fmri-skill
Use this skill whenever the user wants to perform fMRI preprocessing, first-level analysis, ROI extraction, functional connectivity, effective connectivity, or atlas-based alignment to MNI152 space using either fMRIPrep, HCP-style pipelines, or CONN Toolbox. Triggers include: 'fmri', 'fMRI analysis', 'functional connectivity', 'effective connectivity', 'ROI extraction', 'seed-based correlation', 'PPI', 'DCM', 'atlas alignment', 'MNI152', 'HCP pipeline', 'CONN toolbox', or any request involving BOLD data.
npx skills add BioTender-max/awesome-bio-agent-skills --skill fmri-skill --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
The skill is an interface that plans and delegates fMRI data processing tasks. It identifies input data, builds a numbered execution plan detailing what to do and which tool will handle each step, and then, after user confirmation, dispatches every step to the appropriate base/tool skills via claw-shell. It ends by saving outputs into a clean fmri_output/ directory.
How it works
- Identify input data (BIDS dataset or preprocessed BOLD files).
- Generate a numbered execution plan that states WHAT needs to be done and WHICH tool skill will handle each step.
- Present the full plan, estimated runtime, resource requirements, and risks to the user and wait for explicit confirmation.
- On confirmation, delegate every step to the appropriate skill via claw-shell.
- After execution, save all outputs in a clean directory structure (fmri_output/).
When to use it
- Triggered by requests involving fMRI preprocessing, first-level analysis, ROI extraction, functional or effective connectivity, or atlas-based alignment to MNI152 using fMRIPrep, HCP-style pipelines, or CONN Toolbox.
- Includes workflows that reference BIDS organization, fMRIPrep preprocessing, XCP-D post-processing, and ROI/connectivity analyses.
What it can touch
- Tools explicitly listed for use: fmriprep-tool, hcppipeline-tool, conn-tool, fsl-tool, bids-organizer, claw-shell
- It wires data through these tools to perform the end-to-end pipeline and stores outputs under fmri_output/.
Caveats
- License for the skill is listed as NOASSERTION; this may affect redistribution.
- The core workflow requires user confirmation before execution.
- The description specifies resting-state and task-based distinctions and assumes availability of required Docker/ licenses for downstream tools.
# fMRI Skill (Modality Layer) ## Overview `fmri-skill` is the NeuroClaw **modality-layer** interface skill responsible for all fMRI data processing and analysis tasks. It strictly follows the NeuroClaw hierarchical design principles: - This skill **only describes WHAT needs to be done** and **which tool skill to delegate to**. - It contains **no implementation code or concrete commands**. - All concrete execution is delegated to existing base/tool skills: `fmriprep-tool`, `hcppipeline-tool`, `conn-tool`, `fsl-tool`, `bids-organizer`, and `claw-shell`. **Core workflow (never bypassed):** 1. Identify input data (BIDS dataset or preprocessed BOLD files). 2. Generate a **numbered execution plan** that clearly states WHAT needs to be done and which tool skill will handle each step. 3. Present the full plan, estimated runtime, resource requirements, and risks to the user and wait for explicit confirmation (“YES” / “execute” / “proceed”). 4. On confirmation, delegate every step to the appropriate skill via `claw-shell`. 5. After execution, save all outputs in a clean directory structure (`fmri_output/`). ## Benchmark-Facing Default Mainline For benchmark-style prompts, choose the narrowes
- Overview
- Benchmark-Facing Default Mainline
- Quick Reference (Common fMRI Tasks – Updated 2026-03-28)
- Installation (Handled by dependency-planner)
- Complete ADNI-Style rsfMRI Processing Workflow
- Recommended 3-Stage Processing Pipeline
- NeuroClaw recommended wrapper script
- Important Notes & Limitations
- Processing Architecture & Runtimes
- Data Requirements & Outputs
- Resting-State Network Considerations
- Key Differences from Task-Based fMRI
- Input Data Requirements
- System & License Requirements
docker run --rm -it \ docker run -ti --rm \
What does the fmri-skill skill do?
Use this skill whenever the user wants to perform fMRI preprocessing, first-level analysis, ROI extraction, functional connectivity, effective connectivity, or atlas-based alignment to MNI152 space using either fMRIPrep, HCP-style pipelines, or CONN Toolbox. Triggers include: 'fmri', 'fMRI analysis', 'functional connectivity', 'effective connectivity', 'ROI extraction', 'seed-based correlation', 'PPI', 'DCM', 'atlas alignment', 'MNI152', 'HCP pipeline', 'CONN toolbox', or any request involving BOLD data.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill fmri-skill --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.