SEA Software Engineering Academy gGmbHmsg systems ag

Agentic Software Engineering

Boost your daily productivity while
keeping yourself in the driver's seat!
Fuse Agentic AI Coding with
traditional Software Engineering!
Let the agent handle the chores while
you stay in control of the craft!
Various operation modes provided —
you choose the best for the job!
github (author stars)github (author followers)github (release)github (project stars)How to star the ASE project on GitHub

Agentic Software Engineering (ASE) is the opinionated Open Source toolkit of renowned and prolific author Dr. Ralf S. Engelschall, for fusing the concepts of Agentic AI Coding and traditional Software Engineering. ASE ships as a plugin for the software development tool Anthropic Claude Code CLI — and with reduced support also for the alternative tools GitHub Copilot CLI and OpenAI Codex CLI. It comprises agent hooks, parametrizable agent skills, an underlying Model-Context-Protocol (MCP) service and a companion Command-Line Interface (CLI).

Dr. Ralf S. Engelschall in a
studio interview on ASE

ASE incorporates reasonable methodology and automation aspects to support the daily, recurring tasks of a Software Developer and Software Architect. The comprehensive functionality spans from brainstorming ideas, searching the Web, asking foreign LLMs, discovering components, through evaluating alternatives, challenging statements, analyzing root causes, managing tasks, grilling task plans, all the way to analyzing, fixing, refactoring, and crafting code, reviewing changesets, and many more.

Claude Code:

Boost Your Daily Software Engineering

Pre-manufactured skills for the Agentic AI Tool that fold reasonable methodology and automation into the recurring work-steps of industrial Software Engineering — with you in the loop and in the driver's seat to still ensure stable results quality.

Token and Time Optimization

Switch persona to cut down tokens and reading time

To reduce your overall output tokens and necessary reading time, switch the communication style of the LLM across five built-in intensity levels: from the decorative, eloquent, and explaining writer, the concise, factual, and accurate engineer (default), the layered, pyramid-structured journalist, through the brief, factual, and abbreviating telegrapher, down to the terse, rough, and stuttering caveman.

Claude Code
/ase-meta-persona engineer

Parametrized Brainstorming

Diverge, cluster, and shortlist new ideas

Turn a fuzzy topic into a focused shortlist: first diverge into a broad set of ideas, then converge by clustering and scoring them, and finally distill a ranked shortlist with a recommended direction to pursue.

Claude Code
/ase-meta-brainstorm --max-clarify=0 --min-ideas=20 clever and useful AI skills for software engineering

Root-Cause Analysis

Trace root cause with Five-Whys method

Drill past symptoms to the real cause: the Five-Whys method iteratively asks "why?" to trace a problem or claim back to its root, optionally branching into multiple causal paths to widen the investigation.

Claude Code
/ase-meta-why --width=2 is the Decibel (dB) unit a logarithmic one?

Component Discovery

Discover the right components for your technology stack

Get methodical support in finding suitable libraries and frameworks to establish or extend your technology stack — grounded, not guessed, by taking into account the downloads per time, age of component, last update time, and GitHub stars.

Claude Code
/ase-arch-discover command-line option and argument parsing library

Multi-Criteria Decisions

Evaluate alternatives with a multi-criteria decision matrix

Compare alternatives the rigorous way: a weighted multi-criteria decision matrix turns a fuzzy "which is best?" question into a transparent, defensible verdict.

Claude Code
/ase-meta-evaluate Commander vs. Yargs vs. Optique, focus on popularity, features, ease of use, and TypeScript support

Play Advocatus Diaboli

Challenge your decision with an Advocatus Diaboli

Know the antitheses to your decision before others bring them up. Let the decision be challenged by a relentless Advocatus Diaboli and even get a resulting synthesis based on Dialectical Reasoning.

Claude Code
/ase-meta-diaboli --count=10 We should migrate the ASE project from TypeScript to Rust

Play Steelman

Strengthen your decision with a Steelman

Further improve the strength of your decision before others challenge it. Let the decision be fortified by a helpful Steelman into a stronger position that consolidates everything that genuinely strengthens it while honestly bounding where it holds.

Claude Code
/ase-meta-steelman --count=10 ASE is a useful Claude Code plugin

Document Distillation

Distill a document down to its ranked key points

Cut a long document down to its essence: distill it into a flat, importance-ranked list of key points, each with a salience rank, a rationale, and a verbatim, line-cited evidence snippet.

Claude Code
/ase-docs-distill --top=3 @plugin/meta/ase-format-spec.md

Document Proofreading

Analyze documents for spelling, punctuation, and grammar

Scan your documents for problems in spelling, punctuation, and grammar. Optionally, let the documents be automatically corrected.

Claude Code
/ase-docs-proofread --auto @README.md

Logical Code Analysis

Analyze code for logic and control-flow defects

Scan your source code for problems in its logic, semantics, and control flow, with evidence-grounded, line-cited findings instead of vague hand-waving.

Claude Code
/ase-code-analyze --severity=MEDIUM @tool/src/

Alternative Approach Funnel

Craft through a funnel of approaches

Plan-driven, but not too direct and hence with enough preliminary consideration: ASE first proposes a "funnel" of crafting alternative approaches, lets you pick a suitable one, and only then composes a structured task plan.

Claude Code
/ase-code-craft --next IMPLEMENT,DELETE hello: "ase hello" CLI command which prints a nice "Hello World!" in blue to the terminal

Named and Persisted Plans

Implement against named, persisted, structured task plans

Use named, persisted, structured, and cross-session task plans in a strict, fixed format — then implement them as a complete, surgical change set you stay in control of.

Claude Code
/ase-task-implement hello

Automatic ChangeLog

Auto-generate ChangeLog entries from Git

Keep your CHANGELOG.md file current without the chore: derive concise, human-readable changelog entries straight from your Git history and fold them into the existing file under the right version and category.

Claude Code
/ase-meta-changelog

See the Productivity Difference

Claude Code CLI is already phantastic, but it can become even better in the context of Software Engineering. Some sample, recurring work-steps, done three ways: old-school by hand, with the bare Claude Code CLI tool, and with the same tool boosted by the ASE plugin.

Manually:
Old-School
Claude Code CLI:
AI Native
Claude Code CLI + ASE:
AI Advanced
Researching
Topic
Hop between Google, Wikipedia, and ChatGPT, stitching the answers together yourself.Fire the agent's WebSearch tool or lean on the LLM's world knowledge for a single take.Run /ase-meta-search or /ase-meta-quorum to poll multiple engines and AI services at once, auto-distilled into one consensus answer.
Finding
Components
Search the Web, tally NPM downloads and GitHub stars by hand, then go with your gut.Let the agent search, gather, and guess through an ad-hoc consolidation prompt.Run /ase-arch-discover to discover, rank, and report candidates via a strict, multi-criteria process.
Evaluating
Alternatives
Open a dozen tabs, skim READMEs, and settle "which is best?" on gut and recency bias.Ask the agent and get a confident single-shot pick with no defensible reasoning.Run /ase-meta-evaluate for a weighted decision matrix, plus /ase-meta-steelman, turning a fuzzy question into a defensible verdict.
Analyzing
Problems
Browse the code and bisect with console.log until the bug surfaces.Hand the agent your error messages and let it ad-hoc hunt down and patch the bug.Run /ase-code-resolve to investigate, decide an approach, plan a task, and resolve it end-to-end.
Crafting
Code
Hand-edit every aspect of the new feature and hope it's right first time.Tell the agent to "craft this" and get a plausible but unbounded change set to review after the fact.Run /ase-code-craft for a funnel- and plan-driven build, where the agent grills you and your plan to kill underspecified aspects.
Refactoring
Code
Hand-edit each call site, hope the tests catch the rest, and hold the whole blast radius in your head.Tell the agent to "refactor this" and get a plausible but unbounded change set to review after the fact.Run /ase-code-refactor for a surgical, scoped change set, guided by refactoring tenets and an optional preflight you approve first.
Spec-Driven
Development
Up-front write a monolithic spec in Word, then manually derive and implement User-Stories from the Backlog.Iteratively write a modular spec in ad-hoc Markdown, manually derive Plans, and let Claude Code implement them.Iteratively write a modular spec in strict, pre-defined Markdown, then regularly run /ase-sync-reconcile to keep code and spec aligned.

Easy Setup

Getting started with ASE takes just a couple of commands. Follow the prerequisites and the installation steps on the left, run the ready-to-copy commands shown on the right, and you are up and running within minutes.

As a prerequisite, please install the Agentic AI Coding tool Anthropic Claude Code CLI (or with reduced support the alternatives GitHub Copilot CLI or OpenAI Codex CLI) and the essential run-time environment Node.js for your particular platform.

Then execute the two commands on the right under Installation. The first command installs the ASE Command-Line Interface (CLI), the second uses it to install the ASE Plugin into your Agentic AI Coding tool. The ase setup update keeps ASE up to date and the ase setup uninstall will reverse the installation residue-free again at any time. The --tool claude is the default and not necessary for the mainstream tool Anthropic Claude Code CLI.

The --scope option defaults to user (global, machine-wide). Use --scope project to share the plugin registration via the project repository, or --scope local to share the plugin via the project repository while keeping it out of version control. Option --scope is only supported for --tool claude.

Terminal:Installation
npm install -g @rse/ase
ase setup install
[--tool claude|copilot|codex]
[--scope user|project|local]
Terminal:Updating
ase setup update
[--tool claude|copilot|codex]
[--scope user|project|local]
Terminal:Uninstallation
ase setup uninstall
[--tool claude|copilot|codex]
[--scope user|project|local]

Power-Up with Custom Statusline

ASE can optionally configure a rich, information-dense statusline for the Anthropic Claude Code CLI (or with reduced support the alternative GitHub Copilot CLI), rendered by the built-in ase statusline command.

It surfaces your current user, project, task, and session, the active model, effort, and thinking state, the persona, and a live context-window gauge, all colorized and width-aware. ASE will just add the corresponding statusLine entry to the tool's settings.json, leaving any of your other settings untouched.

NOTICE:The custom statusline is NOT required for ASE to work. It is fully optional when using ASE, but will power up your Agentic AI Coding session with an at-a-glance overview of the current context. An existing, hand-crafted statusLine is always preserved. The statusline is supported for --tool claude and --tool copilot, but not for --tool codex.

The <format> template lines support the following %x expansion constructs, plus arbitrary <color>...</color> markup (color being one of black, red, green, yellow, blue, magenta, cyan, white, or default). When no <format> arguments are given, ASE uses the following implicit positional arguments: "<blue>%u</blue> <red>%p</red> <black>%T</black> %s" "%m %e %t" "%P %c"

Terminal:Statusline Installation
ase setup statusline activate
[--tool claude|copilot|codex]
[--scope user|project|local]
[--width <n>] [--margin <n>]
[--no-icons] [--no-labels]
[<format> ...]
Terminal:Statusline Uninstallation
ase setup statusline deactivate
[--tool claude|copilot|codex]
[--scope user|project|local]
MeaningMeaning
%uUser name%SSession usage
%pProject name%DSession resets
%TTask id%WWeekly usage
%sSession id%QWeekly resets
%mModel name%HElapsed time
%eEffort level%XSession cost
%tThinking state%aLines added
%OOutput style%rLines removed
%PPersona style%bGit branch
%cContext gauge%gGit changes
%CToken count%GUntracked files
%MMemory usage%dWorking directory
%VTool versions

Power-Up with Foreign Services

ASE can optionally leverage additional Model-Context-Protocol (MCP) servers for contacting foreign LLM services (OpenAI ChatGPT, Google Gemini, DeepSeek, xAI Grok, Alibaba Qwen, or Z.AI GLM) via MCP-to-OpenAI servers and Web Search services (Brave, Perplexity, or Exa) via their dedicated MCP servers.

You only need the access tokens of those services at hand. ASE will just add the corresponding local MCP servers to the Agentic AI Coding tool.

NOTICE:These add-on MCP servers are NOT required for ASE to work. They are fully optional when using ASE, but will power up some of the ASE skills (like ase-meta-chat, ase-meta-search, and ase-meta-quorum) with additional functionality.
Terminal:MCP Installation
ase setup mcp list
export ASE_MCP_KEY_<service>="<token>"
ase setup mcp activate
[--tool claude|copilot|codex]
[--scope user|project|local]
Terminal:MCP Uninstallation
ase setup mcp deactivate
[--tool claude|copilot|codex]
[--scope user|project|local]

Understand Our Philosophy

ASE is based on Dr. Ralf S. Engelschall's Agentic AI Level model, which tries to focus on the "sweet-spot" levels. Here ASE provides its various skills to let the engineer drive the next steps. Some skills are stand-alone, others are part of a particular operation mode.

Agentic AI Levels

From Manual To
Fully Autonomous

ASE supports Dr. Ralf S. Engelschall's Agentic AI Level Model, which classifies the gradual sharing of work with AI agents.

Level 0 (manual): you do everything manually and without support from the AI agent at all — think "do-it-yourself", where you obviously know the results and inherently train your own skills.

Level 1 (assisted): you are supported by the AI agent for individual steps only — think "assistant", where you still know all results in detail, but inherently your practical execution skills can partly start to wane in the long term.

Level 2 (supervised): you are working on an entire task with the AI agent as an equal pair — think "co-worker", where your colleague shares the task workload with you, you mutually review each other's results, hence you still know all results in detail, but inherently your practical execution skills can be lost in the long term.

Level 3 (delegated): you are delegating entire workflows to the AI agent — think "offshoring", where your colleague shoulders the entire workload for you, you just review the results, but your own practical execution skill and your ability to quality assure the results will be forever lost if you are not regularly, at least virtually, replaying the delegated work.

Level 4 (autonomous): you are assigning entire process lifecycles to the AI agent — think "third-party", where your vendor does everything for you fully autonomously, you just take the results as is, and your own practical execution skill doesn't matter as it is already out of scope.

Skills & Workflows

Step-Wise Driving,
You In The Loop

Within the above Agentic AI Levels, ASE provides its various skills to let the engineer trigger the next steps. Some skills are stand-alone, others are part of a particular operation mode that chains skills into a repeatable workflow.

Each skill is named /ase-xxx-xxx and most of those skills even have useful Unix-style options for adjusting their behavior. Click on the skill name in the following table to open its Unix-style man-page in an in-page popup or run /ase-xxx-xxx --help at run-time.

Standalone
Mode 1/2

  • ase-meta-
  • ase-meta-
  • ase-meta-
  • ase-meta-
  • ase-meta-
  • ase-meta-
  • ase-meta-
  • ase-meta-
  • ase-meta-
  • ase-meta-

Standalone
Mode 2/2

  • ase-arch-
  • ase-code-
  • ase-code-
  • ase-code-
  • ase-docs-
  • ase-docs-
  • ase-meta-
  • ase-meta-
  • ase-meta-
  • ase-meta-

Task
Mode

  • ase-task-
  • ase-task-
  • ase-task-
  • ase-task-
  • ase-task-
  • ase-task-
  • ase-task-
  • ase-task-
  • ase-task-
  • ase-task-
  • ase-task-

Funnel
Mode

  • ase-arch-
  • ase-code-
  • ase-code-
  • ase-code-
  • ase-code-

Sync
Mode

  • ase-sync-
  • ase-sync-
  • ase-sync-

Operation Modes

Advanced Workflows, Maximum Flexibility

On top of the usual Agentic AI Coding tool modes Ad-Hoc Mode and Plan Mode, ASE adds three operation modes:

Task Mode: The more advanced mode where the user initially crafts and then continuously refines a task plan. The task plan has a fixed format and is persisted and hence is available across agent sessions.

Funnel Mode: The even more advanced mode where the user first sketches the plan, then the agent figures out possible approaches, then the user selects one approach, then a task plan is created for this approach, and then finally this switches over to the regular Task Mode. The task plan has a fixed format and is persisted and hence is available across agent sessions.

Sync Mode: The separated reconciliation mode where the user aligns the code and documentation to the specification and architecture ("forward engineering") or even aligns the specification and architecture to the code and documentation ("reverse engineering").

How to Use Workflows

ASE is based on individual skills which can both be sequentially triggered and partially also automatically chained. This way typical practical workflows can be realized. The following shows typical workflows in the Agentic AI Coding tool Anthropic Claude Code CLI.

Which workflow to choose depends on the requirements, the desired pros and the acceptable cons. ASE does not mandate a particular workflow as the particular tasks of Software Engineers are too different. In practice, one needs nearly all workflows, each one at a certain time.

Ad-Hoc Prompt

Simple Prompting

The simplest approach is a straight and ad-hoc instruction, as provided by Anthropic Claude Code CLI already without ASE. The pro is its simplicity, the con is its weakness due to missing detail decisions. Use for simple tasks only.

Claude Code
Create "hello" CLI command which prints "Hello World" in red to the terminal

Plan Mode

Simple Planning

The advanced approach is to use the standard plan mode, as provided by Anthropic Claude Code CLI already without ASE. The pro is its convenience and possibility to interactively add detail decisions, the con is its weakness because plans cannot easily co-exist and be edited in parallel. Use for medium-complex tasks under ad-hoc planning.

Claude Code
/plan
Create "hello" CLI command which prints "Hello World" in red to the terminal
(3) Change output color to blue
(3) Use NPM package "chalk" for coloring
(1) (Yes, and use auto mode)

Task Mode #1

Advanced Planning

The advanced approach is the ASE Task Mode. The pro is that its plans can easily co-exist and be edited in parallel, the con is its weakness because crafting / resolution / refactoring tenets are not internalized by the LLM and hence cannot guide the operation. Use for complex tasks under explicit planning.

Claude Code
/ase-task-edit Create "hello" CLI command which prints "Hello World" in red to the terminal
Change output color to blue
Use NPM package "chalk" for coloring
IMPLEMENT
DELETE

Task Mode #2

Professional Planning

The professional approach is the ASE Task Mode, combined with other ASE task skills. The pro is that the plan becomes more robust and the result more foreseeable, the con is its necessary extra steps. Use for complex tasks where extra "thinking before acting" is desired.

Claude Code
/ase-task-edit Create "hello" CLI command which prints "Hello World" in red to the terminal
Change output color to blue
Use NPM package "chalk" for coloring
GRILL
PREFLIGHT
IMPLEMENT
DELETE

Funnel Mode

Expert Planning

The expert approach is the ASE Funnel Mode, followed by the ASE Task Mode. The pro is that crafting / resolution / refactoring tenets are internalized by the LLM and multiple solution approaches are evaluated, the con is its necessary extra steps. Use for complex tasks under explicit planning where the solution approach is not obvious.

Claude Code
/ase-code-craft Create "hello" CLI command which prints "Hello World" in red to the terminal
A1
Change output color to blue
Use NPM package "chalk" for coloring
GRILL
PREFLIGHT
IMPLEMENT
DELETE

Quick Mode

Quick & Dirty

The quick & dirty approach is the ASE Funnel Mode, followed by the ASE Task Mode, but driven in an automatic approach decision mode, without validations and with an immediate implementation. The pro is its grounding, speed and batch operation, the con is that it might require subsequent session reverts or ad-hoc adjustment promptings. Use for medium tasks where backing off the previous state is acceptable.

Claude Code
/ase-code-craft --quick Create "hello" CLI command which prints "Hello World" in red to the terminal

Reconciliation Mode

Artifact Reconciliation

The artifact reconciliation approach updates the CODE and DOCS artifacts based on (changed) information in the SPEC and ARCH artifacts. The pro is its precise grounding and batch operation, the con is its requirement for using a specification-driven development approach. Use for very complex projects where explicit specifications drive the development.

Claude Code
/ase-sync-reconcile --source SPEC,ARCH --target CODE,DOCS

Understand Design Assumptions

ASE is based on the following assumptions which inherently drive whether ASE is for you or not:

  • You are in the Driver's Seat:
    ASE assumes you are an experienced Software Developer or even Software Architect and want to still be in the driver's seat, i.e., you decide and trigger the next operations and you are reviewing the results. This means ASE is neither about plain "vibe coding" nor driving fully autonomous Software Engineering agents.
  • Thinking before Acting:
    ASE assumes you are an engineer, which means that you prefer thinking long enough instead of immediately acting to increase the overall result quality and at least avoid unnecessary post-adjustments to your results.
  • Flexible Operation Modes:
    ASE assumes your job as a Software Developer and Software Architect inherently requires multiple operation modes, ranging from ad-hoc LLM prompting, through user-story-based planning mode, through an approach-selecting funnel mode, up to an artifact reconciliation mode. You are free to choose per task the mode which fits best.
  • Unix CLI-style Skills:
    ASE assumes you like the style of the Unix command-line interface (CLI) and appreciate skills which are intended to be explicitly called as commands and which can be parametrized through Unix-style options.

Know Design Decisions

ASE is based on the following distinct decisions which inherently shaped the overall design of ASE:

  • Agent & Plugin:
    ASE is a real plugin for the Agentic AI Coding tools Anthropic Claude Code CLI, GitHub Copilot CLI, and OpenAI Codex CLI, and can be non-intrusively and easily installed and later also residue-free uninstalled from those tools at any time.
  • Recurring Software Engineering Tasks:
    ASE targets the most important, recurring tasks in industrial Software Engineering only. Especially, ASE is not targeting the Consulting, Operations, or Management disciplines.
  • Built-In Methodology:
    ASE tries to ship out-of-the-box with built-in well-known methodology aspects to make them more accessible to the average software developer and software architect.
  • Skills & MCP/CLI:
    The probabilistic LLM-based ASE skills are strongly coupled to and work on top of the deterministic TypeScript-based ASE MCP/CLI service. In particular, the ASE skills are not written to be used standalone or in foreign environments.
  • Configuration Scopes:
    Parameters of the agent and the project can be configured across the hierarchy of the distinct scopes default, user, project, and session. This allows the flexible configuration of ASE.
  • Session Constitution:
    All agent sessions have meta descriptions — a sort of "constitution" — preloaded at all times, based on the configured parameters. This allows controlling the general LLM behavior. Additionally, skills load more meta descriptions on demand. This allows skills to reuse definitions.
  • Task Skills:
    Recurring tasks are supported with dedicated skills, which can be manually triggered as commands. This allows explicitly controlling the specific agent behavior. Skills are grouped into meta (ase-meta-*), code (ase-code-*), architecture (ase-arch-*), task (ase-task-*), documentation (ase-docs-*), and synchronization (ase-sync-*) families.
  • Artifact Formats:
    The format of the primary input artifacts of Software Engineering (requirements specification and architecture description) is strictly defined. This allows both humans and agents to operate on them concurrently.

Overview of the Architecture

ASE is not just a bunch of skills for Agentic AI Coding tools like Anthropic Claude Code CLI. Instead, ASE is based on an integrated set of Hooks, Skills, a Model-Context-Protocol (MCP) service and a Command-Line Interface (CLI).

Software Architecture

Hooks, Skills, MCP, CLI

At first glance, ASE is just a plugin of skills for the Agentic AI Coding tool. Actually, it is more. ASE uses Hooks of the Agentic AI Coding tool to load its constitution (important global information).

Then it provides the various user-facing skills (ase-xxx-xxx) for the probabilistic execution of actions. Additionally, it uses an underlying (automatically started per project) MCP service (ase_xxx tools) for deterministic execution of actions. Finally, it provides CLI commands (ase xxx).

Agent Tool and LLM Compatibility

ASE uses elaborate control structures (flow, step, if, while, define, expand, etc.) and XML-based placeholders (constants, variables) to realize its skills plus Agent Harness hooks, built-in tools, and an MCP service to provide its remaining functionality. As a consequence, it requires the combination of a compatible Agent Harness plus a strong instruction-following LLM. The following tables show the currently known compatibility for ASE.

Agent HarnessVersionCompat.Goal
Anthropic Claude Code CLI2.1.x100%primary
GitHub Copilot CLI1.0.x95%secondary
OpenAI Codex CLI0.x.x90%secondary
LLMVersionCompat.Goal
Anthropic Claude Fable5100%secondary
Anthropic Claude Opus4.8100%primary
Anthropic Claude Sonnet595%secondary
Anthropic Claude Haiku4.570%none
OpenAI GPT5.595%none
Gemini Flash3.570%none

OpenAI Codex CLI has no scriptable custom status line support at all.

NOTICE: Agent Harness Compatibility

This compatibility statement here does not mean ASE cannot be used with the Anthropic Claude Code IDE plugin or Desktop app. It only means that ASE is primarily developed against Anthropic Claude Code CLI. For the IDE plugin or Desktop app at least expect certain incompatibilities or missing functionalities (e.g. the statusbar, etc.).

NOTICE: LLM Compatibility

This compatibility statement here does not mean ASE cannot be used with other combinations than Anthropic Claude Code CLI + Claude Opus, like the cheap and powerful Anthropic Claude Code CLI + Minimax-M3 or the popular alternative OpenAI Codex CLI + GPT combination. It only means that ASE is primarily developed against Anthropic Claude Code CLI + Claude Opus and hence this combination showed no incompatibilities or unexpected results.

For other combinations, the usual and hence expected incompatibilities are that some instructions of the ASE skills are not 100% strictly followed.

About the Author

ASE is primarily authored by Dr. Ralf S. Engelschall (abbreviated RSE and stylized ), a German Computer Scientist, Executive Manager, Solution Architect and Software Artist.

RSE is CTO msg group and Director msg Research at msg — a Software Engineering company group with more than 11,000 people — managing director and co-founder of Software Engineering Academy (SEA), managing director and co-founder of OpenPKG, and especially also one of the founders of the Apache Software Foundation (ASF).

RSE was awarded the Balzert Prize 2023 of the Gesellschaft für Informatik (GI) "for his outstanding contribution to the teaching of Computer Science" with his Multimedia Didactics, and the Ernst Denert Software Engineering Prize 2018 for his Hierarchical User Interface Component Architecture (HUICA).

RSE has been a well-known Open Source authority for over 35 years and the founder and prolific author of numerous popular software projects, like Apache mod_rewrite, Apache mod_ssl, OpenSSL, OpenPKG, GNU Shtool, GNU Pth, OSSP uuid, ComponentJS, Studio Canvas, Studio AI, Rundown, Trait-TS, MQTT+, ASE, and about 300 more.

You can follow RSE and his current Open Source software developments on GitHub, and follow him and his current (German) article publishing on LinkedIn.

Thanks to the Contributors

The ASE project received support from the following individual contributors (in alphabetical order), coming from both the industrial Software Engineering and Open Source Software contexts. Many thanks to them for their valuable feedback and support!

Lars Artmann
Mathias Böni
Matthias Brusdeylins
Noah S. Engelschall
Jochen Hörtreiter
Michael Kagel
Maximilian Marsch
Christian Reiber
Zoltan Ruzman
Michael Schäfer
Markus Schnappinger
Jan Tuttas
Linda Zeman

Thanks to the Sponsors

The ASE project is supported by the following organizations through time, material, knowledge, and/or experience. Many thanks to them for their support!

msg systems ag
msg systems ag
SEA Software Engineering Academy gGmbH
SEA Software Engineering Academy gGmbH