proofloop CLAUDE.md

Repository instructions for Proofloop, an AI agent orchestrator: a tool that runs agents until defined completion conditions are met, then can pause and resume them. It supports Claude Code, OpenCode, and Codex through a layered Python application.

In plain words
What is it for?
Use them when developing Proofloop, running its tests and checks, adding or changing agent providers, and deciding which of its architectural layers should contain new code.
Why use it?
They give contributors a quick way to follow the project’s testing, formatting, typing, asynchronous-code, logging, and architecture rules. This keeps changes consistent across the command-line, business-logic, application, and infrastructure layers.

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/exiw-ai/proofloop/claude-md
Clone the repo
git clone --depth 1 https://github.com/exiw-ai/proofloop
Per session 551 This file is loaded in full into every session.
When invoked 551 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 $0.00551 $0.00551
Opus 5 $0.00275 $0.00275
Sonnet 5 $0.00110 $0.00110
Haiku 4.5 $0.00055 $0.00055

Measured 2d ago against content hash 60ef0f8bd097, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

proofloop 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 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.

CLAUDE.md · 86 lines

How it starts

The opening of the file, as written. The whole thing — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.

CLAUDE.md

Instructions for AI coding agents working on this repository.

Quick Reference

make test       # Run tests
make pre-commit # Run linters, typechecker, formatters
make check      # Run all checks (pre-commit + tests)

Project Overview

Proofloop is an AI agent orchestrator - agents that run until done. Define conditions, go to sleep, wake up to results. Supports multiple providers: Claude Code, OpenCode, and Codex.

Key technologies: Python 3.12+, Typer (CLI), Pydantic, asyncio, Claude Code SDK, OpenCode, Codex

Code Style

  • Python 3.12+ with type hints required
  • Async/await for I/O operations
  • Use loguru for logging
  • Follow existing patterns in the codebase

Comments Policy

  • Avoid comments that restate the code
  • Docstrings only for public interfaces and non-obvious logic
  • Goal: self-documenting code

Architecture

The project follows layered architecture:

src/
├── cli/            # Command-line interface
├── domain/         # Business logic, entities, value objects
├── application/    # Use cases, orchestration
└── infrastructure/ # External adapters (Claude SDK, Git, MCP)

Rules:

  • Domain layer has no external dependencies
  • Infrastructure implements interfaces defined in domain
  • Keep business logic in domain, not in adapters

Guidelines

Do

  • Follow existing project structure and patterns
  • Keep changes minimal and focused
  • Add tests for functional changes
  • Run make check before committing

Don't

  • Commit secrets or API keys
  • Break existing tests
  • Add unnecessary defensive code for impossible cases
  • Invent custom implementations when a library exists

Dependencies

Core libraries used in the project:

  • Validation: pydantic
  • Retry logic: tenacity
  • Logging: loguru
  • HTTP: httpx

When Done

A change is complete when:

  • All checks pass (make check)
  • New behavior is covered by tests
  • Working tree is clean

System Contract

Key guarantees that must be preserved when modifying the core pipeline:

Read the full file on GitHub · 86 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 · 86 lines · 551 tokens per session scan A 60ef0f8bd097

Subscribe to this mod's changes

proofloop CLAUDE.md is an instructions file published in the GitHub repository exiw-ai/proofloop (38 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 551 tokens to every session, about $0.0028 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.

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