ripple_detection: Instructions file for Claude Code

CLAUDE.md

ripple_detection CLAUDE.md is an instructions file for Claude Code from Eden-Kramer-Lab/ripple_detection. It costs 3,134 tokens per session, scanned A, original, MIT.

Project instructions for a Python package that detects sharp-wave ripple events in brain recordings. These are brief high-frequency patterns in local field potential signals, commonly studied in neuroscience.

In plain words
What is it for?
Use them when installing, testing, developing, or running examples for the ripple_detection package.
Why use it?
They collect the project’s setup, environment, testing, and development rules in one place, including the supported detection methods.

Instructions file for Claude Code

Written for Claude Code: the file is CLAUDE.md. Also seen: mentions CLAUDE.md; mentions Claude Code.

This is Eden-Kramer-Lab/ripple_detection's own configuration. It tells Claude Code how to work on ripple_detection itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything ripple_detection configures →

Reuse

Borrowing it

Nothing to install: this file belongs to Eden-Kramer-Lab/ripple_detection. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/Eden-Kramer-Lab/ripple_detection/master/CLAUDE.md
Clone the repo
git clone --depth 1 https://github.com/Eden-Kramer-Lab/ripple_detection

Made for: Claude Code.

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README.md
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Per session 3,134 This file is loaded in full into every session.
When invoked 3,134 The same file — it is already loaded in full.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
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 5.1 $0.03134 $0.03134
Opus 5 $0.01567 $0.01567
Sonnet 5 $0.00627 $0.00627
Haiku 4.5 $0.00313 $0.00313

Measured 8d ago against content hash 31e0b106d4f0, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-07, from the pricing page.

Security

Grade A, and why

ripple_detection 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 8d ago.

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 · 346 lines

How it starts

The opening of the file, as written. The whole thing — 346 lines — stays where its author put it; the contents beside it link to each section on GitHub.

CLAUDE.md

This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.

Project Overview

ripple_detection is a Python package for detecting sharp-wave ripple events (150-250 Hz) from local field potentials (LFPs) in neuroscience research. It implements detection algorithms from Karlsson et al. 2009 and Kay et al. 2016, along with other variants.

Development Commands

Setup

# Install from source (development mode with dev dependencies)
pip install -e .[dev,examples]

# Or create conda environment with all dependencies
conda env create -f environment.yml
conda activate ripple_detection
pip install -e .[dev,examples]

# Minimal install (runtime dependencies only)
pip install -e .

Testing

# Run all tests with coverage (93% coverage achieved!)
pytest --cov=ripple_detection tests/

# Run specific test module
pytest tests/test_core.py          # Core signal processing tests
pytest tests/test_detectors.py     # Detector integration tests
pytest tests/test_simulate.py      # Simulation module tests

# Run specific test class or function
pytest tests/test_core.py::TestGetEnvelope
pytest tests/test_detectors.py::TestKayRippleDetector::test_single_channel_with_ripples

# Generate HTML coverage report
pytest --cov=ripple_detection --cov-report=html tests/
open htmlcov/index.html

# Test notebooks (as done in CI)
jupyter nbconvert --to notebook --ExecutePreprocessor.kernel_name=python3 --execute examples/detection_examples.ipynb
jupyter nbconvert --to notebook --ExecutePreprocessor.kernel_name=python3 --execute examples/test_individual_algorithm_components.ipynb
jupyter nbconvert --to notebook --ExecutePreprocessor.kernel_name=python3 --execute examples/ripple_detection_tutorial.ipynb

Code Quality

# Format code with black
black ripple_detection/ tests/

# Check formatting without modifying files
black --check ripple_detection/ tests/

# Lint code with ruff (fast, modern linter - replaces flake8)
ruff check ripple_detection/ tests/

# Auto-fix ruff issues where possible
ruff check --fix ripple_detection/ tests/

# Type check with mypy
mypy ripple_detection/

Read the full file on GitHub · 346 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. 8d ago First seen · 346 lines · 3,134 tokens per session scan A 31e0b106d4f0

Subscribe to this mod's changes

ripple_detection CLAUDE.md is an instructions file published in the GitHub repository Eden-Kramer-Lab/ripple_detection (43 stars, last pushed 13d ago), licensed MIT. It adds 3,134 tokens to every session, about $0.0157 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 2026-08-30.

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