A guide to using fMRIPrep, a pipeline that prepares functional magnetic resonance imaging (fMRI) data for analysis. It works with BIDS datasets, a standard structure for organizing neuroimaging data, and uses containerized tools.

Use it to preprocess task-based or resting-state fMRI data and configure Docker, Singularity, or Apptainer runs.

It helps researchers set up and run a repeatable preprocessing workflow without manually coordinating every imaging step.

fmriprep — Skill for Claude Code and Codex by NeuroAIHub

Install

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.

Any agent

npx skills add NeuroAIHub/BrainPilot --skill fmriprep

Clone the repo

git clone --depth 1 https://github.com/NeuroAIHub/BrainPilot

Made for: Claude Code, Codex.

Wrote 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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/neuroaihub/brainpilot/fmriprep.svg)](https://agentmods.dev/skills/neuroaihub/brainpilot/fmriprep)

Your own site

<a href="https://agentmods.dev/skills/neuroaihub/brainpilot/fmriprep"><img src="https://agentmods.dev/badge/skills/neuroaihub/brainpilot/fmriprep.svg" alt="Measured on agentmods" height="20"></a>

Per session 158 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 4,682 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits

NVIDIA SkillSpector warn 7 Sept 2026

SkillSpector: 4 findings, 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 MCP Rug Pull · line 83
Docker image references without a specific tag (:latest is implicit) or digest (@sha256:...) can be silently replaced by a malicious image.

Fix: Pin the image: image:tag or image@sha256:abc123
medium MCP Rug Pull · line 149
Docker image references without a specific tag (:latest is implicit) or digest (@sha256:...) can be silently replaced by a malicious image.

Fix: Pin the image: image:tag or image@sha256:abc123
medium MCP Rug Pull · line 252
Docker image references without a specific tag (:latest is implicit) or digest (@sha256:...) can be silently replaced by a malicious image.

Fix: Pin the image: image:tag or image@sha256:abc123
low Tool Misuse · line 252
Tool parameters are crafted to achieve unintended or unsafe behavior. Parameter abuse can bypass intended safety checks (e.g. shell=True, --force, dangerous glob patterns).

Fix: Validate all tool parameters against an allowlist. Reject dangerous parameter values (shell=True, --force, -rf /) and use safe defaults.

How audits are shown

Origin unknown No closer match found in the catalogue.

Token cost

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	$0.00158	$0.04682
Opus 5	$0.00079	$0.02341
Sonnet 5	$0.00032	$0.00936
Haiku 4.5	$0.00016	$0.00468

Measured 8d ago against content hash ad7c8330d845, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-07, from the pricing page.

Security

Grade A, and why

fmriprep 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.

packages/skills/skills/06_fMRI_Neuroimaging/fmriprep/SKILL.md · 257 lines

The source is not reproduced here

Licensed AGPL-3.0

The repository is licensed AGPL-3.0, which this catalogue does not treat as permission to reproduce the file. Read it at the source.

Read it on GitHub

Files

What ships with it

12 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.

Changes

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 · 257 lines · 158 tokens per session scan A ad7c8330d845

Subscribe to this mod's changes

fmriprep is a skill published in the GitHub repository NeuroAIHub/BrainPilot (566 stars, last pushed today), licensed AGPL-3.0. It adds 158 tokens to every session and 4,682 once invoked, about $0.0008 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.

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