itscloud0

Effortlane — AI skill for Claude Code

AI community

Effortlane: open-source model and reasoning-effort routing for coding agents.

How to install Effortlane

This entry records only its repository, not the path inside it, so there is no exact command to give. Open itscloud0/effortlane and copy the folder into ~/.claude/skills/, or the file into ~/.claude/agents/.

What Effortlane does

Effortlane: open-source model and reasoning-effort routing for coding agents. Native Codex auth, Shadow evaluation, cache-aware policy and local metrics. macOS; experimental Claude Code Shadow.

Alternatives in AI

  • Ccstatusline — by sirmalloc - A highly customizable status line formatter for Claude Code CLI that displays model info, git b 5.5k ★
  • Codex Skill — by klaudworks - Enables users to prompt codex from claude code 914 ★
  • My Free Code — Open-source multi-provider AI gateway for Claude Code and other coding agents, with model routing, streaming 633 ★

README

Effortlane — model and reasoning-effort routing for coding agents

Effortlane

**Choose model and reasoning effort. Keep your native coding agent.**

Open-source LLM routing for Codex on macOS, with Shadow evaluation and local usage telemetry.

[![Tests](https://img.shields.io/github/actions/workflow/status/itscloud0/effortlane/tests.yml?branch=main&style=flat-square&label=tests)](https://github.com/itscloud0/effortlane/actions/workflows/tests.yml) [![MIT](https://img.shields.io/badge/license-MIT-94a3b8?style=flat-square)](LICENSE) ![macOS](https://img.shields.io/badge/platform-macOS-94a3b8?style=flat-square) ![Experimental](https://img.shields.io/badge/status-experimental-fbbf24?style=flat-square)

[**Try Shadow →**](#quick-start) · [See the evidence](#what-we-have-measured) · [How it works](#how-it-works) · [Get help](#help-build-effortlane)

What is Effortlane?

Effortlane is a local model and reasoning-effort router for coding agents. It selects from your authenticated Codex model catalog before a user turn, preserves native ChatGPT authentication, and records routing evidence locally. In **Shadow mode**, you keep the real executor and effort while Effortlane records what it would recommend.

The goal: **less subscription allowance per correctly completed task**. A cheap call that causes retries or rework is not a saving.

What you get Why it matters
Model and effort selection Adjust reasoning depth as well as model capability
Shadow before Auto Inspect recommendations before letting them change execution
Decisions between turns Keep one executor through a turn's thoughts and tool calls
Native login and context Continue using your existing Codex account and full executor context
Local evidence and recovery Inspect routes, cache counters and failures; return to native Codex

[!IMPORTANT] **Experimental. Subscription savi