ruby_llm-agents CLAUDE.md

A project instruction file for RubyLLM::Agents, a Rails engine for building and monitoring AI agents.

In plain words
What is it for?
Use it to run the right Ruby tests and checks, understand the agent system, and follow the repository's design and contribution practices.
Why use it?
It gives coding agents the project context, required versions, standard commands, testing rules, and linting requirements they need to work safely in the repository.

Instructions file

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/adham90/ruby_llm-agents/claude-md
Clone the repo
git clone --depth 1 https://github.com/adham90/ruby_llm-agents
Per session 4,346 This file is loaded in full into every session.
When invoked 4,346 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 $0.04346 $0.04346
Opus 5 $0.02173 $0.02173
Sonnet 5 $0.00869 $0.00869
Haiku 4.5 $0.00435 $0.00435

Measured 3d ago against content hash a1b3138b8188, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

ruby_llm-agents 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 3d 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 · 437 lines

How it starts

The opening of the file, as written. The whole thing — 437 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.

Project Overview

RubyLLM::Agents is a Rails engine gem for building, managing, and monitoring LLM-powered AI agents. It provides a DSL for agent configuration, a middleware pipeline for execution, automatic tracking with cost analytics, and a mountable dashboard UI.

Requirements: Ruby >= 3.1, Rails >= 7.0, RubyLLM >= 1.16.0

Common Commands

bundle exec rspec                          # Run full test suite (~3700+ specs)
bundle exec rspec spec/agents/routing_spec.rb  # Run a single spec file
bundle exec rspec spec/agents/ -e "parses"     # Run specs matching description
bundle exec standardrb                     # Lint (StandardRB, targets Ruby 3.1)
bundle exec standardrb --fix              # Auto-fix lint issues
bundle exec rake                           # Run both specs and linter (default task)
RUN_INTEGRATION=1 bundle exec rspec        # Include integration tests (skipped by default)

Pre-commit hook: A git pre-commit hook runs standardrb --no-fix and blocks commits on lint failures. Fix with bundle exec standardrb --fix before committing.

CI: Runs lint on Ruby 3.4, tests on Ruby 3.2/3.3/3.4.


Core Design Principles

Deep Modules Philosophy

From John Ousterhout's A Philosophy of Software Design: The best modules are deep — they hide significant complexity behind a simple interface.

Module Depth = (Complexity Hidden) / (Interface Exposed)

A deep module has a small public API that hides substantial logic. A shallow module exposes an interface almost as complex as its implementation. Do not create shallow modules.

Decision Framework: When to Create a New Abstraction

Before creating a new class, module, concern, or middleware, answer:

  1. What complexity does it hide? If "not much" or "it just delegates," don't create it.
  2. Is the interface simpler than the implementation? If the public API has as many concepts as the internals, it's shallow.
  3. Does it have a reason to change independently? If it always changes in lockstep with another module, merge them.
  4. Can I name it with a specific noun/verb? Vague names (Manager, Handler, Processor, Utils) usually signal shallow design.

Read the full file on GitHub · 437 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. 3d ago First seen · 437 lines · 4,346 tokens per session scan A a1b3138b8188

Subscribe to this mod's changes

ruby_llm-agents CLAUDE.md is an instructions file published in the GitHub repository adham90/ruby_llm-agents (136 stars, last pushed 1mo ago), licensed MIT. It adds 4,346 tokens to every session, about $0.0217 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 2026-08-30.