What is sushihacks2025 CLAUDE.md?

Project instructions for a full-stack application with a web frontend, Convex backend, fish-occurrence data, prediction services, and Arduino sensor APIs. They list commands for development, building, linting, and tests.

Use them to start the app, run its frontend or backend services, install Python dependencies, build the project, lint it, or test its prediction and sensor APIs.

They tell a coding agent how this particular application is organized and how to check changes across its frontend and backend parts.

How Cheggin/sushihacks2025 configures Claude Code

Reuse

Borrowing it

Nothing to install: this file belongs to Cheggin/sushihacks2025. 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/Cheggin/sushihacks2025/main/CLAUDE.md

Clone the repo

git clone --depth 1 https://github.com/Cheggin/sushihacks2025

Made for: Claude Code.

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.

README.md

[![agentmods](https://agentmods.dev/badge/instructions/cheggin/sushihacks2025/claude-md.svg)](https://agentmods.dev/instructions/cheggin/sushihacks2025/claude-md)

Your own site

<a href="https://agentmods.dev/instructions/cheggin/sushihacks2025/claude-md"><img src="https://agentmods.dev/badge/instructions/cheggin/sushihacks2025/claude-md.svg" alt="Measured on agentmods" height="20"></a>

Per session 1,036 This file is loaded in full into every session.

When invoked 1,036 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.

Model	Per session	Once invoked
Fable 5.1	$0.01036	$0.01036
Opus 5	$0.00518	$0.00518
Sonnet 5	$0.00207	$0.00207
Haiku 4.5	$0.00104	$0.00104

Measured 8d ago against content hash ffcb453de667, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

sushihacks2025 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 · 97 lines

How it starts

The opening of the file, as written. The whole thing — 97 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.

Commands

Frontend Development

cd frontend
npm install                # Install dependencies
npm run dev                # Start both frontend (Vite) and Convex backend
npm run dev:frontend       # Start only frontend dev server (port 5173)
npm run dev:backend        # Start only Convex backend
npm run build             # Build for production
npm run lint              # Run ESLint checks
npm run preview           # Preview production build

Backend Development

cd backend

# Install Python dependencies
pip install -r requirements.txt

# Start individual APIs
python3 fish_api.py                                    # Fish Occurrence API (port 8000)
python3 cts_prediction/cts_prediction_api.py          # CTS Prediction API (port 8002)
python3 sensor_api/sensor_api.py                      # Real Arduino Sensor API (port 8003)
python3 sensor_api/demo_sensor_api.py                 # Demo Sensor API (port 8004)

# Run tests
python3 cts_prediction/test_api_works.py              # Test CTS prediction API

Architecture

This is a full-stack application combining marine biology data visualization with occupational health monitoring for fishermen and maritime workers.

Technology Stack

Frontend: React 19 + TypeScript, Vite, React Router, Tailwind CSS, Recharts, Leaflet maps
Backend: FastAPI (Python), scikit-learn for ML, Pandas for data processing
Real-time Backend: Convex for database and real-time sync
Hardware Integration: Arduino sensors via Bleak (Bluetooth Low Energy)

Project Structure

/
├── frontend/                 # React + Vite application
│   ├── src/
│   │   ├── pages/           # Main application pages (Landing, HomePage, Health, FishMapPage)
│   │   ├── components/      # Reusable components (Navbar, FishMap, Layout)
│   │   └── utils/           # Utilities (fishZoneAnalyzer.ts)
│   └── convex/              # Convex backend functions
│       ├── myFunctions.ts   # Database queries and mutations
│       └── schema.ts        # Database schema definitions
│
└── backend/                 # Python FastAPI services
    ├── fish_api.py          # Fish occurrence data API
    ├── cts_prediction/      # ML-based CTS severity prediction
    │   ├── cts_prediction_api.py
    │   └── carpal_tunnel_rf_model.joblib
    ├── sensor_api/          # Arduino sensor integration
    │   ├── sensor_api.py    # Real hardware connection
    │   └── demo_sensor_api.py  # Hardware simulation
    └── data/                # Data files
        └── occurrence_parsed.csv  # 50K+ fish occurrence records

Read the full file on GitHub · 97 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.

8d ago First seen · 97 lines · 1,036 tokens per session scan A ffcb453de667

Subscribe to this mod's changes

sushihacks2025 CLAUDE.md is an instructions file published in the GitHub repository Cheggin/sushihacks2025 (5 stars, last pushed 11mo ago), licensed MIT. It adds 1,036 tokens to every session, about $0.0052 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.

next.js AGENTS.md

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

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 · 5,153 tokens

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

sushihacks2025: Instructions file for Claude Code