Bobby NoelteandClaude Opus 5.5 dd41e5d57c chore(ai): add agent self-review and a CI check for agent-assisted PRs
Additional changes
Agents write a task plan to .scratch/<branch>/plan.md and self-review with code-review
Agents fix hard violations, report judgement calls and rerun the self-review before every push
Agents declare the review in a Self-review section of the pull request body
Add the ai-self-review workflow and scripts/check_pr_self_review.py for pull requests into main
Detect agents by Assisted-by or Anthropic Co-Authored-By trailers, body markers or the AI box
Fail only on a missing Self-review section or a missing, malformed or stale Reviewed SHA
Note an unticked AI-assisted box and malformed optional lines without failing
Comment on the pull request how to fix a failure and update that one comment on later runs
Show the same report in the job summary and accept Windows line endings in the body
Skip dependabot and release pull requests
Run on pull_request_target with the base branch's script, never pull request code
Add the pull request template: the pr skill's sections plus Checklist and Self-review
Require always valid optimization parameters in the energy-domain skill
Require datetimeutil for date and time handling in new and changed code in AGENTS.md
Require the Assisted-by trailer on agent commits in the commit-messages skill
Add tests for the check in tests/test_ai.py
Add GLOSSARY.md and the agent tracker docs in docs/agents/
Add decision record docs/adr/0001 with an ADR index in the docs toctree
Document the workflow in docs/develop/ai.md and CONTRIBUTING.md
Exclude docs/agents from Sphinx and PyMarkdown and ignore .scratch/
Document cz check instead of make gitlint for commit messages in docs/develop/ai.md

Assisted-by: Claude Code (claude-opus-5-5)
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
2026-11-11 15:37:33 +01:00
2025-01-24 20:08:48 +01:00
2025-04-07 22:23:35 +02:00
2024-05-03 10:43:31 +02:00
2024-11-15 22:27:25 +01:00
2026-11-11 09:36:16 +01:00

AkkudoktorEOS AkkudoktorEOS

Build optimized energy management plans for your home automation

AkkudoktorEOS is a comprehensive solution for simulating and optimizing energy systems based on renewable sources. Optimize your photovoltaic systems, battery storage, load management, and electric vehicles while considering real-time electricity pricing.

Why use AkkudoktorEOS?

AkkudoktorEOS can be used to build energy management plans that are optimized for your specific setup of PV system, battery, electric vehicle, household load and electricity pricing. It can be integrated into home automation systems such as NodeRED, Home Assistant, EVCC.

🏘️ Community

We are an open-source community-driven project and we love to hear from you. Here are some ways to get involved:

What do people build with AkkudoktorEOS

The community uses AkkudoktorEOS to minimize grid energy consumption and to maximize the revenue from grid energy feed in with their home automation system.

Why not use AkkudoktorEOS?

AkkudoktorEOS does not control your home automation assets. It must be integrated into a home automation system. If you do not use a home automation system or you feel uncomfortable with the configuration effort needed for the integration you should better use other solutions.

Quick Start

Upgrading an existing installation?

The next release includes a substantial energy-planning update: 15-minute GENETIC plans, EV departure targets, flexible household loads, optional battery export, and better measurement and PV-forecast tools. These features are currently unreleased.

Existing Home Assistant automations, Node-RED flows and custom API clients may need changes. Devices now use stable IDs, the new optimizer reads hardware settings from EOS configuration, and incomplete forecasts or outdated battery measurements can stop a run. The legacy POST /optimize remains available with GENETIC0; the new POST /v1/optimize has a different request format. Back up your configuration and data before updating.

Read the upgrade guide and changelog before switching an existing installation.

Start with Docker

Run EOS with Docker (access dashboard at http://localhost:8504):

docker run -d \
  --name akkudoktoreos \
  -p 8503:8503 \
  -p 8504:8504 \
  -e OPENBLAS_NUM_THREADS=1 \
  -e OMP_NUM_THREADS=1 \
  -e MKL_NUM_THREADS=1 \
  -e EOS_SERVER__HOST=0.0.0.0 \
  -e EOS_SERVER__EOSDASH_HOST=0.0.0.0 \
  -e EOS_SERVER__EOSDASH_PORT=8504 \
  --ulimit nproc=65535:65535 \
  --ulimit nofile=65535:65535 \
  --security-opt seccomp=unconfined \
  akkudoktor/eos:latest

System Requirements

  • Python: 3.11 or higher
  • Architecture: amd64, aarch64 (armv8)
  • OS: Linux, Windows, macOS

Note

: Other architectures (armv6, armv7) require manual compilation of dependencies with Rust and GCC.

Installation

Home Assistant add-on

Supports aarch64 Architecture Supports amd64 Architecture

To install the Akkudoktor-EOS add-on in Home Assistant:

Open your Home Assistant instance and show the add add-on repository dialog with a specific repository URL pre-filled.

  1. Add the repository URL:

    In Home Assistant, go to:

    Settings → Add-ons → Add-on Store → ⋮ (top-right menu) → Repositories
    

    and enter the URL of this Git repository:

    https://github.com/Akkudoktor-EOS/EOS
    
  2. Install the add-on:

    After adding the repository, the add-on will appear in the Add-on Store. Click Install.

  3. Start the add-on:

    Once installed, click Start in the add-on panel.

  4. Access the dashboard:

    Click Open Web UI in the add-on panel.

  5. Configure EOS (optional): In the dashboard, go to:

    Config
    
  1. create docker compose file:
services:
  eos:
    image: akkudoktor/eos:latest
    container_name: akkudoktoreos
    restart: unless-stopped
    environment:
      - OPENBLAS_NUM_THREADS=1
      - OMP_NUM_THREADS=1
      - MKL_NUM_THREADS=1
      - EOS_SERVER__HOST=0.0.0.0
      - EOS_SERVER__PORT=8503
      - EOS_SERVER__EOSDASH_HOST=0.0.0.0
      - EOS_SERVER__EOSDASH_PORT=8504
      # Change this to a random secret, e.g. output of: openssl rand -hex 32
      - EOS_SERVER__EOSDASH_SESSKEY=change-me
    ports:
      - "8503:8503"   # EOS API
      - "8504:8504"   # EOSdash
    volumes:
      - ./data:/data
  1. create a .env file with the secret:
echo "EOS_SESSKEY=$(openssl rand -hex 16)" > .env
  1. pull image and start:
docker pull akkudoktor/eos:latest
docker compose up -d
  1. Access EOS:

Configuration and data are stored in ./data.

Note: The docker-compose.yaml in the repository root is intended for development (it builds the image locally and relies on the repository's .env). Use the file above if you only want to run the published image.

From Source

git clone https://github.com/Akkudoktor-EOS/EOS.git
cd EOS

Linux:

python -m venv .venv
.venv/bin/pip install -r requirements.txt
.venv/bin/pip install -e .
.venv/bin/python -m akkudoktoreos.server.eos

Windows:

python -m venv .venv
.venv\Scripts\pip install -r requirements.txt
.venv\Scripts\pip install -e .
.venv\Scripts\python -m akkudoktoreos.server.eos

Configuration

EOS uses EOS.config.json for configuration. If the file doesn't exist, a default configuration is created automatically.

Custom Configuration Directory

export EOS_DIR=/path/to/your/config

Configuration Methods

  1. EOSdash (Recommended) - Web interface at http://localhost:8504
  2. Manual - Edit EOS.config.json directly
  3. API - Use the Server API

See the documentation for all configuration options.

Port Configuration

Default ports: 8503 (API), 8504 (Dashboard)

If running on shared systems (e.g., Synology NAS), these ports may conflict with system services. Reconfigure port mappings as needed:

docker run -p 8505:8503 -p 8506:8504 ...

API Documentation

Interactive API docs available at:

  • Swagger UI: http://localhost:8503/docs
  • OpenAPI Spec: View Online

Resources

Contributing

We welcome contributions! See CONTRIBUTING for guidelines.

Contributors

License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

S
Description
This repository features an Energy Optimization System (EOS) that optimizes energy distribution, usage for batteries, heat pumps& household devices. It includes predictive models for electricity prices (planned), load forecasting& dynamic optimization to maximize energy efficiency & minimize costs. Founder Dr. Andreas Schmitz (YouTube @akkudoktor)
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