Conciseness

Mostly efficient and information-dense, but with repeated content that could be tightened: QC thresholds (min 200 genes, 500 UMI, <20% MT) appear three times (workflow diagram, Phase 1 summary, Quantified Minimums), the ASCII workflow diagram substantially duplicates the Phase Summaries, and the HuBMAP examples section contains two separate example blocks where one would do. This matches 'mostly efficient but includes some unnecessary explanation or could be tightened' rather than the noticeably-verbose level 2, since the domain-specific interpretation guidance (Moran's I thresholds, evidence grading) is genuinely additive rather than padding.

Actionability

The content is largely executable: concrete tool invocations with named parameters (`HuBMAP_search_samples(organ="LK", sample_category="block", registered_only=True, limit=5)`), runnable Python (`sc.read_visium`, `sc.read_10x_mtx`), specific thresholds (FDR < 0.05, Moran's I > 0.3, z-score > 2), and named methods per phase. Minor gaps — e.g., the GEO download snippet shows the URL but not the actual download/parse step, and the H&E DeepSpotM tool is named without a call example — keep it at 'mostly executable guidance with minor gaps' rather than fully copy-paste-ready coverage.

Workflow Clarity

The eight-phase pipeline is clearly sequenced with most checkpoints present: QC gates in Phase 1, alignment verification, FDR filtering in Phase 4, and marker-gene validation in Phase 6. It falls short of level 5 because feedback loops are implicit — there is no guidance on what to do when a checkpoint fails (e.g., too few spots survive QC, clustering yields no coherent domains, alignment mismatch), which the anchor requires as explicit error-recovery steps.

Progressive Disclosure

Section structure is good and reference links are clearly signaled ('See [report_template.md](report_template.md)', 'See [code_examples.md](code_examples.md)'), but no bundle files exist in the skill directory, so both referenced files are dangling links that provide no actual detail. Additionally, substantial content that belongs in reference files is inlined in SKILL.md (the full HuBMAP API documentation with two example blocks, use-case walkthroughs, and interpretation guidance), matching 'some structure but... content that should be separate is inline' rather than the well-split level 4.

Specificity

The description lists multiple concrete actions — "Maps gene expression to tissue architecture, identifies spatially variable genes (SVGs), tissue-domain segmentation, and cell-cell interaction inference" — alongside the four named platforms (Visium, MERFISH, seqFISH, Slide-seq), matching the comprehensive-coverage anchor. Re-checking the level-4 anchor ('several specific actions; minor gaps') shows no meaningful gap, since segmentation, SVG identification, and interaction inference cover the skill's core capabilities.

Completeness

Both questions are explicitly answered: the 'what' via the concrete action list and the 'when' via "Use for spatial gene-expression questions, tissue architecture analysis, and SVG identification", mirroring the anchor's what-plus-explicit-trigger pattern. Third-person voice is used correctly ('Maps', 'identifies'), so no voice penalty applies.

Trigger Term Quality

Natural terms users would say are well covered: "spatial transcriptomics", platform names (Visium, MERFISH, seqFISH, Slide-seq), "tissue architecture", "cell-cell interaction", "spatially variable genes (SVGs)", "gene-expression questions". A few natural variations users might phrase differently (e.g., "spatial data", "tissue mapping", "spatial domains", "deconvolution") are absent, which matches the good-coverage-with-minor-gaps anchor rather than the fully comprehensive one.

Distinctiveness Conflict Risk

Spatial transcriptomics is a clear niche with distinct platform-name triggers, and the scope ('maps gene expression to tissue architecture', SVGs, tissue-domain segmentation) is unlikely to fire for unrelated skills. There is slight conceptual overlap with a generic single-cell analysis skill, but the explicit spatial framing and platform names keep conflict risk minimal, matching the clear-niche anchor.