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Evanflow Improve Architecture

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evanklem
evanflow-improve-architecture

Identify refactoring opportunities by surfacing architectural friction. Apply the deletion test, deep-modules vocabulary, and seams analysis. Each opportunity becomes its own evanflow-writing-plans cycle. Use when reviewing code for refactoring, when a file has grown too large, or when architecture concerns surface during feature work.

Overview

Publisherevanklem
Repositoryevanflow
Skill nameevanflow-improve-architecture
Stars
418
Forks
18
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by evanklem on GitHub. Read the source before you install it.

Installation

Install the Evanflow Improve Architecture AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/evanklem/evanflow/tree/main/skills/evanflow-improve-architecture
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/evanklem/evanflow.git /tmp/evanflow
mkdir -p .claude/skills
cp -r /tmp/evanflow/skills/evanflow-improve-architecture .claude/skills/evanflow-improve-architecture
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Evanflow Improve Architecture in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Evanflow Improve Architecture on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Evanflow Improve Architecture is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

EvanFlow: Improve Architecture

Vocabulary

See evanflow meta-skill. Key terms: module, interface, depth, seam, adapter, deletion test.

When to Use

  • "This file is too big" / "this is hard to follow"
  • A feature plan reveals friction the existing structure can't accommodate cleanly
  • Periodic architectural review (Phase D of EvanFlow rollout)
  • After tests reveal a module has the wrong shape

SKIP when: the user is doing a small, well-scoped change. Don't try to refactor adjacent code.

The Flow

1. Read Context First

  • CONTEXT.md (if it exists) — for canonical domain language
  • docs/adr/ — for prior architectural decisions you must respect
  • The target file(s) and their direct callers

2. Identify Candidates

Walk the codebase noting friction:

  • A concept that bounces across many modules to be understood
  • An interface that rivals its implementation in complexity
  • Files that have grown unwieldy (>400 lines is a smell, >800 is a klaxon)
  • Conditional logic that branches by concept (suggests two adapters in a missing seam)

3. Apply the Deletion Test

For each candidate module: "If I deleted this, where would the complexity go?"

  • Vanishes → the module is bloat; delete it
  • Concentrates across N callers → the module earns its existence; preserve and possibly deepen it
  • Moves to one place → the module is a thin pass-through; consider folding it

4. Embedded Grill — Candidate Selection

Before proposing changes, stress-test:

  • "Which candidate has the highest leverage (improves most caller code)?"
  • "Which candidate is safest to change (smallest blast radius)?"
  • "Are there ADRs that constrain the design we're considering?"
  • "Does the proposed deepening match domain language in CONTEXT.md, or are we inventing new terms?"
  • "If we change this seam, will it require touching tests that currently bypass the interface? (If yes, the test design is also wrong.)"

5. Present Opportunities

For each, name:

  • File(s) affected
  • Current shape (what's wrong)
  • Proposed shape (small interface, complex internals)
  • Benefits (in domain language from CONTEXT.md)
  • Cost (estimated PRs, blast radius)

Prioritize. Recommend ONE to tackle next.

6. Hand Off Per Opportunity

Each opportunity becomes its own evanflow-writing-plans cycle. Do not bundle multiple refactors into one plan — that's a horizontal-slice failure.

If the opportunity changes a public interface, route through evanflow-design-interface first.

Hard Rules

  • Read CONTEXT.md and docs/adr/ before proposing changes. Domain language and prior decisions must inform the design.
  • Deletion test first. Don't add abstractions just because they feel cleaner.
  • One refactor per plan. Bundling refactors hides their individual value and inflates risk.
  • Two adapters before a seam. Don't introduce variation points hypothetically — wait until two real implementations exist.
  • Never auto-commit. Refactor PRs are easy to slip past review when batched; keep them small and explicit.

Hand-offs

  • Opportunity selected → evanflow-design-interface (if interface changes) or evanflow-writing-plans (otherwise)
  • New domain term emerged during discussion → evanflow-glossary to update CONTEXT.md
  • Decision behind the design merits an ADR → write under docs/adr/

Frequently asked questions

What does the Evanflow Improve Architecture AI skill do?

Identify refactoring opportunities by surfacing architectural friction. Apply the deletion test, deep-modules vocabulary, and seams analysis. Each opportunity becomes its own evanflow-writing-plans cycle. Use when reviewing code for refactoring, when a file has grown too large, or when architecture concerns surface during feature work.

Why use Evanflow Improve Architecture on TypingMind?

Because you install it once and use it with any model. Evanflow Improve Architecture is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Evanflow Improve Architecture in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/evanklem/evanflow/tree/main/skills/evanflow-improve-architecture. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Evanflow Improve Architecture?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Evanflow Improve Architecture?

As many as you like. As long as a model supports skills, you can use Evanflow Improve Architecture with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Evanflow Improve Architecture AI skill free?

It is published on GitHub by evanklem. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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