Scientific Agent Skills is a collection of reusable procedures that give AI agents capabilities for scientific research across areas such as biology, chemistry, medicine, and drug discovery. It is used by researchers and by people building AI scientist workflows with compatible coding agents. The catalogue contains many of the project's skills and supporting instructions.
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 skills add K-Dense-AI/scientific-agent-skills --skill neuropixels-analysisgit clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-skillsWrote 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/k-dense-ai/scientific-agent-skills/neuropixels-analysis)<a href="https://agentmods.dev/skills/k-dense-ai/scientific-agent-skills/neuropixels-analysis"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/neuropixels-analysis/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/k-dense-ai/scientific-agent-skills/neuropixels-analysis"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/neuropixels-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium analysis-evasion · line 1 Suspicious Unicode normalization or mixed-script contentFix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
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 | $0.00098 | $0.04307 |
| Opus 5 | $0.00049 | $0.02153 |
| Sonnet 5 | $0.00020 | $0.00861 |
| Haiku 4.5 | $0.00010 | $0.00431 |
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 8d 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
2 near-identical copies found in the catalogue:
- neuropixels-analysis — 91% identical, 28 lines differ
- neuropixels-analysis — 91% identical, 19 lines differ
How it starts
The opening of the file, as written. The whole thing — 430 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Neuropixels Data Analysis
Overview
Toolkit for analyzing Neuropixels high-density neural recordings using current best practices from SpikeInterface, the Allen Institute, and the International Brain Laboratory (IBL). It covers the full workflow from raw data to publication-ready curated units.
All examples use the real SpikeInterface API (spikeinterface.full as si) plus the
companion curation module (spikeinterface.curation as sc). The skill ships runnable
scripts in scripts/ and a copy-and-edit template in assets/ that implement this
workflow directly on top of SpikeInterface — there is no separate package to install
beyond the dependencies listed under Installation.
When to Use This Skill
This skill should be used when:
- Working with Neuropixels recordings (
.ap.bin,.lf.bin,.metafiles) - Loading data from SpikeGLX, Open Ephys, or NWB formats
- Preprocessing neural recordings (filtering, common reference, bad-channel detection)
- Detecting and correcting motion/drift
- Running spike sorting (Kilosort4, SpykingCircus2, Mountainsort5, Tridesclous2)
- Computing quality metrics (SNR, ISI violations, presence ratio, amplitude cutoff)
- Curating units (threshold-based, model-based, or AI-assisted)
- Creating visualizations and exporting to Phy or NWB
Supported Hardware & Formats
| Probe | Electrodes | Channels | Notes |
|---|---|---|---|
| Neuropixels 1.0 | 960 | 384 | Use phase_shift for ADC 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
Import and configure parallel processing
import spikeinterface.full as si
# Global job kwargs are reused by all parallelizable steps
si.set_global_job_kwargs(n_jobs=-1, chunk_duration="1s", progress_bar=True)
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 5.7 KB
- references/ANALYSIS.md 9.6 KB
- references/api_reference.md 7.2 KB
- references/AUTOMATED_CURATION.md 13 KB
- references/MOTION_CORRECTION.md 8.4 KB
- references/plotting_guide.md 11 KB
- references/PREPROCESSING.md 6.5 KB
- references/QUALITY_METRICS.md 9.2 KB
- references/SPIKE_SORTING.md 7.6 KB
- references/standard_workflow.md 9.2 KB
- scripts/compute_metrics.py 5.5 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 4.2 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.
- 8d ago First seen · 430 lines · 98 tokens per session scan A 3eadefe63c27
neuropixels-analysis is a skill published in the GitHub repository K-Dense-AI/scientific-agent-skills (44,220 stars, last pushed 4d ago), licensed MIT. It adds 98 tokens to every session and 4,307 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 2026-09-03.
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