEG, WGCNA, machine-learning feature selection, immune infiltration) create minor overlap risk with closely related sibling research-planner skills (e.g., prognostic or disease-generic bioinformatics planners).