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 magic3007/dotfiles --skill neuropixels-analysisgit clone --depth 1 https://github.com/magic3007/dotfilesWrote 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/magic3007/dotfiles/neuropixels-analysis)<a href="https://agentmods.dev/skills/magic3007/dotfiles/neuropixels-analysis"><img src="https://agentmods.dev/badge/skills/magic3007/dotfiles/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 | $0.02938 |
| Opus 5 | $0.00055 | $0.01469 |
| Sonnet 5 | $0.00022 | $0.00588 |
| Haiku 4.5 | $0.00011 | $0.00294 |
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 4d 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
92% identical to neuropixels-analysis — 30 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.
- 4d ago First seen · 350 lines · 109 tokens per session scan A f591f6143362
neuropixels-analysis is a skill published in the GitHub repository magic3007/dotfiles (11 stars, last pushed today), licensed MIT. It adds 109 tokens to every session and 2,938 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 92% identical to neuropixels-analysis, differing in 30 lines, and is treated as a copy.
Other skills, from other repositories
assess-quality
Foundational quality framework: the five questions (readable, easy to start, expands without bloat, consistent, intentional) every other dev skill is judged against, plus the dual-audience and workshop principles. Use when onboarding to a project, defining a quality bar, setting an assessment checklist, or arbitrating…
create-oss-skill
Create well-formed Agent Skills following the agentskills.io specification. Scaffold directories, write SKILL.md files, bundle scripts, and structure instructions for progressive disclosure. Use when creating a new skill, reviewing skill structure, optimizing a skill description, or setting up evals for skill quality.
orchestrate-agents
Orchestrate multiple agent CLIs (Claude, Codex, Antigravity) via tmux with a shared fleet store, dispatching one guardian subagent per pane. Survey-first: inspects and adopts existing tmux sessions, windows, and agent panes before creating anything new. Use when running a multi-agent session, dispatching parallel…
scaffold-project
Generates cross-language standard files (README, AGENTS.md, LICENSE, CONTRIBUTING.md, SECURITY.md, sr.yaml, .envrc, llms.txt), documentation conventions, and project structure, then dispatches to language-specific scaffolds. Use first for cross-language standard files and structure, THEN load the matching scaffold…
test-code
Testing philosophy, test types (unit, integration, golden, fuzz, property, benchmark, smoke, E2E), per-language conventions (Rust, Go, Python, TypeScript), file organization, fixtures/mocks, CI strategy, and what NOT to test. Use when writing tests, reviewing test coverage, setting up test infrastructure, or deciding…
extend-oss-skills-to-claude
Extend standard agentskills.io skills with Claude Code-specific features. Invocation control, subagent execution, dynamic context injection, string substitutions, model/effort overrides, and deployment scoping. Use when adapting a portable skill for Claude Code, adding Claude-specific frontmatter, setting up subagent…