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cellxgene-census

Queries the CZ CELLxGENE Census programmatically for versioned public single-cell and spatial transcriptomics data. Use when you need population-scale cell metadata, gene expression slices, Census summary counts, source H5AD URIs/downloads, embeddings, spatial Census data, or reference atlas comparisons across organisms, tissues, diseases, assays, and cell types. For analyzing your own local single-cell data use scanpy, anndata, or scvi-tools.

70

Quality

88%

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SKILL.md
Quality
Evals
Security

Quality

Content

78%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.

A well-structured, highly actionable body with verified one-level-deep references, clear when-to-read signals, and executable code throughout. The main weakness is conciseness: version/date specifics recur across nearly every section and several best practices are stated multiple times instead of being consolidated or moved to a versioning/deprecated section.

Suggestions

Consolidate time-sensitive specifics (pinned build '2025-11-08', SDK '1.18.0', schema '2.4.0', verification date) into a single versioning section or an 'old patterns' note, and have inline examples reference that section instead of repeating the literal build date in every code snippet.

Deduplicate repeated guidance: state the is_primary_data filter and census_version pinning advice once in Key Concepts and have Troubleshooting cross-reference them rather than restating the same filters and code.

Tighten the Use Cases section by removing examples that repeat code already shown in Key Concepts (e.g., the get_obs value_counts and get_anndata examples), keeping only cases that introduce new patterns.

DimensionReasoningScore

Conciseness

The body is largely efficient and assumes domain competence (no padding explaining what single-cell data is), but time-sensitive specifics are pervasive rather than consolidated — the pinned build '2025-11-08' appears in nearly every code example, plus '2026-09-30', 'cellxgene-census==1.18.0', and 'schema 2.4.0' scattered across sections with no deprecated/old-patterns section. Advice is also repeated (the is_primary_data filter appears in three sections; version-pinning guidance in three). This fits 'mostly efficient but includes some unnecessary explanation or could be tightened'; it is not 4 because the repetition and scattered version/date details go beyond minor trimmable instances.

3 / 5

Actionability

Nearly every section ships copy-paste-ready executable code: install commands, open_soma/get_obs/get_anndata queries, axis_query size estimation, presence-matrix slicing, a full tiledbsoma-ml dataloader loop, and a complete scanpy rank_genes_groups workflow. The common cases (explore, query expression, ML batches, cross-tissue analysis) are all covered with concrete runnable snippets; the only placeholder is the intentional '# Training logic' comment, so this is not 4.

5 / 5

Workflow Clarity

There is a clear sequence (resolve/pin the release version, explore metadata, estimate query size, then query, escalating to out-of-core processing), plus explicit checkpoints like counting rows before loading and checking the presence matrix, and a Troubleshooting section with error-recovery feedback loops (memory errors, gene not found, duplicate cells). It falls short of 5 because the eight detailed workflow patterns and their step-by-step validation are delegated to the reference file, and there is no consolidated checklist tying the sequence together inline.

4 / 5

Progressive Disclosure

The SKILL.md body is an overview that indexes the eight workflow patterns with one-line summaries pointing to references/core_workflow_patterns.md, moves schema detail to references/census_schema.md and pattern examples to references/common_patterns.md (both with explicit '**When to read:**' labels), and points to references/api_access.md for endpoint contracts — all four files verified to exist and one level deep. Navigation is easy and content is appropriately split, matching the top anchor; the single sibling cross-link from core_workflow_patterns.md to common_patterns.md does not create deep nesting.

5 / 5

Total

17

/

20

Passed

Description

92%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.

A strong description in third-person voice that names the domain, enumerates concrete capabilities, gives an explicit 'Use when' trigger clause, and cleanly disambiguates against local-data analysis tools. The only improvement space is adding a few more natural synonyms such as 'scRNA-seq'.

DimensionReasoningScore

Specificity

The description lists multiple concrete capabilities: 'Queries the CZ CELLxGENE Census programmatically', 'gene expression slices', 'Census summary counts', 'source H5AD URIs/downloads', 'embeddings', 'spatial Census data', and 'reference atlas comparisons'. Coverage is comprehensive and every action named is concrete rather than abstract, matching the top anchor; it is not score 4 because there are no noticeable coverage gaps.

5 / 5

Completeness

It explicitly answers both questions: what ('Queries the CZ CELLxGENE Census programmatically for versioned public single-cell and spatial transcriptomics data') and when ('Use when you need population-scale cell metadata, gene expression slices, ...') with concrete trigger phrases, and adds a negative boundary ('For analyzing your own local single-cell data use scanpy, anndata, or scvi-tools'). This matches the top anchor exactly; the explicit 'Use when' clause rules out the cap of 3.

5 / 5

Trigger Term Quality

Good natural-term coverage including 'single-cell', 'spatial transcriptomics', 'cell metadata', 'gene expression', 'embeddings', 'H5AD', plus organism/tissue/disease/assay/cell-type vocabulary users would actually say. A few common variations are missing (e.g., 'scRNA-seq', 'single-cell RNA sequencing'), which keeps it below the comprehensive-synonym anchor of 5 but clearly above the sparse keyword coverage of 3.

4 / 5

Distinctiveness Conflict Risk

A clear niche (the CZ CELLxGENE Census specifically) with distinct triggers like 'Census summary counts', 'source H5AD URIs', and 'reference atlas comparisons', plus explicit disambiguation directing local-data analysis to scanpy/anndata/scvi-tools. Minimal conflict risk with other skills; this fits the top anchor rather than the 'minor overlap risk' of 4.

5 / 5

Total

19

/

20

Passed

Validation

100%

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

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository

K-Dense-AI/scientific-agent-skills

Path

skills/cellxgene-census/SKILL.md

Reviewed

Table of Contents

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