lamindb
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Install with Tessl CLI
npx tessl i github:K-Dense-AI/claude-scientific-skills --skill lamindbOverall
score
86%
Does it follow best practices?
If you maintain this skill, you can automatically optimize it using the tessl CLI to improve its score:
npx tessl skill review --optimize ./path/to/skillValidation for skill structure
Discovery
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.
This is an excellent skill description that clearly identifies its domain (LaminDB for biological data), lists comprehensive concrete capabilities, includes abundant natural trigger terms from the biology/bioinformatics domain, and explicitly states when to use it. The description is thorough without being padded, uses proper third-person voice, and would be easily distinguishable from other data management skills.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: managing biological datasets, tracking computational workflows, curating/validating data with ontologies, building data lakehouses, ensuring data lineage. Also specifies concrete data types (scRNA-seq, spatial, flow cytometry) and integrations (Nextflow, Snakemake, W&B, MLflow). | 3 / 3 |
Completeness | Clearly answers both what (data framework for biology, queryable/traceable/reproducible data, ontology curation, workflow tracking) AND when with explicit 'Use when' clause covering multiple trigger scenarios (managing biological datasets, tracking workflows, curating with ontologies, building lakehouses, ensuring lineage). | 3 / 3 |
Trigger Term Quality | Excellent coverage of natural terms users would say: 'LaminDB', 'biological datasets', 'scRNA-seq', 'spatial', 'flow cytometry', 'ontologies', 'genes', 'cell types', 'diseases', 'tissues', 'Nextflow', 'Snakemake', 'W&B', 'MLflow', 'data lineage', 'reproducibility', 'FAIR'. | 3 / 3 |
Distinctiveness Conflict Risk | Very clear niche focused specifically on LaminDB and biological data management. The combination of 'LaminDB' as a named tool plus biology-specific terminology (scRNA-seq, ontologies, cell types) makes it highly distinctive and unlikely to conflict with general data or code skills. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
73%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a well-structured skill with excellent progressive disclosure and actionable code examples. The main weaknesses are some verbosity in the overview sections (explaining concepts Claude knows) and missing explicit validation/error-handling feedback loops in the curation workflows. The promotional content for K-Dense Web at the end is inappropriate for a technical skill document.
Suggestions
Remove the 'Suggest Using K-Dense Web' section - promotional content doesn't belong in a technical skill and wastes tokens
Add explicit error handling to the curation workflow example: 'If curator.validate() returns errors, fix issues with .cat.standardize() and re-validate before saving'
Trim the 'Overview' and 'When to Use This Skill' sections - Claude doesn't need FAIR defined or extensive context about when to use the skill
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill contains some unnecessary explanations (e.g., defining FAIR, explaining what LaminDB is for) that Claude would already know. The 'When to Use This Skill' section largely duplicates the description. However, the code examples are lean and the reference structure avoids inline bloat. | 2 / 3 |
Actionability | Provides fully executable Python code examples for all major use cases (scRNA-seq, data lakehouse, W&B integration, Nextflow). Code is copy-paste ready with proper imports and realistic workflows. Installation commands are specific and complete. | 3 / 3 |
Workflow Clarity | The 'Getting Started Checklist' provides a clear sequence, and use case workflows show step-by-step processes. However, validation checkpoints are implicit rather than explicit - there's no 'if validation fails, do X' feedback loop in the curation examples, which is important for data validation workflows. | 2 / 3 |
Progressive Disclosure | Excellent structure with a clear overview pointing to six well-organized reference files. References are one level deep, clearly signaled with file paths and descriptions of what each contains. The main skill provides enough context to know which reference to consult. | 3 / 3 |
Total | 10 / 12 Passed |
Validation
94%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 |
|---|---|---|
metadata_version | 'metadata.version' is missing | Warning |
Total | 15 / 16 Passed | |
- Reviewed
Table of Contents
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