treelang AGENTS.md

Repository instructions for treelang, a Python library with tree-based language structures, AI integrations, examples, and evaluation tools.

In plain words
What is it for?
Use them when developing treelang, running its tests and checks, formatting code, building the package, or running its offline evaluation benchmark.
Why use it?
They tell an agent where project code and tests live, which Python version and commands to use, and how to check changes.

Instructions file for CodexOpenCode

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/cs0lar/treelang/agents-md
Clone the repo
git clone --depth 1 https://github.com/cs0lar/treelang

Made for: Codex, OpenCode.

Per session 3,898 This file is loaded in full into every session.
When invoked 3,898 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 $0.03898 $0.03898
Opus 5 $0.01949 $0.01949
Sonnet 5 $0.00780 $0.00780
Haiku 4.5 $0.00390 $0.00390

Measured 2d ago against content hash bedfdc49a629, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

treelang 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 2d 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 · 375 lines

How it starts

The opening of the file, as written. The whole thing — 375 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Repository Guidelines

Project Structure & Module Organization

treelang/ contains the library. AST models and evaluation behavior live under treelang/trees/, while providers, prompts, selection, and memory integrations live under treelang/ai/. Unit tests mirror the package beneath tests/; tree tests are split across tests/trees/test_ast.py, test_nodes.py, and test_tool.py. Use cookbook/ for runnable examples and notebooks. evaluation/ contains the regression harness, curated questions, and evaluation tools. Package metadata and dependencies are declared in pyproject.toml and locked in uv.lock.

Build, Test, and Development Commands

  • uv sync --frozen --all-groups: install the exact locked development environment.
  • make check: run linting, type checks, tests with coverage, and package builds.
  • uv run pytest: run the complete test suite.
  • make format: apply Ruff lint fixes and formatting.
  • uv run python evaluation/eval.py: run the deterministic offline benchmark and compare it with the committed baseline; this requires no credentials or network access.

Python 3.12 or newer is required. Run cookbook scripts with uv, for example uv run python cookbook/calculator.py.

Coding Style & Naming Conventions

Use four-space indentation and Ruff's formatter. Follow standard Python naming: snake_case for functions, variables, and modules; PascalCase for classes; and UPPER_CASE for constants. Add type annotations to public interfaces and async functions where practical. Keep schema changes in treelang/trees/schemas/ and provider-specific behavior in treelang/ai/; avoid mixing those concerns.

Testing Guidelines

Tests run with pytest; existing cases use unittest.IsolatedAsyncioTestCase and AsyncMock for asynchronous behavior. Name files test_*.py, classes Test*, and methods test_*. Add focused tests for success paths, validation failures, and async provider interactions. The current branch-coverage floor is 60%; raise it only with corresponding tests. Every behavior change should include a regression test. Run the full suite before opening a pull request.

Read the full file on GitHub · 375 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. 2d ago First seen · 375 lines · 3,898 tokens per session scan A bedfdc49a629

Subscribe to this mod's changes

treelang AGENTS.md is an instructions file published in the GitHub repository cs0lar/treelang (5 stars, last pushed 5d ago), licensed MIT. It adds 3,898 tokens to every session, about $0.0195 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.