proteusllp-actuarial-library: Instructions file for Codex

AGENTS.md

proteusllp-actuarial-library AGENTS.md is an instructions file for Codex, OpenCode from ProteusLLP/proteusllp-actuarial-library. It costs 1,030 tokens per session, scanned A, original, MIT.

Repository instructions for ProteusLLP's Actuarial Library, a Python package for modelling financial and insurance risk with random simulations.

In plain words
What is it for?
Working on probability distributions, stochastic variables, copulas, insurance models, reinsurance, risk measures, GPU support, tests, and documentation in that repository.
Why use it?
They tell coding agents which files and commands are authoritative and how the library is organised, reducing inconsistent changes.

Instructions file for CodexOpenCode

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

This is ProteusLLP/proteusllp-actuarial-library's own configuration. It tells Codex and OpenCode how to work on proteusllp-actuarial-library 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 proteusllp-actuarial-library configures →

Reuse

Borrowing it

Nothing to install: this file belongs to ProteusLLP/proteusllp-actuarial-library. 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/ProteusLLP/proteusllp-actuarial-library/main/AGENTS.md
Clone the repo
git clone --depth 1 https://github.com/ProteusLLP/proteusllp-actuarial-library

Made for: Codex, OpenCode.

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When invoked 1,030 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 $0.01030 $0.01030
Opus 5.5 $0.00412 $0.00412
Sonnet 5.5 $0.00206 $0.00206
Haiku 4.5 $0.00103 $0.00103

Measured 2mo ago against content hash cd409282dabd, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

proteusllp-actuarial-library 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 2mo 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.

PAL Agent Guide

This is the canonical repository-level guidance for coding agents working on the Proteus Actuarial Library (PAL). Tool-specific instruction files should point here rather than duplicate these rules.

What PAL Is

PAL is a Python library for simulation-based actuarial and financial modelling. It provides stochastic variables, probability distributions, copulas, frequency-severity models, reinsurance contracts, risk measures and optional CuPy GPU acceleration.

The installed package is pal; the PyPI distribution is proteusllp-actuarial-library. PAL intentionally exposes the four core modelling abstractions ProteusVariable, StochasticScalar, FreqSevSims, and FrequencySeverityModel at the package root; domain-specific modelling classes remain organised under their public submodules.

Sources of Truth

When instructions disagree, use these sources in this order:

  1. pyproject.toml for supported Python versions, dependencies, Ruff configuration and package metadata.
  2. Makefile and CI workflows for the commands that must pass.
  3. This file and any more-specific nested AGENTS.md for architectural and behavioural rules.
  4. STYLE_GUIDE.md, docs/structure.md and docs/development.md for additional explanation.

Do not copy configuration values into new guidance when they can be referenced from the authoritative configuration instead.

Repository Map

  • src/pal/: library implementation. Read src/pal/AGENTS.md before changing library code.
  • tests/: automated tests. Read tests/AGENTS.md before adding or changing tests.
  • docs/: Sphinx documentation and tutorials. Read docs/AGENTS.md before changing documentation.
  • examples/: executable end-to-end examples.
  • .github/workflows/: CPU, GPU and other CI workflows.
  • pyproject.toml: package, dependency and static-analysis configuration.
  • Makefile: canonical local development commands.
  • docs/structure.md: type-system architecture and dependency direction.

Development Environment

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. 2mo ago Changed · +2 lines · +126 tokens per session cd409282dabd
  2. 2mo ago First seen · 95 lines · 904 tokens per session scan A bee8912706dc

Subscribe to this mod's changes

proteusllp-actuarial-library AGENTS.md is an instructions file published in the GitHub repository ProteusLLP/proteusllp-actuarial-library (11 stars, last pushed 6d ago), licensed MIT. It adds 1,030 tokens to every session, about $0.0041 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 2026-08-30.

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