T1Prep CLAUDE.md

T1Prep CLAUDE.md is an instructions file for coding agents from ChristianGaser/T1Prep. It costs 1,598 tokens per session, scanned A, original, Apache-2.0.

Repository instructions for T1Prep, a Python pipeline that prepares and segments T1-weighted MRI scans, including correction, lesion detection, and surface reconstruction.

In plain words
What is it for?
Use it when developing, reviewing, testing, or routing work in the T1Prep codebase.
Why use it?
It defines the required order for security, performance, and style work, along with project commands and background-execution rules.

Instructions file

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add instructions/christiangaser/t1prep/claude-md
Clone the repo
git clone --depth 1 https://github.com/ChristianGaser/T1Prep

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for T1Prep CLAUDE.md

README.md
[![agentmods](https://agentmods.dev/badge/instructions/christiangaser/t1prep/claude-md.svg)](https://agentmods.dev/instructions/christiangaser/t1prep/claude-md)
Your own site
<a href="https://agentmods.dev/instructions/christiangaser/t1prep/claude-md"><img src="https://agentmods.dev/badge/instructions/christiangaser/t1prep/claude-md.svg" alt="Measured on agentmods" height="20"></a>
Per session 1,598 This file is loaded in full into every session.
When invoked 1,598 The same file — it is already loaded in full.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.01598 $0.01598
Opus 5 $0.00799 $0.00799
Sonnet 5 $0.00320 $0.00320
Haiku 4.5 $0.00160 $0.00160

Measured today against content hash 82372ffac46a, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

T1Prep CLAUDE.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured today.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

CLAUDE.md · 139 lines

How it starts

The opening of the file, as written. The whole thing — 139 lines — stays where its author put it; the contents beside it link to each section on GitHub.

CLAUDE.md – T1Prep Project

For full contributor documentation see Agents.md.

Sub-Agent Routing Rules

  • Always sequential: All tasks (security, performance, style, refactoring) must be processed sequentially.
  • No parallelization: Only one sub-agent or one check may be active at a time.
  • Workflow: First execute security, then performance, then style. Wait for each step to complete.
  • Dependencies: B tasks must wait for the output of A tasks.

Background Execution Rules

Run in background automatically:

  • Web research and documentation lookups
  • Codebase exploration and analysis
  • Security audits and performance profiling
  • Any task where results aren't immediately needed
  • Research or analysis tasks (not file modifications)
  • Results aren't blocking your current work

Overview

T1Prep is a Python-based pipeline for preprocessing and segmenting T1-weighted MRI data (bias-field correction, segmentation, lesion detection, cortical surface reconstruction, CAT12 integration). Code lives in src/, helper/dev scripts in scripts/, Flask web UI in src/t1prep/webui/.

Entry points are installed into the environment's bin/ (the canonical way to run T1Prep): T1Prep (bash orchestrator), PyCAT (symlink to T1Prep; same CLI, PyCAT startup banner), t1prep-ui, t1prep-run (Python single-subject), CAT_SurfView, CAT_VolView, t1prep-make-apps, t1prep-download-models, t1prep-bbreg. The scripts/ folder is a source-tree/dev fallback and should not be put on PATH.

Key Commands

# CLI (from <venv>/bin on PATH; or ./scripts/T1Prep in a source checkout)
T1Prep --help
T1Prep --out-dir /tmp/out file.nii.gz

# Python API
from t1prep import run_t1prep

# Web UI
t1prep-ui --port 5050

# Sanity check
python -m compileall src

# Tests
pytest

# Recalibrate the QA rating bounds from a processed BrainWeb Phantom set
python scripts/qa_calibrate.py /path/to/BWP/report

# Score the spherical registration against the Mindboggle-101 manual labels
python scripts/eval_mindboggle.py project --mindboggle DIR... --t1prep DIR --work DIR
python scripts/eval_mindboggle.py dice --work DIR --protocol both

# Linting / formatting
black src scripts
flake8 src scripts       # or: ruff check src scripts
shellcheck scripts/*.sh

Read the full file on GitHub · 139 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. today Changed · +4 lines · +65 tokens per session 82372ffac46a
  2. 4d ago First seen · 135 lines · 1,533 tokens per session scan A cc1ab39171fc

Subscribe to this mod's changes

T1Prep CLAUDE.md is an instructions file published in the GitHub repository ChristianGaser/T1Prep (10 stars, last pushed yesterday), licensed Apache-2.0. It adds 1,598 tokens to every session, about $0.0080 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

Related

Other instructions, from other repositories

vscode buildNext.instructions.md

Working notes and architecture documentation for the new esbuild-based build system in build/next. Use when making changes to the new build pipeline (transpile/bundle commands, NLS plugin, source-map handling, resource copying, or self-hosting watch tasks).

microsoft/vscode · 6,785 tokens

spec-kit AGENTS.md

AGENTS.md instructions for github/spec-kit, covering agents.md, about spec kit and specify, quickstart — add a new integration in 5 steps, integration architecture and integrationmanifest — file tracking.

github/spec-kit · 7,104 tokens

codex AGENTS.md

AGENTS.md instructions for openai/codex, covering rust/codex-rs, the codex-core crate, code review rules, crate api surface and model visible context.

openai/codex · 5,182 tokens

langchain AGENTS.md

AGENTS.md instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.

langchain-ai/langchain · 4,345 tokens

vscode oss-third-party-notices.instructions.md

Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, applying the notice (cutover) and fallback chain (never fail the build).

microsoft/vscode · 5,001 tokens

next.js AGENTS.md

Instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.

vercel/next.js · 7,296 tokens