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marker-panel-annotation

Assigns cell-type labels to clusters from a protein marker panel (multiplexed imaging such as MIBI, CODEX, IMC and CyCIF; mass and flow cytometry; CITE-seq protein), where the labels are judged against an expert reference. Within-dataset normalisation of cluster summaries, lineage first by positive defining markers, subtype only as far as the panel can determine it, marker-poor clusters assigned by exclusion, class-coverage sanity checks, and two independent annotators with adjudication. Use when the output is one label per cluster and the grader is agreement with an expert; for gating single cells in FCS data use flow-cytometry-analysis, and for transcript-based annotation use scanpy or scvi-tools.

75

Quality

94%

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SKILL.md
Quality
Evals
Security

Quality

Content

88%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The body delivers expert, actionable annotation rules in a clear six-step sequence with real validation checkpoints and executable summary code, and it wastes almost no tokens on concepts Claude already knows. The main structural gap is that everything lives in one file; the lineage marker table and subtype rules are natural candidates for a one-level-deep reference file.

Suggestions

Move the lineage/marker table (section 2) and the subtype-bound rules (section 3) to a references/ file (e.g. references/lineage-markers.md), keeping SKILL.md as a leaner overview that links to it — this improves progressive disclosure and trims context load.

Tighten the opening framing paragraph and rhetorical asides (e.g. "They are the clusters that decide a 0.95 agreement gate") into direct statements of the rules they introduce.

Extend the code block slightly to show how the positivity cut is derived from a bimodal split, since the prose offers both a 90th-percentile default and a bimodal alternative but only the percentile version is executable.

DimensionReasoningScore

Conciseness

The body is dense and assumes domain competence (no explanation of what MIBI or CD markers are), with every section carrying operational rules. Not level 5 because minor padding could be trimmed: the framing paragraph ("Both directions cost equally under an expert-agreement grader... Everything below follows from that") and rhetorical asides like "They are the clusters that decide a 0.95 agreement gate". Not level 3 because there is no genuine over-explanation of known concepts.

4 / 5

Actionability

Guidance is fully executable: a copy-paste-ready pandas/numpy block computing mean, z-score, and positivity fraction per cluster, a concrete lineage-by-marker table ("CD68, CD163, CD14, CD11b, CD11c, HLA-DR"), and decision rules specific enough to apply directly ("CD68+ CD163-high → M2", "FoxP3+ CD4+ → regulatory"). The remaining steps are judgment calls with explicit criteria and an evidence-file requirement.

5 / 5

Workflow Clarity

Sections 1–6 form a clear sequence (summarise per dataset → lineage → subtype bounds → marker-poor exclusion → sanity checks → two annotators and adjudication), with explicit validation checkpoints in section 5 (class coverage, prevalence plausibility, vocabulary exactness) and an error-recovery loop ("a call you cannot justify from a marker is a call to revisit"). Not level 4 because checkpoints are explicit and batch output is validated before writing.

5 / 5

Progressive Disclosure

The single file is well organized with clear section headers, no nested or dead references, and the code and pitfalls are appropriately placed. Not level 5 because at ~160 lines it is monolithic: the 8-lineage marker table and the subtype rules are reference-grade material that would fit a references/ file, keeping SKILL.md as a leaner overview. Not level 3 because what is inline is navigable and clearly signaled.

4 / 5

Total

18

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20

Passed

Description

100%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description is specific, complete, and distinctive: it names the concrete actions, gives an explicit trigger clause, includes platform synonyms users would naturally say, and routes adjacent tasks to other skills by name. It is written in third person with no fluff or over-claims.

DimensionReasoningScore

Specificity

"Assigns cell-type labels to clusters from a protein marker panel" plus a list of concrete method components — "Within-dataset normalisation of cluster summaries", "lineage first by positive defining markers", "subtype only as far as the panel can determine it", "marker-poor clusters assigned by exclusion", "class-coverage sanity checks", "two independent annotators with adjudication" — is comprehensive coverage of specific actions. Not level 4 because there are no minor gaps: every major capability of the skill is named.

5 / 5

Completeness

Explicitly answers both: what ("Assigns cell-type labels to clusters... judged against an expert reference" with method detail) and when ("Use when the output is one label per cluster and the grader is agreement with an expert"). Not level 4 because the when-clause is fully explicit rather than merely present.

5 / 5

Trigger Term Quality

Natural terms a user would say are all present with synonyms: "cell-type labels", "clusters", "protein marker panel", "multiplexed imaging", and platform names "MIBI, CODEX, IMC and CyCIF", "mass and flow cytometry", "CITE-seq protein". Not level 4 because both the task phrasing and the full set of platform synonyms users would actually name are covered.

5 / 5

Distinctiveness Conflict Risk

Clear niche (protein-marker-panel cluster labelling) with explicit boundary routing: "for gating single cells in FCS data use flow-cytometry-analysis, and for transcript-based annotation use scanpy or scvi-tools". Not level 4 because conflicts with the closest neighboring skills are pre-empted by name, leaving minimal overlap risk.

5 / 5

Total

20

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20

Passed

Validation

87%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 14 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

metadata_version

'metadata.version' is missing

Warning

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

14

/

16

Passed

Repository
synthetic-sciences/openscience
Reviewed

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