Skills neuropixels-analysis

📦

neuropixels-analysis

Name: neuropixels-analysis
Author: davila7

Content revision r1 Medium Risk 🔑 Env variables📁 Filesystem access⚡ Contains scripts🌐 Network access⚙️ External commands

Analyze Neuropixels Recordings

Neuropixels analysis requires many linked preprocessing, sorting, and curation steps. This skill provides practical SpikeInterface workflows, scripts, and references for reproducible neural recording analysis.

Supports: Claude Codex Code(CC)

🥉 77 Bronze

Install with my Agent

Copy this request to your Agent. It includes the canonical Skill page and manifest.

Agent request

Review the Skillstore skill "neuropixels-analysis" from https://skillstore.io/skills/davila7-neuropixels-analysis.md and its manifest at https://skillstore.io/api/skills/davila7-neuropixels-analysis/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.

Your Agent should still show its plan and request any confirmation required by the security policy.

Agent-readable resources

Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.

Markdown detail GET /skills/davila7-neuropixels-analysis.md Signed manifest GET /api/skills/davila7-neuropixels-analysis/manifest Signed lockfile GET /api/skills/davila7-neuropixels-analysis/lockfile

Test it

Using "neuropixels-analysis". Plan preprocessing for a Neuropixels 1.0 SpikeGLX recording.

Expected outcome:

Load the AP stream and confirm channel count, duration, and sampling rate.
Apply high-pass filtering, phase shift correction, bad channel removal, and common median reference.
Save the preprocessed recording in a new output directory and keep the raw data unchanged.
Generate drift and probe layout plots before selecting a sorter.

Using "neuropixels-analysis". Help interpret quality metrics after Kilosort4.

Expected outcome:

Separate obvious noise units using low SNR and poor presence ratio.
Flag units with refractory period violations for manual review.
Compare Allen, IBL, and strict thresholds before declaring final good units.
Export uncertain units to Phy when the experiment depends on precise curation.

Using "neuropixels-analysis". Create a reporting checklist for a completed analysis.

Expected outcome:

Record probe type, preprocessing parameters, sorter version, and motion correction settings.
Summarize total units, accepted units, rejected units, and key quality metric distributions.
Include drift plots, waveform summaries, correlograms, and curation criteria in the methods record.

Security Audit

Medium Risk

v9 • 7/23/2026 Open versioned report

All 101 static findings were adjudicated. The static alerts are false positives from Markdown examples, placeholder API keys, documentation URLs, scientific domain terms, and expected output writes. One semantic privacy risk remains because AI-assisted curation may send derived neural data to external model providers.

Files scanned

5,375

Lines analyzed

Review items

False positives ignored

Confirmed security concerns (1)

Medium

SKILL.md:159-178 AI_CURATION.md:44-72

External AI Curation Can Share Research Data

The skill describes Anthropic and OpenAI clients for visual unit analysis. This can transmit waveform plots or derived neural recording data to external model providers unless users opt in.

The cited sections explicitly document provider clients and visual unit analysis. The feature appears intended rather than malicious, but it creates a privacy and data governance risk.

Risk Factors

🔑 Env variables (2)

AI_CURATION.md:299 AI_CURATION.md:308

📁 Filesystem access (7)

assets/analysis_template.py:236 AUTOMATED_CURATION.md:341 references/standard_workflow.md:343 scripts/compute_metrics.py:134 scripts/explore_recording.py:161 scripts/neuropixels_pipeline.py:331 scripts/neuropixels_pipeline.py:342

⚡ Contains scripts (2)

AUTOMATED_CURATION.md:158-162 MOTION_CORRECTION.md:92-95

🌐 Network access (11)

AUTOMATED_CURATION.md:40 AUTOMATED_CURATION.md:194 scripts/neuropixels_pipeline.py:11 scripts/neuropixels_pipeline.py:12 SKILL.md:338 SKILL.md:339 SKILL.md:340 SKILL.md:341 SKILL.md:342 SKILL.md:343 SKILL.md:344

⚙️ External commands (48)

Audited by: codex View Audit History →

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Academic citations (APA · BibTeX · CFF)

APA citation

davila7. (2026). neuropixels-analysis security audit report (audit version 9) [Author version unspecified]. Skillstore. https://skillstore.io/skills/davila7-neuropixels-analysis/audits/9

BibTeX citation

@techreport{davila7-davila7-neuropixels-analysis-2026,
  author = {davila7},
  title = {neuropixels-analysis security audit report (audit version 9)},
  institution = {Skillstore},
  year = {2026},
  number = {9},
  url = {https://skillstore.io/skills/davila7-neuropixels-analysis/audits/9},
  note = {Author version unspecified}
}

CITATION.cff

cff-version: 1.2.0
message: "If you use this Skill, cite its author and this versioned security audit report."
title: "neuropixels-analysis security audit report (audit version 9)"
version: "unspecified"
type: report
authors:
  - name: "davila7"
date-released: "2026-07-23"
url: "https://skillstore.io/skills/davila7-neuropixels-analysis/audits/9"
identifiers:
  - type: other
    value: "skillstore:davila7-neuropixels-analysis:audit:9"
    description: "Skillstore immutable audit report identifier"

Compare variants

2 installable variants

Each author remains a separate installable skill. The recommended variant is ranked by Skillstore evidence.

Why this variant is first

Highest Skillstore Score

Author / source Skillstore Score Evidence Confidence Skillstore usage Updated

davila7 Recommended Current

davila7-neuropixels-analysis

Skillstore Score 77

Evidence Confidence High

Skillstore usage 12

Updated

2026-08-21

K-Dense-AI

k-dense-ai-neuropixels-analysis

Skillstore Score 76

Evidence Confidence High

Skillstore usage 22

Updated

2026-08-21

Skillstore Score

Why this score Evidence Confidence: High

Architecture

Maintainability

Content

Community

Spec Compliance

What You Can Build

Prepare a New Recording

Load a SpikeGLX or Open Ephys session, inspect channels, apply preprocessing, and save reusable intermediate data.

Run a Sorting Pipeline

Choose a sorter, run postprocessing, compute quality metrics, and export results for manual review.

Standardize Unit Curation

Apply Allen, IBL, or strict criteria across sessions and document thresholds for reproducible analysis.

Try These Prompts

Plan Basic Preprocessing

I have a Neuropixels SpikeGLX recording at [path]. Help me plan safe preprocessing, expected outputs, and checks before spike sorting.

Choose Motion Correction

My drift plot shows [describe pattern]. Recommend a motion correction strategy and explain which parameters I should inspect.

Compare Sorter Options

I need to sort a Neuropixels recording with [hardware limits]. Compare Kilosort4, SpykingCircus2, and Mountainsort5 for this case.

Audit Curation Criteria

Review these quality metric thresholds and curation labels for a Neuropixels study. Identify weak assumptions and suggest a reproducible reporting plan.

Best Practices

Inspect drift and bad channels before spike sorting, especially for long recordings.
Save preprocessed data and parameter records so analyses can be reproduced.
Use automated curation as a first pass and manually review borderline units.

Avoid

Running spike sorting on raw data without checking drift, probe metadata, or bad channels.
Treating quality metric thresholds as universal across probes, brain regions, and experiments.
Sending plots or metadata to external AI services without confirming data governance rules.