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.
72
88%
Does it follow best practices?
Impact
—
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 thoroughly covers a highly specialized domain. It lists concrete actions, provides comprehensive trigger terms that domain users would naturally use, includes an explicit 'Use when...' clause, and occupies a very distinct niche with virtually no conflict risk. The description is information-dense without being padded, and uses 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 a neuroscience user would say: Neuropixels, SpikeGLX, Open Ephys, Kilosort, spike sorting, extracellular electrophysiology, neural recordings, quality metrics, unit curation. These are the exact terms domain users would use. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive niche domain — Neuropixels neural recording analysis is extremely specific and unlikely to conflict with other skills. The trigger terms (Neuropixels, SpikeGLX, Kilosort, spike sorting) are unique to this domain. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
77%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, highly actionable skill with a clear multi-step workflow and concrete executable code throughout. Its main weakness is verbosity — several sections (hardware tables, installation, project structure, 'when to use' list) could be trimmed or moved to reference files to reduce token consumption. The progressive disclosure structure is well-designed in theory but cannot be verified without bundle files, and the main file retains too much content that belongs in references.
Suggestions
Move the 'Supported Hardware & Formats' tables, 'Installation', 'Project Structure', and 'Additional Resources' sections to reference files to reduce the main SKILL.md token footprint.
Remove or significantly trim the 'When to Use This Skill' section — this duplicates the YAML description and is metadata, not actionable instruction.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is reasonably well-organized but includes some unnecessary content: the 'When to Use This Skill' section largely duplicates the description, the 'Supported Hardware & Formats' table explains things Claude likely knows, the 'Project Structure' section is somewhat obvious, and the 'Additional Resources' with external URLs adds bulk. The installation section also explains basic pip commands. However, the core workflow sections are fairly lean. | 2 / 3 |
Actionability | The skill provides fully executable, copy-paste ready Python code throughout - from loading data through preprocessing, sorting, postprocessing, curation, and export. CLI commands for bundled scripts are concrete, and the quality metrics query uses real pandas syntax with specific threshold values. | 3 / 3 |
Workflow Clarity | The 8-step workflow is clearly sequenced with explicit validation checkpoints: checking drift before sorting (with a quantitative threshold of >10μm), detecting and removing bad channels before CAR, and the 'Common Pitfalls' section reinforces critical ordering. The drift check includes a feedback loop (estimate → check threshold → correct if needed). | 3 / 3 |
Progressive Disclosure | The skill has a clear overview with well-signaled references to 10+ detailed reference files and bundled scripts, which is good structure. However, no bundle files were provided, so we cannot verify these references exist. Additionally, the SKILL.md itself is quite long (~300 lines) and includes content that could be pushed to reference files (e.g., installation, project structure, supported hardware tables, additional resources). | 2 / 3 |
Total | 10 / 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
cbcae7b
- Reviewed
Table of Contents
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