hcpd-skill

name: hcpd-skill description: "Use this skill whenever the user wants an end-to-end workflow for the HCP Development (HCP-D) dataset, including dataset download, BIDS organization, and multimodal processing of sMRI, fMRI, and dMRI. Triggers include: 'HCP Development', 'HCP-D', 'process HCP Development data', 'HCP Development sMRI fMRI', or any request to run the HCP-D 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-D Skill (Dataset-Orchestration Layer)

Overview

hcpd-skill is the NeuroClaw orchestration skill for the HCP Development (HCP-D) 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-D 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 (hcpd_output/).

Research use only.

Quick Reference

Task	What needs to be done	Delegate to	Expected output
Data download	Download HCP-D from ConnectomeDB	`claw-shell`	Raw HCP-D files
BIDS staging	Reorganize HCP-D native layout to BIDS	`scripts/reorganize_hcpd.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, behavioral, developmental data	`scripts/extract_hcpd_phenotype.py`	Merged phenotype CSV
QC summary	Per-subject quality control	`scripts/hcpd_qc_summary.py`	QC summary + exclusion list

Download Stage (Mandatory First Step)

Source

HCP-D 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: ~600+ children and adolescents ages 5-21 years
Modalities: T1w, T2w, dMRI, rs-fMRI, task-fMRI
Focus: Brain development, maturation of neural circuits, cognitive and emotional development
Unique feature: Covers the developmental period from childhood to early adulthood

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-D 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_hcpd.py`

Converts HCP-D native directory structure to BIDS-compliant layout.

python skills/hcpd-skill/scripts/reorganize_hcpd.py \
  --input /path/to/HCPD/raw \
  --output /path/to/HCPD/bids \
  --participants /path/to/subject_list.txt

Features:

Subject ID normalization: HCP format to BIDS sub- labels
Age-band session handling 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-D 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_hcpd.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_hcpd_phenotype.py.
If QC summary is requested, run scripts/hcpd_qc_summary.py.
Save outputs into hcpd_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

hcpd_output/
├── raw/                    # Downloaded original HCP-D 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-D data staging.

If the task starts from raw HCP-D 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-D 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-D covers ages 5-21 years; pediatric processing may require age-specific templates and atlases.
Head motion is typically higher in pediatric populations; QC thresholds may need adjustment.
HCP-D complements HCP-YA (22-35) and HCP-A (36-100) to cover the full lifespan.
For HCP-native preprocessing, optionally delegate to hcppipeline-tool.
hcpd-skill is orchestration-only; detailed preprocessing logic remains in modality skills.

When to Call This Skill

User asks for end-to-end HCP Development workflow.
User asks to download HCP-D and run sMRI/fMRI/DTI processing.
User needs BIDS staging for HCP-D data.
User asks to extract HCP-D phenotype data (cognitive, behavioral, developmental).

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

Reference

HCP Development: https://www.humanconnectome.org/study/hcp-lifespan-development
ConnectomeDB: https://db.humanconnectome.org/
Somerville et al. (2018): The Lifespan Human Connectome Project in Development

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