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/synthetic-sciences/openscience/neuropixels-analysisnpx skills add synthetic-sciences/openscience --skill neuropixels-analysisgit clone --depth 1 https://github.com/synthetic-sciences/openscienceWhat 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 | $0.00109 | $0.02932 |
| Opus 5 | $0.00055 | $0.01466 |
| Sonnet 5 | $0.00022 | $0.00586 |
| Haiku 4.5 | $0.00011 | $0.00293 |
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 3d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- alterlab-neuropixels — 89% identical, 140 lines differ
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.
- 3d ago First seen · 350 lines · 109 tokens per session scan A 5e73ee7c26d3
neuropixels-analysis is a skill published in the GitHub repository synthetic-sciences/openscience (3,385 stars, last pushed yesterday), licensed Apache-2.0. It adds 109 tokens to every session and 2,932 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
squash-merge
Squash-merge a PR into zeroclaw-labs/zeroclaw master with fully preserved commit history in the squash message body. Use this skill when the user explicitly mentions squash-merging, merging a specific PR number, landing a PR, or 合入 — e.g. "squash-merge #123", "merge PR 456", "land #789", "合入 #123", "/squash-merge…
zeroclaw
Help users operate and interact with their ZeroClaw agent instance — through both the CLI (zeroclaw commands) and the REST/WebSocket gateway API. Use this skill whenever the user wants to: send messages to ZeroClaw, manage memory or cron jobs, check system status, configure channels or providers, hit the gateway API…
github-pr
Open or update a GitHub Pull Request for ZeroClaw. Handles creating new PRs with a fully filled-out template body, and updating existing PRs (title, body sections, labels, comments). Use this skill whenever the user wants to open a PR, create a pull request, update a PR, edit PR description, add labels to a PR, or…
feature-matrix-parity
Update the OpenClaw and Hermes comparison columns of the ZeroClaw feature-and-support matrix. Use this skill when the user wants to refresh, fill, or verify parity data in docs/book/feature-matrix-parity.toml, add a new comparison row or section to the feature matrix, or re-walk the competitor repos for support…
meta-paper-write
Use this meta-skill instead of answering directly when the current user asks to draft or produce a new academic/research paper or LaTeX manuscript. It uses multi-skill orchestration for manuscript workflows that need source search, citation planning, experiment or figure/table placeholders, drafting, length checks…
peer-review
Structured manuscript/grant review with checklist-based evaluation. Use when writing formal peer reviews with specific criteria methodology assessment, statistical validity, reporting standards compliance (CONSORT/STROBE), and constructive feedback. Best for actual review writing, manuscript revision. For evaluating…