Neuropixels Data Analysis

What it does

When your agent uses it

• Loading and exploring SpikeGLX or Open Ephys recordings
• Running spike sorting with Kilosort4 on Neuropixels data
• Computing quality metrics and curating units with Allen or IBL criteria
• Checking and correcting motion drift in a recording
• Exporting sorted units to Phy for manual review

Example prompts

• “Load my SpikeGLX recording from ./data/session1 and plot the drift map.”
• “Run Kilosort4 on this Neuropixels 2.0 recording and compute quality metrics.”
• “Curate the sorted units using IBL criteria and export them to Phy.”

Requirements

• Python with SpikeInterface
• A GPU for Kilosort4

Workflow steps

8 steps, taken from the step headings in SKILL.md.

1. 1Preprocessing
2. 2Check and Correct Drift
3. 3Spike Sorting
4. 4Postprocessing
5. 5Curation
6. 6AI-Assisted Curation (For Uncertain Units)
7. 7Generate Analysis Report
8. 8Export Results

What it can do on your machine

Read from SKILL.md and the folder at commit 14680ec. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  Ships 5 files in scripts/ (Python, from the files we listed), which the agent can run.

  Shell commands in SKILL.md call:

  • pip
  • python

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  Links to these hosts (documentation or services it may open):

  • github.com
  • spikeinterface.readthedocs.io

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names no API keys, tokens, secrets or passwords.

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Neuropixels Data Analysis loads about 2.8k tokens when it runs, and up to ~9.7k if it reads all its reference files. Until then it costs about 116 tokens; SKILL.md has 513 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~116

When it runs · the whole SKILL.md, loaded when a task matches

~2.8k

With references · SKILL.md plus every file in references/, read only if the agent opens them

~9.7k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from davila7/claude-code-templates at commit 14680ec, republished under its MIT licence (© davila7). 513 words, ~2,811 tokens.

name

neuropixels-analysis

description

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.

import spikeinterface.full as si
import neuropixels_analysis as npa

# Configure parallel processing
job_kwargs = dict(n_jobs=-1, chunk_duration='1s', progress_bar=True)

# SpikeGLX (most common)
recording = si.read_spikeglx('/path/to/data', stream_id='imec0.ap')

# Open Ephys (common for many labs)
recording = si.read_openephys('/path/to/Record_Node_101/')

# Check available streams
streams, ids = si.get_neo_streams('spikeglx', '/path/to/data')
print(streams)  # ['imec0.ap', 'imec0.lf', 'nidq']

# For testing with subset of data
recording = recording.frame_slice(0, int(60 * recording.get_sampling_frequency()))

# Run full analysis pipeline
results = npa.run_pipeline(
    recording,
    output_dir='output/',
    sorter='kilosort4',
    curation_method='allen',
)

# Access results
sorting = results['sorting']
metrics = results['metrics']
labels = results['labels']

# Recommended preprocessing chain
rec = si.highpass_filter(recording, freq_min=400)
rec = si.phase_shift(rec)  # Required for Neuropixels 1.0
bad_ids, _ = si.detect_bad_channels(rec)
rec = rec.remove_channels(bad_ids)
rec = si.common_reference(rec, operator='median')

# Or use our wrapper
rec = npa.preprocess(recording)

# Check for drift (always do this!)
motion_info = npa.estimate_motion(rec, preset='kilosort_like')
npa.plot_drift(rec, motion_info, output='drift_map.png')

# Apply correction if needed
if motion_info['motion'].max() > 10:  # microns
    rec = npa.correct_motion(rec, preset='nonrigid_accurate')

# Kilosort4 (recommended, requires GPU)
sorting = si.run_sorter('kilosort4', rec, folder='ks4_output')

# CPU alternatives
sorting = si.run_sorter('tridesclous2', rec, folder='tdc2_output')
sorting = si.run_sorter('spykingcircus2', rec, folder='sc2_output')
sorting = si.run_sorter('mountainsort5', rec, folder='ms5_output')

# Check available sorters
print(si.installed_sorters())

# Create analyzer and compute all extensions
analyzer = si.create_sorting_analyzer(sorting, rec, sparse=True)

analyzer.compute('random_spikes', max_spikes_per_unit=500)
analyzer.compute('waveforms', ms_before=1.0, ms_after=2.0)
analyzer.compute('templates', operators=['average', 'std'])
analyzer.compute('spike_amplitudes')
analyzer.compute('correlograms', window_ms=50.0, bin_ms=1.0)
analyzer.compute('unit_locations', method='monopolar_triangulation')
analyzer.compute('quality_metrics')

metrics = analyzer.get_extension('quality_metrics').get_data()

# Allen Institute criteria (conservative)
good_units = metrics.query("""
    presence_ratio > 0.9 and
    isi_violations_ratio < 0.5 and
    amplitude_cutoff < 0.1
""").index.tolist()

# Or use automated curation
labels = npa.curate(metrics, method='allen')  # 'allen', 'ibl', 'strict'

from anthropic import Anthropic

# Setup API client
client = Anthropic()

# Analyze uncertain units visually
uncertain = metrics.query('snr > 3 and snr < 8').index.tolist()

for unit_id in uncertain:
    result = npa.analyze_unit_visually(analyzer, unit_id, api_client=client)
    print(f"Unit {unit_id}: {result['classification']}")
    print(f"  Reasoning: {result['reasoning'][:100]}...")

# Generate comprehensive HTML report with visualizations
report_dir = npa.generate_analysis_report(results, 'output/')
# Opens report.html with summary stats, figures, and unit table

# Print formatted summary to console
npa.print_analysis_summary(results)

# Export to Phy for manual review
si.export_to_phy(analyzer, output_folder='phy_export/',
                 compute_pc_features=True, compute_amplitudes=True)

# Export to NWB
from spikeinterface.exporters import export_to_nwb
export_to_nwb(rec, sorting, 'output.nwb')

# Save quality metrics
metrics.to_csv('quality_metrics.csv')

python scripts/preprocess_recording.py /path/to/data --output preprocessed/

python scripts/run_sorting.py preprocessed/ --sorter kilosort4 --output sorting/

python scripts/compute_metrics.py sorting/ preprocessed/ --output metrics/ --curation allen

python scripts/export_to_phy.py metrics/analyzer --output phy_export/

cp assets/analysis_template.py my_analysis.py
# Edit parameters and run
python my_analysis.py

# Core packages
pip install spikeinterface[full] probeinterface neo

# Spike sorters
pip install kilosort          # Kilosort4 (GPU required)
pip install spykingcircus     # SpykingCircus2 (CPU)
pip install mountainsort5     # Mountainsort5 (CPU)

# Our toolkit
pip install neuropixels-analysis

# Optional: AI curation
pip install anthropic

# Optional: IBL tools
pip install ibl-neuropixel ibllib

project/
├── raw_data/
│   └── recording_g0/
│       └── recording_g0_imec0/
│           ├── recording_g0_t0.imec0.ap.bin
│           └── recording_g0_t0.imec0.ap.meta
├── preprocessed/           # Saved preprocessed recording
├── motion/                 # Motion estimation results
├── sorting_output/         # Spike sorter output
├── analyzer/               # SortingAnalyzer (waveforms, metrics)
├── phy_export/             # For manual curation
├── ai_curation/            # AI analysis reports
└── results/
    ├── quality_metrics.csv
    ├── curation_labels.json
    └── output.nwb

© davila7, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 18 other files (scripts, references, assets) in cli-tool/components/skills/scientific/neuropixels-analysis of davila7/claude-code-templates.

• SKILL.md
• AI_CURATION.md
• ANALYSIS.md
• AUTOMATED_CURATION.md
• LICENSE.txt
• MOTION_CORRECTION.md
• PREPROCESSING.md
• QUALITY_METRICS.md
• SPIKE_SORTING.md
• assets/analysis_template.py
• references/api_reference.md
• references/plotting_guide.md
• references/standard_workflow.md
• scripts/compute_metrics.py
• scripts/explore_recording.py
• scripts/export_to_phy.py
• scripts/neuropixels_pipeline.py
• scripts/preprocess_recording.py
• … and 1 more

Open the folder on GitHubat commit 14680ec

Used in 10 other repositories

We found 19 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 10 other GitHub owners. This page covers the copy in davila7/claude-code-templates, which our catalogue first saw on October 7, 2026.

• aiskillstore/marketplaceskills/davila7/neuropixels-analysisskills/k-dense-ai/neuropixels-analysis
• majiayu000/claude-skill-registryskills/ai-ml/neuropixels-analysisskills/ai-ml/scientific-neuropixels-analysis+7 more
• lamm-mit/scienceclawskills/neuropixels-analysis
• agent-skills-hub/agent-skills-hubskills/neuropixels-analysis
• foryourhealth111-pixel/Vibe-Skillsbundled/skills/neuropixels-analysis
• LeonChaoX/qinyan-academic-skillsskills/07-临床医学与精准医疗/neuropixels-analysis
• wu-yc/LabClawskills/med/neuropixels-analysis
• FreedomIntelligence/OpenClaw-Medical-Skillsskills/neuropixels-analysis
• BioTender-max/awesome-bio-agent-skillsskills/kdense/neuropixels-analysis
• infometa/workbuddyskillsplugins/codebuddy-plugins-official/external_plugins/scientific-skills/neuropixels-analysis