airbnb-search: Instructions file for Codex

AGENTS.md

airbnb-search AGENTS.md is an instructions file for Codex, OpenCode from Olafs-World/airbnb-search. It costs 660 tokens per session, scanned A, original, MIT.

Project instructions for airbnb-search, a command-line tool that searches Airbnb listings through Airbnb's GraphQL interface, a structured way for programs to request data.

In plain words
What is it for?
Use them when developing features, running unit or integration tests, checking code style, testing searches locally, or preparing a release.
Why use it?
They give coding agents the project's setup, test, lint, release, and local usage rules so changes follow the repository's workflow.

Instructions file for CodexOpenCode

Written for Codex and OpenCode: the file is AGENTS.md. Also seen: mentions AGENTS.md.

This is Olafs-World/airbnb-search's own configuration. It tells Codex and OpenCode how to work on airbnb-search 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 airbnb-search configures →

Reuse

Borrowing it

Nothing to install: this file belongs to Olafs-World/airbnb-search. 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/Olafs-World/airbnb-search/main/AGENTS.md
Clone the repo
git clone --depth 1 https://github.com/Olafs-World/airbnb-search

Made for: Codex, OpenCode.

Wrote this? Show the measurements

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Per session 660 This file is loaded in full into every session.
When invoked 660 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 $0.00660 $0.00660
Opus 5 $0.00330 $0.00330
Sonnet 5 $0.00132 $0.00132
Haiku 4.5 $0.00066 $0.00066

Measured 9d ago against content hash 215794fb262e, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

airbnb-search 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 9d 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.

AGENTS.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.

AGENTS.md

Instructions for AI agents working on this repository.

Overview

airbnb-search is a CLI tool for searching Airbnb listings. Uses Airbnb's internal GraphQL API (no browser automation).

Development

Setup

git clone https://github.com/Olafs-World/airbnb-search.git
cd airbnb-search
uv sync

Running Tests

uv run pytest                        # All tests
uv run pytest -m "not integration"   # Unit tests only (CI uses this)
uv run ruff check .                  # Linting

Testing Locally

uv run airbnb-search "Denver, CO" --checkin 2025-03-01 --checkout 2025-03-03

Project Structure

airbnb_search/
├── __init__.py      # Package exports
├── cli.py           # CLI entry point (argparse)
├── search.py        # Core search logic (GraphQL API calls)
tests/
├── test_cli.py      # CLI tests
├── test_search.py   # Search function tests (unit + integration)

Making a Release

⚠️ NEVER manually publish to PyPI! Always use git tags - CI handles PyPI automatically.

Release Process

  1. Bump version in pyproject.toml
  2. Update CHANGELOG.md with changes under new version header
  3. Commit: git add -A && git commit -m "Bump version to X.Y.Z"
  4. Tag: git tag vX.Y.Z
  5. Push both: git push && git push --tags

CI will automatically:

  • Run tests on Python 3.8-3.12
  • Publish to PyPI (only on tag push)
  1. Create GitHub Release (optional but recommended):
    • Go to Releases → Draft new release
    • Select the tag you just pushed
    • Copy release notes from CHANGELOG.md

Why not manual PyPI publish?

  • Keeps GitHub releases and PyPI versions in sync
  • Ensures tests pass before publishing
  • Creates audit trail via CI logs
  • Prevents accidental publishes of broken code

Code Style

  • Use ruff for linting
  • Follow existing patterns in the codebase
  • Keep CLI output user-friendly with emoji
  • Support both --output text and --output json

API Notes

  • Uses Airbnb's internal StaysSearch GraphQL endpoint
  • No authentication required
  • Rate limiting: be respectful, don't hammer the API
  • API may change without notice (Airbnb doesn't publish it)

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.

  1. 9d ago First seen · 97 lines · 660 tokens per session scan A 215794fb262e

Subscribe to this mod's changes

airbnb-search AGENTS.md is an instructions file published in the GitHub repository Olafs-World/airbnb-search (6 stars, last pushed 6mo ago), licensed MIT. It adds 660 tokens to every session, about $0.0033 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.

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