filtering
Use this model doc whenever the user wants to perform neuroimaging signal denoising with classical temporal filtering methods. This is a non-deep-learning preprocessing route focused on temporal cleaning, frequency selection, and preparation of cleaner time series for downstream analysis.
npx skills add BioTender-max/awesome-bio-agent-skills --skill filtering --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.
# Filtering Model Doc ## Overview Filtering is a classical non-deep-learning method for neuroimaging signal denoising. - Model family: non-deep-learning preprocessing and denoising method - Typical objectives: - remove unwanted frequency content from BOLD time series - retain frequency bands relevant to resting-state or task analysis - prepare cleaner voxel-wise or ROI-wise time series for downstream connectivity, decoding, or statistical analysis - Primary input: preprocessed fMRI time series, optional confounds, optional mask, TR - Primary output: denoised BOLD image, cleaned ROI time series, optional QC summaries In NeuroClaw, this document is model-level guidance for temporal filtering workflows rather than predictive modeling. Upstream preparation should usually be delegated to: - `fmri-skill` for modality-level denoising planning and validated preprocessing sequences - `nilearn-tool` for concrete filtering and cleaned image export **Research use only.** --- ## Quick Start ### 1) Prepare denoising inputs Expected inputs: - preprocessed BOLD image - repetition time (`TR`) - optional confounds TSV - optional brain mask - optional requested frequency band If images are not prepro
- Overview
- Quick Start
- 1) Prepare denoising inputs
- 2) Filtering route
- Input / Output Contract
- Required inputs
- Optional inputs
- Produced outputs
- Recommended Delegation
- When to Use Filtering
- Limitations and Notes
- Reference
delegated through claw-shell after preprocessing is confirmed python skills/nilearn-tool/scripts/preprocess_bold_reference.py \
What does the filtering skill do?
Use this model doc whenever the user wants to perform neuroimaging signal denoising with classical temporal filtering methods. This is a non-deep-learning preprocessing route focused on temporal cleaning, frequency selection, and preparation of cleaner time series for downstream analysis.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill filtering --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.