Borrowing it
Nothing to install: this file belongs to Orinks/AccessiWeather. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Orinks/AccessiWeather/main/AGENTS.mdgit clone --depth 1 https://github.com/Orinks/AccessiWeatherWrote 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.
[](https://agentmods.dev/instructions/orinks/accessiweather/agents-md)<a href="https://agentmods.dev/instructions/orinks/accessiweather/agents-md"><img src="https://agentmods.dev/badge/instructions/orinks/accessiweather/agents-md/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/instructions/orinks/accessiweather/agents-md"><img src="https://agentmods.dev/badge/instructions/orinks/accessiweather/agents-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.01944 | $0.01944 |
| Opus 5.5 | $0.00778 | $0.00778 |
| Sonnet 5.5 | $0.00389 | $0.00389 |
| Haiku 4.5 | $0.00194 | $0.00194 |
Grade A, and why
AccessiWeather AGENTS.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 29d 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.
How it starts
The opening of the file, as written. The whole thing — 222 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Agent Guidelines - AccessiWeather
Role: You are an expert Python Desktop Application Engineer and Technical Lead specializing in accessible, cross-platform wxPython applications. You are responsible for understanding requirements, implementing carefully, testing, and preserving accessibility.
Quick Reference Commands
# Development
uv run accessiweather # Run the app from source
pytest -v # Run all tests serially
pytest -n auto # Run all tests in parallel
pytest tests/test_file.py::test_func # Run one test
pytest -k "test_name" -v # Run tests matching a pattern
pytest --lf --ff -m "unit" # Run last-failed/first-failed unit tests
# Linting & Formatting
ruff check --fix . && ruff format . # Lint and format code
pyright # Type checking; may be scoped for noisy legacy areas
# Build & Package
python installer/build_nuitka.py # Build packaged app artifacts
python installer/build.py --dev # Run installer build helper in development mode
# Git on Windows
git --no-pager log --oneline -5
git --no-pager diff
git --no-pager show HEAD
Project Overview
AccessiWeather is a cross-platform accessible desktop weather application built with Python and wxPython. It prioritizes screen reader accessibility, multi-source weather data, weather alerts, and straightforward JSON-based configuration.
Tech Stack
| Category | Technology |
|---|---|
| Language | Python 3.11+ |
| GUI Framework | wxPython |
| HTTP Client | httpx |
| Build Tool | Nuitka packaging scripts |
| Testing | pytest, pytest-asyncio, pytest-xdist, hypothesis |
| Linting | Ruff |
| Type Checking | Pyright |
| Package Format | pyproject.toml |
Architecture & Codebase Structure
Main Package: src/accessiweather/
| Module | Purpose |
|---|---|
app.py |
Main wxPython application orchestration |
main.py |
Console/GUI entry point |
weather_client.py |
Multi-source weather data orchestration |
alert_manager.py |
Weather alert management with rate limiting |
alert_notification_system.py |
Desktop notifications for weather alerts |
background_tasks.py |
Async periodic weather updates |
cache.py |
Weather API response caching |
ui/ |
wxPython windows, dialogs, and UI helpers |
config/ |
Settings, saved locations, secure storage, and source priority |
api/ |
NWS, Open-Meteo, Visual Crossing, and related API clients |
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.
- 29d ago Changed · +1 lines · +59 tokens per session e4022c3a38a2
- 1mo ago First seen · 221 lines · 1,885 tokens per session scan A dd913584ff21
AccessiWeather AGENTS.md is an instructions file published in the GitHub repository Orinks/AccessiWeather (24 stars, last pushed 2d ago), licensed MIT. It adds 1,944 tokens to every session, about $0.0078 per session on Opus 5.5. 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 2026-09-03.
Other instructions, from other repositories
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next.js AGENTS.md
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