Hcpa Skill

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.

Quick Reference

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

HCP-A Task Paradigms

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)

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/.

Modality Processing Delegation

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 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 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