| name | hcpa-skill |
| description | Use this skill whenever the user wants an end-to-end workflow for the HCP Aging (HCP-A) dataset, including dataset download, BIDS organization, and multimodal processing of sMRI, fMRI, and dMRI. Triggers include: 'HCP Aging', 'HCP-A', 'process HCP Aging data', 'HCP Aging sMRI fMRI', or any request to run the HCP-A multimodal pipeline. |
| license | MIT License (NeuroClaw custom skill - freely modifiable within the project) |
| layer | subagent |
| skill_type | dataset |
| dependencies | ["smri-skill","fmri-skill","dwi-skill","bids-organizer","claw-shell"] |
| complementary_skills | ["hcppipeline-tool"] |
HCP-A Skill (Dataset-Orchestration Layer)
Overview
hcpa-skill is the NeuroClaw orchestration skill for the HCP Aging (HCP-A) dataset.
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 via
claw-shell.
- Companion scripts in
scripts/ provide reference implementations for data reorganization, phenotype extraction, and QC.
Core workflow (never bypassed):
- Identify input HCP-A data and target modalities.
- Generate a numbered execution plan clearly stating 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 ("YES" / "execute" / "proceed").
- On confirmation, delegate every step to the appropriate skill via
claw-shell.
- After execution, save all outputs in a clean directory structure (
hcpa_output/).
Research use only.
Quick Reference
| Task | What needs to be done | Delegate to | Expected output |
|---|
| Data download | Download HCP-A from ConnectomeDB | claw-shell | Raw HCP-A files |
| BIDS staging | Reorganize HCP-A native layout to BIDS | scripts/reorganize_hcpa.py | BIDS-compliant dataset |
| sMRI processing | Brain extraction, tissue segmentation, cortical reconstruction | smri-skill | smri_output/ derivatives |
| fMRI processing | Preprocessing, denoising, connectivity, task GLM | fmri-skill | fmri_output/ derivatives |
| dMRI processing | Eddy correction, tensor metrics, tractography | dwi-skill | dwi_output/ metrics |
| Phenotype extraction | Cognitive, health, demographic data | scripts/extract_hcpa_phenotype.py | Merged phenotype CSV |
| QC summary | Per-subject quality control | scripts/hcpa_qc_summary.py | QC summary + exclusion list |
Download Stage (Mandatory First Step)
Source
HCP-A data is distributed through ConnectomeDB:
Dataset Characteristics
- Cohort: ~700+ adults ages 36-100 years
- Modalities: T1w, T2w, dMRI, rs-fMRI, task-fMRI
- Focus: Normal aging, cognitive decline, brain structure-function changes across the lifespan
- Unique feature: Complements HCP-YA to cover the full adult lifespan (22-100 years)
Download Inputs to Confirm in Plan
- ConnectomeDB credentials/token
- Target modalities (all, structural, functional, diffusion)
- Subject list scope (full or custom subset)
- Destination directory with sufficient disk space
HCP-A Task Paradigms
| Task | Description | Duration |
|---|
| MOTOR | Finger tapping, toe movement, tongue movement | ~3 min |
| EMOTION | Faces and shapes matching | ~2 min |
| GAMBLING | Card guessing with reward/loss | ~3 min |
| LANGUAGE | Story comprehension and math | ~4 min |
| RELATIONAL | Relational reasoning matching | ~3 min |
| SOCIAL | Social cognition (mentalizing) movie clips | ~3 min |
| WM | Working memory (faces, places, tools, body parts) | ~5 min |
| REST | Resting-state (eyes open) | ~15 min × 4 runs |
BIDS Preparation
Script: scripts/reorganize_hcpa.py
Converts HCP-A native directory structure to BIDS-compliant layout.
python skills/hcpa-skill/scripts/reorganize_hcpa.py \
--input /path/to/HCPA/raw \
--output /path/to/HCPA/bids \
--participants /path/to/subject_list.txt
Features:
- Subject ID normalization: HCP format to BIDS
sub- labels
- Session handling: multiple visits if applicable
- Modality routing: T1w, T2w, dMRI, rs-fMRI, task-fMRI
- Sidecar JSON generation from HCP metadata
dataset_description.json and participants.tsv generation- Dry-run mode:
--dry-run to preview without copying
Core Workflow (Never Bypassed)
- Identify user target: full HCP-A processing, imaging subset, phenotype extraction, or BIDS staging only.
- Generate a numbered plan with tools, outputs, runtime, storage, and risks.
- Wait for explicit confirmation (
YES / execute / proceed).
- On confirmation, run download stage first (if needed).
- After download success, run BIDS preparation using
scripts/reorganize_hcpa.py.
- Delegate to
smri-skill for structural MRI processing.
- Delegate to
fmri-skill for functional MRI processing.
- Delegate to
dwi-skill for diffusion MRI processing.
- If phenotype extraction is requested, run
scripts/extract_hcpa_phenotype.py.
- If QC summary is requested, run
scripts/hcpa_qc_summary.py.
- Save outputs into
hcpa_output/.
Modality Processing Delegation
| Modality | Delegated skill | Typical tasks | Main outputs |
|---|
| sMRI (T1w/T2w) | smri-skill | brain extraction, tissue segmentation, cortical reconstruction, ROI morphometry | smri_output/ derivatives |
| fMRI (rs-fMRI/task-fMRI) | fmri-skill | preprocessing, denoising, ROI time series, connectivity, task GLM | fmri_output/ derivatives |
| dMRI (DWI) | dwi-skill | eddy correction, tensor metrics, tractography, connectome | dwi_output/ metrics |
Standard Output Layout
hcpa_output/
├── raw/ # Downloaded original HCP-A files
├── bids/ # BIDS-staged data
├── smri/ # Structural MRI derivatives
├── fmri/ # Functional MRI derivatives
├── dwi/ # Diffusion MRI derivatives
├── phenotype/ # Merged phenotype tables
├── qc/ # QC summaries and exclusion lists
└── logs/ # Download + orchestration logs
Benchmark Adapter Guidance
For benchmark-style prompts, do not force the full orchestration when the task only asks for local HCP-A data staging.
- If the task starts from raw HCP-A data already present on disk and only asks for BIDS-style staging:
- Skip the mandatory download stage
- Default to the narrow path
local raw HCP-A discovery -> BIDS-style staging -> minimal metadata -> validation/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
- HCP-A complements HCP-YA to cover the full adult lifespan (22-100 years).
- HCP-A processing is resource intensive; plan storage and compute accordingly.
- Age range: 36-100 years; includes both cognitively normal and impaired participants.
- For HCP-native preprocessing, optionally delegate to
hcppipeline-tool.
hcpa-skill is orchestration-only; detailed preprocessing logic remains in modality skills.
When to Call This Skill
- User asks for end-to-end HCP Aging workflow.
- User asks to download HCP-A and run sMRI/fMRI/DTI processing.
- User needs BIDS staging for HCP-A data.
- User asks to extract HCP-A phenotype data (cognitive, health, demographic).
Complementary / Related Skills
smri-skill → structural MRI preprocessingfmri-skill → functional MRI preprocessing and analysisdwi-skill → diffusion MRI preprocessing and analysishcppipeline-tool → HCP-native minimal preprocessing pipelinesbids-organizer → BIDS validation and organizationbrain-visualization → visualization of derivativesdependency-planner → dependency resolutionconda-env-manager → environment managementclaw-shell → command execution
Reference
Created At: 2026-05-06 13:02 HKT
Last Updated At: 2026-05-06 13:02 HKT
Author: chengwang96
