--- name: bold5000-skill description: "Use this skill whenever the user wants an end-to-end workflow for the BOLD5000 dataset, including download, BIDS organization, and processing of task-fMRI data with visual image stimuli. Triggers include: 'BOLD5000', 'BOLD 5000', 'process BOLD5000', 'visual fMRI', or any request to run the BOLD5000 pipeline. This is the NeuroClaw dataset-orchestration layer for BOLD5000." license: MIT License (NeuroClaw custom skill - freely modifiable within the project) layer: subagent skill_type: dataset dependencies: - smri-skill - fmri-skill - bids-organizer - claw-shell --- # BOLD5000 Skill (Dataset-Orchestration Layer) ## Overview `bold5000-skill` is the NeuroClaw orchestration skill for the **BOLD5000** dataset. BOLD5000 is a high-density repeated visual fMRI dataset with 8 participants performing 5,000-image visual recognition tasks. It is designed for studying visual object recognition and scene understanding. It coordinates a fixed three-phase workflow: 1. Download BOLD5000 data from the OpenNeuro repository. 2. Prepare and validate BIDS-style data organization for downstream processing. 3. Delegate modality pipelines to `smri-skill` and `fmri-skill`. This skill follows NeuroClaw hierarchy: - Defines **WHAT to do**, not low-level implementation details. - Does **not** execute direct shell commands itself. - Delegates all execution via `claw-shell` to base/tool skills. **Research use only.** --- ## Download Stage (Mandatory First Step) ### Source BOLD5000 data is available on OpenNeuro: - Website: https://bold5000-dataset.github.io/ - OpenNeuro: https://openneuro.org/datasets/ds002785 ### Supported BOLD5000 Data Packages - **Imaging data**: T1w structural, task-fMRI (NIfTI format) - **Stimulus data**: 5,000 natural images with category labels and image metadata - **Behavioral data**: Recognition memory judgments, response times - **Participants**: 4 participants x ~1250 images each (high-density repeated measures) ### Delegation Rules for Download - Environment/setup checks: `dependency-planner` + `conda-env-manager` - OpenNeuro dataset download: `claw-shell` (via `openneuro` CLI or `datalad`) - Optional raw-data organization to BIDS-style staging: `bids-organizer` ### Download Inputs to Confirm in Plan - Target subset (all subjects, specific subjects) - Whether to include stimulus images - Destination directory with sufficient disk space --- ## Narrow Path: BOLD5000 Raw NIfTI -> BIDS Staging Use this path when the task only asks to reorganize raw BOLD5000 NIfTI files into a BIDS-style dataset and does not require preprocessing or downstream analysis. ### Expected narrow-path behavior 1. BOLD5000 data from OpenNeuro is already in BIDS format; verify and validate structure. 2. Route modalities: - T1w -> `anat/*_T1w` - task-fMRI -> `func/*_task-*_bold` 3. Preserve stimulus metadata and event files. 4. Emit dataset-level outputs such as `dataset_description.json`, `participants.tsv`. --- ## Core Workflow (Never Bypassed) 1. Identify user target: download, BIDS staging, or full preprocessing. 2. Generate a numbered plan with tools, outputs, runtime, storage, and risks. 3. Wait for explicit confirmation (`YES` / `execute` / `proceed`). 4. On confirmation, run download stage first (if needed). 5. After download success, verify/prepare BIDS staging using `scripts/reorganize_bold5000.py`. 6. Delegate to modality skills: - `smri-skill` for structural MRI (T1w) - `fmri-skill` for task-fMRI 7. If stimulus analysis is requested, use `scripts/extract_bold5000_stimulus.py` to generate stimulus metadata. 8. Save outputs into a BOLD5000-centered structure under `bold5000_output/`. --- ## Stimulus Metadata Extraction ### Script: `scripts/extract_bold5000_stimulus.py` Extracts and organizes BOLD5000 stimulus metadata for downstream analysis. ```bash python skills/bold5000-skill/scripts/extract_bold5000_stimulus.py \ --stimulus-dir /path/to/bold5000_raw/stimuli \ --output /path/to/bold5000_output/stimulus/stimulus_metadata.csv ``` Features: - Reads stimulus image file names and paths - Extracts category labels (object, scene, etc.) - Generates per-image metadata CSV for modeling - Links stimulus presentation events to fMRI volumes --- ## QC Integration ### Script: `scripts/bold5000_qc_summary.py` ```bash python skills/bold5000-skill/scripts/bold5000_qc_summary.py \ --fmriprep-dir /path/to/bold5000_output/fmriprep \ --output /path/to/bold5000_output/qc/qc_summary.csv \ --fd-threshold 0.3 ``` --- ## Recommended Output Layout All assets should be organized under `./bold5000_output/`: - `bold5000_output/raw/` (downloaded original BOLD5000 files) - `bold5000_output/bids/` (BIDS data) - `bold5000_output/smri/` (links or copies from `smri_output/`) - `bold5000_output/fmri/` (links or copies from `fmri_output/`) - `bold5000_output/stimulus/` (stimulus metadata and event files) - `bold5000_output/qc/` (QC summaries) - `bold5000_output/logs/` (download + orchestration logs) --- ## Benchmark Adapter Guidance For benchmark-style prompts, do not force the full `download -> staging -> multimodal processing` orchestration when the task is only asking for local BOLD5000 data staging or organization. - If the task starts from raw BOLD5000 data already present on disk and only asks for BIDS-style staging / validation: - skip the mandatory download stage - default to the narrow path `local raw BOLD5000 discovery -> BIDS validation -> minimal metadata -> report` - In benchmark mode, do not require explicit confirmation before presenting the direct staging solution. --- ## Safety and Execution Policy - No execution before explicit plan confirmation. - All execution must be routed via `claw-shell`. - Missing dependencies must be resolved by `dependency-planner` before running. --- ## Important Notes and Limitations - BOLD5000 is a small dataset (4 participants); statistical power is limited for group-level analyses. - BOLD5000 uses high-density repeated image presentations; analysis requires handling of repeated measures. - BOLD5000 data from OpenNeuro is already in BIDS format; re-staging may not be needed. - Stimulus images are included in the dataset; event files reference image file names. - `bold5000-skill` is orchestration-only; detailed preprocessing logic remains in `smri-skill` and `fmri-skill`. --- ## When to Call This Skill - User asks for end-to-end BOLD5000 workflow. - User asks to download BOLD5000 data and then run task-fMRI processing. - User needs BIDS validation for BOLD5000 data. - User asks to extract BOLD5000 stimulus metadata. - User needs BOLD5000-specific QC summaries. --- ## Complementary / Related Skills - `smri-skill` - `fmri-skill` - `bids-organizer` - `fmriprep-tool` - `dependency-planner` - `conda-env-manager` - `claw-shell` --- ## Reference - BOLD5000: https://bold5000-dataset.github.io/ - Chang et al., 2019, *BOLD5000: A public fMRI dataset of 5,000 images* - BIDS spec: https://bids.neuroimaging.io/ Created At: 2026-05-06 01:52 HKT Last Updated At: 2026-05-06 01:52 HKT Author: chengwang96