Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/x-cmd/skill/neuropixels-analysisnpx skills add x-cmd/skill --skill neuropixels-analysisgit clone --depth 1 https://github.com/x-cmd/skillWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/x-cmd/skill/neuropixels-analysis)<a href="https://agentmods.dev/skills/x-cmd/skill/neuropixels-analysis"><img src="https://agentmods.dev/badge/skills/x-cmd/skill/neuropixels-analysis.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00109 | $0.03090 |
| Opus 5 | $0.00055 | $0.01545 |
| Sonnet 5 | $0.00022 | $0.00618 |
| Haiku 4.5 | $0.00011 | $0.00309 |
Grade A, and why
neuropixels-analysis scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
89% identical to neuropixels-analysis — 6 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 350 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Neuropixels Data Analysis
Overview
Comprehensive toolkit for analyzing Neuropixels high-density neural recordings using current best practices from SpikeInterface, Allen Institute, and International Brain Laboratory (IBL). Supports the full workflow from raw data to publication-ready curated units.
When to Use This Skill
This skill should be used when:
- Working with Neuropixels recordings (.ap.bin, .lf.bin, .meta files)
- Loading data from SpikeGLX, Open Ephys, or NWB formats
- Preprocessing neural recordings (filtering, CAR, bad channel detection)
- Detecting and correcting motion/drift in recordings
- Running spike sorting (Kilosort4, SpykingCircus2, Mountainsort5)
- Computing quality metrics (SNR, ISI violations, presence ratio)
- Curating units using Allen/IBL criteria
- Creating visualizations of neural data
- Exporting results to Phy or NWB
Supported Hardware & Formats
| Probe | Electrodes | Channels | Notes |
|---|---|---|---|
| Neuropixels 1.0 | 960 | 384 | Requires phase_shift correction |
| Neuropixels 2.0 (single) | 1280 | 384 | Denser geometry |
| Neuropixels 2.0 (4-shank) | 5120 | 384 | Multi-region recording |
| Format | Extension | Reader |
|---|---|---|
| SpikeGLX | .ap.bin, .lf.bin, .meta |
si.read_spikeglx() |
| Open Ephys | .continuous, .oebin |
si.read_openephys() |
| NWB | .nwb |
si.read_nwb() |
Quick Start
Basic Import and Setup
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)
Loading Data
# 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()))
What ships with it
17 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- assets/analysis_template.py 8.8 KB runs code
- references/AI_CURATION.md 8.4 KB
- references/ANALYSIS.md 9.6 KB
- references/api_reference.md 8.0 KB
- references/AUTOMATED_CURATION.md 9.8 KB
- references/MOTION_CORRECTION.md 8.5 KB
- references/plotting_guide.md 11 KB
- references/PREPROCESSING.md 6.5 KB
- references/QUALITY_METRICS.md 9.2 KB
- references/SPIKE_SORTING.md 7.7 KB
- references/standard_workflow.md 8.4 KB
- scripts/compute_metrics.py 5.1 KB runs code
- scripts/explore_recording.py 5.4 KB runs code
- scripts/export_to_phy.py 2.4 KB runs code
- scripts/neuropixels_pipeline.py 13 KB runs code
- scripts/preprocess_recording.py 4.1 KB runs code
- scripts/run_sorting.py 2.5 KB runs code
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 350 lines · 109 tokens per session scan A e332bc1a3eff
neuropixels-analysis is a skill published in the GitHub repository x-cmd/skill (26 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 109 tokens to every session and 3,090 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 89% identical to neuropixels-analysis, differing in 6 lines, and is treated as a copy.
Other skills, from other repositories
kaggle-research-compute
Use when a research or engineering task needs automatic heavy-compute routing to free Kaggle Kernels through the local broker, with agent-driven push, poll, fetch, and a multi-run resume loop across concurrent kernels; free CPU (quota-free) and GPU under a self-imposed weekly GPU-hour cap.
lean-strict-verification-gate
Use when checking whether a Lean artifact can safely support a research claim.
modal-research-compute
Use when a research or engineering task needs automatic heavy-compute routing through the unified local broker, including Modal-backed remote CPU, high-memory CPU, or GPU execution.
digest-bridge
Use when the user wants to extract arXiv IDs or DOIs from research or RSS digests and turn them into getscipapers requests or manifests.
lean-research-library
Use when any Lean formalization task starts (reuse Mathlib and the personal research library before proving anything new) and when it ends (gate finished results into the library and flag mathlib-PR candidates, always asking the user first). Also scaffolds and publishes paper artifacts from the personal template.
venue-ranking-evidence
Use when identifying a journal, conference, or proceedings series from a partial name, acronym, alias, ISSN, or source ID; preserving source-specific rank, quartile, metric, classification, membership, or coverage observations; or proving that the public ICORE detail page displayed one ICORE claim. Live bulk paths are…