Pnc Skill

PNC Skill (Dataset-Orchestration Layer)

Overview

pnc-skill is the NeuroClaw orchestration skill for the Philadelphia Neurodevelopmental Cohort (PNC) dataset, a large-scale collaborative study between the University of Pennsylvania and the Children's Hospital of Philadelphia.

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 BIDS validation, phenotype extraction, and QC.

Core workflow (never bypassed):

1. Identify input PNC 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 (pnc_output/).

Research use only.

Quick Reference

Dataset Characteristics

• Cohort: ~9,000+ youth aged 8-21 years
• Scanner: 3T Siemens TIM Trio
• Modalities: T1w sMRI, rs-fMRI, task-fMRI, dMRI/DTI
• Task paradigms: Go/No-Go, Fraternal Twins, Penn Line Orientation, Penn Word Memory
• Clinical: Psychiatric assessment, cognitive battery (Penn CNB)
• Access: NIMH Data Archive (NDA), OpenNeuro ds000030 (BIDS subset)
• Format: BIDS-compliant (community conversion)
• Reference: Satterthwaite et al. (2014), NeuroImage

Supported Modalities

PNC Task Paradigms

BIDS Preparation

Script: scripts/validate_pnc.py

Validates PNC BIDS structure and generates a compliance report.

python skills/pnc-skill/scripts/validate_pnc.py \
  --input /path/to/PNC/bids \
  --output /path/to/pnc_output/qc/bids_validation.csv

Features:

• BIDS directory structure validation
• Modality completeness check (T1w, rs-fMRI, task-fMRI, dMRI)
• Age range verification (8-21 years)
• Task paradigm presence check

Core Workflow (Never Bypassed)

1. Identify user target: full PNC processing, imaging subset, phenotype extraction, or BIDS validation 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 BIDS validation using scripts/validate_pnc.py.
5. Delegate to smri-skill for structural MRI processing.
6. Delegate to fmri-skill for rs-fMRI and task-fMRI processing.
7. Delegate to dwi-skill for dMRI processing.
8. If phenotype extraction is requested, run scripts/extract_pnc_phenotype.py.
9. If QC summary is requested, run scripts/pnc_qc_summary.py.
10. Save outputs into pnc_output/.

Modality Processing Delegation

Standard Output Layout

pnc_output/
├── bids/                   # BIDS-staged data (or validation report)
├── smri/                   # Structural MRI derivatives
├── fmri/                   # Functional MRI derivatives (rs + task)
├── dwi/                    # Diffusion MRI derivatives
├── phenotype/              # Merged phenotype tables (cognitive, psychiatric)
├── qc/                     # QC summaries and exclusion lists
└── logs/                   # Processing logs

Benchmark Adapter Guidance

For benchmark-style prompts, do not force the full orchestration when the task only asks for local PNC data validation.

• If the task starts from PNC data already present on disk and only asks for BIDS validation:
  • Skip the download stage
  • Default to the narrow path local PNC discovery -> BIDS validation -> report
• In benchmark mode, do not require explicit confirmation before presenting the validation 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

• PNC is a developmental cohort; analyses should account for age effects (8-21 years).
• Pediatric data may require adjusted preprocessing parameters (e.g., higher motion thresholds).
• Penn CNB (Computerized Neurocognitive Battery) provides rich cognitive phenotyping.
• Psychiatric assessment includes DSM-based diagnoses.
• Large sample size enables well-powered developmental analyses.
• pnc-skill is orchestration-only; detailed preprocessing logic remains in modality skills.

When to Call This Skill

• User asks for end-to-end PNC workflow.
• User asks to process PNC neuroimaging data.
• User needs BIDS validation for PNC data.
• User asks to extract PNC phenotype data (cognitive, psychiatric, demographic).
• User asks for developmental neuroimaging analysis.

Complementary / Related Skills

• smri-skill → structural MRI preprocessing
• fmri-skill → functional MRI preprocessing and analysis
• dwi-skill → diffusion MRI preprocessing
• 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

• PNC: https://www.med.upenn.edu/bbl/
• Satterthwaite et al. (2014): Neuroimaging of the Philadelphia Neurodevelopmental Cohort. NeuroImage.
• OpenNeuro ds000030
• NIMH Data Archive: https://nda.nih.gov/

Created At: 2026-05-06 13:55 HKT Last Updated At: 2026-05-06 13:55 HKT Author: chengwang96