tcp-skill

name	tcp-skill
description	Use this skill whenever the user wants an end-to-end workflow for the Transdiagnostic Connectome Project (TCP) dataset, including BIDS validation, multimodal processing of sMRI, rs-fMRI, and dMRI, phenotype extraction, and QC integration. Triggers include: 'TCP', 'Transdiagnostic Connectome', 'process TCP data', 'TCP fMRI', or any request to run the TCP 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	["brain-visualization"]

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.

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Quick Reference

Task	What needs to be done	Delegate to	Expected output
BIDS validation	Validate TCP BIDS structure	scripts/validate_tcp.py	Validation report
sMRI processing	Brain extraction, tissue segmentation	smri-skill	smri_output/ derivatives
rs-fMRI processing	Preprocessing, denoising, connectivity	fmri-skill	fmri_output/ connectivity
dMRI processing	Diffusion preprocessing, tractography	dwi-skill	dwi_output/ metrics
Phenotype extraction	Psychiatric diagnosis, dimensional measures	scripts/extract_tcp_phenotype.py	Merged phenotype CSV
QC summary	Per-subject quality control	scripts/tcp_qc_summary.py	QC summary + exclusion list

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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

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Supported Modalities

Modality	Description	Details
T1w	High-resolution structural MRI	1mm isotropic, cortical thickness
rs-fMRI	Resting-state functional MRI	Eyes open, functional connectivity
dMRI	Diffusion-weighted imaging	DTI, white matter tractography

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TCP Clinical Dimensions

Domain	Measures	RDoC Construct
Negative valence	Anhedonia, anxiety	Negative valence systems
Positive valence	Reward processing	Positive valence systems
Cognitive	Working memory, executive function	Cognitive systems
Social	Social cognition	Social processes
Arousal	Arousal/regulatory systems	Arousal/regulatory systems

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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

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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/.

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Modality Processing Delegation

Modality	Delegated skill	Typical tasks	Main outputs
sMRI (T1w)	smri-skill	brain extraction, tissue segmentation, cortical thickness	smri_output/ derivatives
rs-fMRI	fmri-skill	preprocessing, denoising, connectivity	fmri_output/ connectivity
dMRI	dwi-skill	diffusion preprocessing, tensor metrics	dwi_output/ metrics

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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

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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.

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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

• 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.

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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.

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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

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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