Conciseness

The body avoids explaining known concepts and stays dense with non-obvious version-specific caveats, but repeats cautions in multiple phrasings ("Never use logged, scaled, residualized, or batch-corrected values as counts. Numerical inspection alone cannot establish that data are raw") and includes general data-hygiene guidance ("Inspect per-library/cluster coverage, doublets, ambient RNA") that could be trimmed. This is anchor 4 (efficient with minor trim opportunities), not anchor 5, where every token would earn its place.

Actionability

Fully executable guidance throughout: a pinned `uv pip install` command, two complete CLI invocations with all flags, exact read calls (`ad.read_h5ad`, `ad.io.read_loom('counts.loom', X_name='spliced', sparse=True)`), exact API calls (`scv.pp.moments(adata, n_neighbors=None)`, `recover_dynamics(var_names='all')`), and a runnable post-run inspection block. It clearly matches anchor 5; the only gap — the inspection snippet assumes the run's `adata` is in scope — is trivial and covered by context.

Workflow Clarity

A clear 7-step numbered sequence with explicit validation checkpoints ("Validate layers, IDs, model compatibility and grouping; refuse previously generated moments/velocity", "Require usable genes and a nonempty velocity graph", diagnostics recorded in `uns['velocity_workflow']['diagnostics']`), plus a problem→action troubleshooting table and pre-run input-contract and post-run evidence-inspection checklists. This matches anchor 5 (explicit validation steps, feedback loops, checklists); anchor 4 would require missing checkpoints.

Progressive Disclosure

Two one-level-deep bundle references, both real files, clearly signaled with purpose: "[model reference](references/velocity_models.md) for assumptions, transition probabilities, and an optional CellRank handoff" and "[bundled script](scripts/rna_velocity_workflow.py)". Theory is appropriately split into the 184-line reference (which itself contains no further nested references), implementation into the 251-line script, and operations kept in SKILL.md. Matches anchor 5: clear overview, well-signaled references, appropriate split.

Specificity

"Fits deterministic or dynamical models, examines gene phase portraits, builds velocity graphs, estimates relative latent time, and ranks velocity-associated genes" lists five concrete actions on top of a named data source ("spliced and unspliced single-cell RNA counts"), matching anchor 5's comprehensive concrete-action coverage. Not anchor 4, since no capability area of the skill is left unnamed.

Completeness

Explicitly answers both questions: the what ("Performs RNA velocity analysis with scVelo... Fits... examines... builds... estimates... ranks") and the when ("Use for directional trajectory hypotheses and kinetic-model diagnostics alongside Scanpy"), with concrete trigger phrases and an honest scope boundary. Clearly matches anchor 5; not anchor 4, because the 'when' clause is explicit and specific rather than merely present.

Trigger Term Quality

Strong natural keyword coverage — "RNA velocity", "scVelo", "spliced and unspliced", "trajectory", "latent time", "dynamical models" — but a few natural terms users would say are missing, such as the "scRNA-seq" acronym and file-format triggers (.h5ad, .loom). This fits anchor 4 ("good keyword coverage; a few natural terms missing") rather than anchor 5's fully comprehensive coverage including synonyms and extensions.

Distinctiveness Conflict Risk

"RNA velocity analysis with scVelo from spliced and unspliced single-cell RNA counts" carves a clear niche with distinct triggers, but "Use for directional trajectory hypotheses... alongside Scanpy" creates minor overlap risk with closely related Scanpy/trajectory-analysis skills on generic trajectory queries. This is anchor 4 (mostly distinct, minor overlap with closely related skills); not 5 because that overlap is plausible, not 3 because the core velocity triggers are unambiguous.