neuropixels-analysis
Neuropixels neural recording analysis. Load SpikeGLX/OpenEphys data, preprocess, motion correction, Kilosort4 spike sorting, quality metrics, Allen/IBL curation, AI-assisted visual analysis, for Neuropixels 1.0/2.0 extracellular electrophysiology. Use when working with neural recordings, spike sorting, extracellular electrophysiology, or when the user mentions Neuropixels, SpikeGLX, Open Ephys, Kilosort, quality metrics, or unit curation.
93
92%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Advisory
Suggest reviewing before use
Quality
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 hits all the marks. It provides highly specific capabilities in a well-defined niche domain, includes comprehensive trigger terms that neuroscientists would naturally use, and has an explicit 'Use when...' clause. The description is concise yet thorough, using proper third-person voice throughout.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: load SpikeGLX/OpenEphys data, preprocess, motion correction, Kilosort4 spike sorting, quality metrics, Allen/IBL curation, AI-assisted visual analysis. Very detailed and actionable. | 3 / 3 |
Completeness | Clearly answers both 'what' (load data, preprocess, motion correction, spike sorting, quality metrics, curation, visual analysis) and 'when' with an explicit 'Use when...' clause listing specific trigger scenarios and terms. | 3 / 3 |
Trigger Term Quality | Excellent coverage of natural terms users would say: 'neural recordings', 'spike sorting', 'extracellular electrophysiology', 'Neuropixels', 'SpikeGLX', 'Open Ephys', 'Kilosort', 'quality metrics', 'unit curation'. These are exactly the terms a neuroscientist would use. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive niche — Neuropixels neural recording analysis is a very specific domain. The combination of hardware (Neuropixels 1.0/2.0), software (SpikeGLX, OpenEphys, Kilosort4), and domain (extracellular electrophysiology) makes it extremely unlikely to conflict with other skills. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
85%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a strong, well-structured skill that provides a complete Neuropixels analysis workflow with executable code, clear sequencing, and good progressive disclosure to reference materials. Its main weakness is moderate verbosity—sections like 'When to Use This Skill,' the hardware table, installation instructions, project structure, and external resource links add bulk without proportional value for Claude. Trimming these could save significant tokens while preserving all actionable content.
Suggestions
Remove or drastically shorten the 'When to Use This Skill' section—this duplicates the YAML description and is metadata, not instruction.
Move 'Installation', 'Project Structure', 'Supported Hardware & Formats', and 'Additional Resources' into a reference file to reduce the main skill's token footprint by ~30%.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is fairly comprehensive but includes some unnecessary sections like 'When to Use This Skill' (which repeats the YAML description), the hardware table (Claude knows Neuropixels specs), installation instructions (standard pip commands), and the 'Additional Resources' links section. The 'Overview' paragraph is also somewhat redundant. However, the core workflow sections are reasonably efficient. | 2 / 3 |
Actionability | The skill provides fully executable Python code throughout, with concrete commands for each pipeline step. Code examples are copy-paste ready with real function signatures, specific parameter values, and CLI commands for bundled scripts. The quality metrics query with pandas syntax is particularly actionable. | 3 / 3 |
Workflow Clarity | The 8-step workflow is clearly sequenced with numbered steps. It includes explicit validation checkpoints: checking drift before sorting (with a quantitative threshold of >10μm), detecting bad channels before referencing, and reviewing uncertain units. The motion correction step has a clear feedback loop (estimate → check → correct if needed). | 3 / 3 |
Progressive Disclosure | Excellent progressive disclosure with a concise quick-start section, a standard workflow with essential code, and a well-organized reference table pointing to 10+ detailed guides (all one level deep). Bundled scripts are listed with usage examples. The separation between inline essentials and referenced deep-dives is well-executed. | 3 / 3 |
Total | 11 / 12 Passed |
Validation
90%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 10 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
metadata_version | 'metadata.version' is missing | Warning |
Total | 10 / 11 Passed | |
- Repository
- K-Dense-AI/claude-scientific-skills
- Commit
25e1c0f
- Reviewed
Table of Contents
Is this your skill?
If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.