hcpa-skill

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):

1. Identify input HCP-A data and target modalities.
2. Generate a numbered execution plan clearly stating 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 (hcpa_output/).

Research use only.

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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

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Download Stage (Mandatory First Step)

Source

HCP-A data is distributed through ConnectomeDB:

• Website: https://db.humanconnectome.org/
• Requires ConnectomeDB account and data use agreement
• Part of the HCP Lifespan initiative

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

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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

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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

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Core Workflow (Never Bypassed)

1. Identify user target: full HCP-A processing, imaging subset, phenotype extraction, or BIDS staging only.
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, run BIDS preparation using scripts/reorganize_hcpa.py.
6. Delegate to smri-skill for structural MRI processing.
7. Delegate to fmri-skill for functional MRI processing.
8. Delegate to dwi-skill for diffusion MRI processing.
9. If phenotype extraction is requested, run scripts/extract_hcpa_phenotype.py.
10. If QC summary is requested, run scripts/hcpa_qc_summary.py.
11. Save outputs into hcpa_output/.

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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

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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

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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.

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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.

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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.

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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).

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Complementary / Related Skills

• smri-skill → structural MRI preprocessing
• fmri-skill → functional MRI preprocessing and analysis
• dwi-skill → diffusion MRI preprocessing and analysis
• hcppipeline-tool → HCP-native minimal preprocessing pipelines
• bids-organizer → BIDS validation and organization
• brain-visualization → visualization of derivatives
• dependency-planner → dependency resolution
• conda-env-manager → environment management
• claw-shell → command execution

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Reference

• HCP Aging: https://www.humanconnectome.org/study/hcp-lifespan-aging
• ConnectomeDB: https://db.humanconnectome.org/
• Bookheimer et al. (2019): The Lifespan Human Connectome Project in Aging

Created At: 2026-05-06 13:02 HKT Last Updated At: 2026-05-06 13:02 HKT Author: chengwang96