cellxgene-census

Query 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.

Quality

92%

Does it follow best practices?

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Securityby

Low

Low-risk findings worth noting

Quality

Content

85%

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

A highly actionable, well-structured skill body with clear workflows and genuine progressive disclosure to two real reference files. The main weakness is conciseness: the redundant 'Common Use Cases' section and standard-scanpy walkthrough add tokens without adding Census-specific value.

Suggestions

Remove or collapse the 'Common Use Cases' section — Use Cases 1–4 duplicate the Core Workflow Patterns already shown in sections 2, 3, 5, and 8, adding length without new information.

Trim the standard scanpy pipeline in section 7 (normalize_total, log1p, highly_variable_genes, PCA, neighbors, UMAP) to just the Census-loading step, since Claude already knows the scanpy workflow.

Consider moving the full pattern catalog in 'Core Workflow Patterns' into references/common_patterns.md and keeping only a quick-start example inline, to reduce body length while preserving detail in the bundle.

Dimension	Reasoning	Score
Conciseness	Mostly efficient Census-specific API detail, but the 'Common Use Cases' section duplicates the Core Workflow Patterns (Use Cases 1–4 re-implement sections 2/3/5/8) and section 7 walks through standard scanpy steps (normalize_total/log1p/PCA/UMAP) Claude already knows.	2 / 3
Actionability	Provides fully executable, import-bearing, copy-paste-ready code throughout (open_soma, get_anndata, axis_query, ExperimentDataset, spatial to_spatialdata), plus concrete filter syntax and commands — matching the executable anchor.	3 / 3
Workflow Clarity	Multi-step processes are clearly sequenced with explicit checkpoints ('Estimate Query Size Before Loading' branches to out-of-core processing; 'Two-Step Workflow: Explore Then Query') and a troubleshooting section for error recovery.	3 / 3
Progressive Disclosure	Body is an overview that pushes detail to two real, one-level-deep bundle files (references/census_schema.md, references/common_patterns.md), each clearly signaled with a 'When to read' clause for easy navigation.	3 / 3
	Total	11 / 12 Passed

Description

100%

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, third-person description that clearly states capabilities, provides explicit 'Use when' triggers with broad natural-language coverage, and disambiguates from neighboring tools via negative guidance. All four dimensions hit the top anchor with no verbosity or fluff.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete capabilities — 'population-scale cell metadata, gene expression slices, Census summary counts, source H5AD URIs/downloads, embeddings, spatial Census data, or reference atlas comparisons' — matching the multiple-specific-actions anchor.	3 / 3
Completeness	Explicitly answers both what ('Query the CZ CELLxGENE Census programmatically for versioned public single-cell and spatial transcriptomics data') and when ('Use when you need population-scale cell metadata...'), with an explicit 'Use when' clause and negative-scope guidance.	3 / 3
Trigger Term Quality	Covers natural terms users would say ('cell metadata, gene expression slices, summary counts, H5AD URIs/downloads, embeddings, spatial Census data' plus organism/tissue/disease/assay/cell type), giving good coverage rather than just 'some relevant keywords'.	3 / 3
Distinctiveness Conflict Risk	Has a clear niche (CZ CELLxGENE Census) with distinct triggers and an explicit redirect for local data ('For analyzing your own local single-cell data use scanpy, anndata, or scvi-tools'), making misfires unlikely.	3 / 3
	Total	12 / 12 Passed

Validation

93%

Warnings & errors only

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

Validation — 15 / 16 Passed

Validation for skill structure

Criteria	Description	Result
skill_md_line_count	SKILL.md is long (560 lines); consider splitting into references/ and linking	Warning

	Total	15 / 16 Passed

Repository: K-Dense-AI/scientific-agent-skills
Path: skills/cellxgene-census/SKILL.md
Commit: 757b63b

Reviewed: about 11 hours ago

Table of Contents

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