Tcp Skill

TCP Skill (Dataset-Orchestration Layer)

Overview

tcp-skill is the NeuroClaw orchestration skill for the Transdiagnostic Connectome Project (TCP) dataset, collected at Washington University in St. Louis.

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 TCP 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 (tcp_output/).

Research use only.

Quick Reference

Dataset Characteristics

• Cohort: ~600+ participants
  • Transdiagnostic approach: participants span multiple diagnostic categories
  • Healthy controls: Age-matched
  • Psychiatric groups: Depression, anxiety, psychosis spectrum, etc.
• Scanner: 3T Siemens (WashU)
• Modalities: T1w sMRI, rs-fMRI, dMRI/DTI
• Clinical: RDoC-informed dimensional measures, diagnostic assessments
• Access: NIMH Data Archive (NDA), OpenNeuro
• Format: BIDS-compliant
• Reference: Barch, Gordon et al., WashU

Supported Modalities

TCP Clinical Dimensions

BIDS Preparation

Script: scripts/validate_tcp.py

Validates TCP BIDS structure and generates a compliance report.

python skills/tcp-skill/scripts/validate_tcp.py \
  --input /path/to/TCP/bids \
  --output /path/to/tcp_output/qc/bids_validation.csv

Features:

• BIDS directory structure validation
• Modality completeness check (T1w, rs-fMRI, dMRI)
• Diagnostic group labeling
• Missing data identification

Core Workflow (Never Bypassed)

1. Identify user target: full TCP 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_tcp.py.
5. Delegate to smri-skill for structural MRI processing.
6. Delegate to fmri-skill for rs-fMRI processing.
7. Delegate to dwi-skill for dMRI processing.
8. If phenotype extraction is requested, run scripts/extract_tcp_phenotype.py.
9. If QC summary is requested, run scripts/tcp_qc_summary.py.
10. Save outputs into tcp_output/.

Modality Processing Delegation

Standard Output Layout

tcp_output/
├── bids/                   # BIDS-staged data (or validation report)
├── smri/                   # Structural MRI derivatives
├── fmri/                   # Functional MRI derivatives (rs-fMRI connectivity)
├── dwi/                    # Diffusion MRI derivatives
├── phenotype/              # Merged phenotype tables (diagnosis, dimensional)
├── 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 TCP data validation.

• If the task starts from TCP data already present on disk and only asks for BIDS validation:
  • Skip the download stage
  • Default to the narrow path local TCP 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

• TCP uses a transdiagnostic approach; analyses should consider dimensional rather than categorical models.
• RDoC-informed phenotyping enables cross-diagnostic connectivity analyses.
• Connectome-based predictive modeling (CPM) is a commonly used analysis approach.
• Multi-diagnostic design requires careful handling of group comparisons.
• tcp-skill is orchestration-only; detailed preprocessing logic remains in modality skills.

When to Call This Skill

• User asks for end-to-end TCP workflow.
• User asks to process TCP neuroimaging data.
• User needs BIDS validation for TCP data.
• User asks to extract TCP phenotype data (diagnostic, dimensional).
• User asks for transdiagnostic connectivity 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

• TCP: Washington University in St. Louis
• Barch, Gordon et al.: Transdiagnostic Connectome Project
• NIMH Data Archive: https://nda.nih.gov/

Created At: 2026-05-06 14:21 HKT Last Updated At: 2026-05-06 14:21 HKT Author: chengwang96