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Refactoring Safely

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AnastasiyaW
refactoring-safely

Change the structure of code that already exists without changing what it does: smells as triggers, the named transformations (extract/inline, move feature, organise data, simplify conditionals), and above all the workflow that makes it safe — characterization tests first, one transformation at a time, green between every step. Use when a file or function is already too large; when asked to "split this module", "extract this", "break up main.py", "clean up this legacy code", "reduce coupling here"; when a shape advisory fires on a grown file; or before any restructuring of code that has users. Do NOT use to decide the target layout of a NEW project (use architecture-first), for unit-level naming and function quality in code you are writing fresh (use code-complexity), for capacity or storage decisions (use system-and-data-design), or to strip over-engineering on request (use lean-code). This is the transformation with a net; deciding WHERE things should end up is a different question, and doing both at once is how refactors lose behaviour.

Overview

PublisherAnastasiyaW
Repositorycodex-claude-code-config
Skill namerefactoring-safely
Stars
212
Forks
32
Bundled files
7
LicenseMIT
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.

  • 7 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

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

Installation

Install the Refactoring Safely 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/AnastasiyaW/codex-claude-code-config/tree/main/skills/development/refactoring-safely
  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/AnastasiyaW/codex-claude-code-config.git /tmp/codex-claude-code-config
mkdir -p .claude/skills
cp -r /tmp/codex-claude-code-config/skills/development/refactoring-safely .claude/skills/refactoring-safely
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Refactoring Safely 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 Refactoring Safely 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 Refactoring Safely 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.

Refactoring safely — changing shape without changing behaviour

Refactoring is a behaviour-preserving transformation. If behaviour changed, that was not a refactor, it was a rewrite with optimistic branding — and the reason large restructures fail is almost always that the two were done in one step.

The rule that makes the rest work

Never restructure and change behaviour in the same commit. Alternate deliberately: refactor (green → green, no behaviour change), then change behaviour (with a new test). When both happen at once, a failing test cannot tell you which half broke it.

Before touching anything: the net

Legacy code is code without tests, regardless of age. So:

  1. Characterization tests first. Not tests of what it should do — tests of what it does, including behaviour you consider wrong. Their job is to detect change, not to judge it. Wrong-but-tested behaviour gets fixed later, on purpose, in its own commit.
  2. Cover the seams you are about to cut, not the whole file. Coverage of the region under the knife is what matters.
  3. Confirm they fail when you break something. A characterization test that passes against deliberately broken code is worse than none — run that check once.
  4. Know how to revert. Small commits, one transformation each.

Smells, and what each one actually indicates

SmellUnderlying problemTransformation
Long functionSeveral jobs in one scopeExtract function, split by level of abstraction
Large moduleSeveral reasons to change in one fileMove features into modules that own their state
Long parameter listA missing object, or a hidden mode flagIntroduce parameter object; split by behaviour
Feature envyMethod uses another object's data more than its ownMove method to where the data lives
Shotgun surgeryOne decision spread across filesGather it into one owner
Divergent changeOne file changing for unrelated reasonsSplit by reason to change
Primitive obsessionA concept represented as a string or dict everywhereIntroduce a type; put its rules with it
Duplicated decisionOne rule expressed in several placesExtract and give it one home

Duplicated text expressing unrelated decisions is not a smell. Merging it couples things that had no reason to change together.

The sequence for a large module

Order matters more than technique, and this order is what keeps each step small:

  1. Constants and pure helpers out first. They have no state and no dependents to break, and they are what every future slice would otherwise import from the old file.
  2. Then the state nobody else touches. Background machinery, caches and locks used by one area move with that area, taking their invariants along.
  3. Then whole vertical slices — one feature's handlers plus the state it owns. A slice that takes its own state with it is a clean cut; a slice that leaves state behind produces "modules" that still need the old file.
  4. The long tail last. Single functions with no clear home go where they are used, or into one honestly-named remainder. Splitting them first is motion without benefit.
  5. Delete an old path only after every identified consumer has migrated and proportionate consumer/integration proof shows the replacement carries the required behavior. For a live externally served boundary, that proof may include traffic/canary evidence; an offline installer, private module, or staged component instead uses its applicable focused contract/integration receipt. Keep the old path until deletion is authorized, needed consumers are proven migrated, and no speculative early removal can erase a rollback or compatibility boundary.

At each step: green before, green after, one commit.

Concurrency: the part that bites

Hand-rolled locks and module-level mutable state do not survive naive moves. Before moving anything guarded:

  • Write down the invariant the lock protects, in words, before the move.
  • Move the state and its lock together, into the module that owns them. State in one module and its lock in another is a bug waiting for load.
  • If two areas share one lock, that is the coupling — resolve it before splitting, not during.

References — load on demand

  • references/refactoring-patterns/smell-catalog.md — the full catalogue with triggers
  • references/refactoring-patterns/refactoring-workflow.md — safe workflow and rollback
  • references/refactoring-patterns/composing-methods.md — extract, inline, replace temp
  • references/refactoring-patterns/moving-features.md — move method/field, hide delegate
  • references/refactoring-patterns/organizing-data.md — replace primitive, encapsulate
  • references/refactoring-patterns/simplifying-conditionals.md — guard clauses, polymorphism
  • *-original.md — the source skill's own framework prose, kept verbatim

Gotchas

  • The big-bang restructure. A branch that reorganises everything cannot be reviewed, cannot be bisected, and conflicts with every parallel session. Small steps on main beat a heroic branch, every time.
  • Refactoring under a shared file. If other sessions or teammates are editing the same file right now, the cut is a coordinated operation, not a background tidy. Check first; conflicts here lose work rather than just time.
  • "Modular" that still imports the old module. If every new module imports the file you split, you moved text, not boundaries. Constants and shared state first — that is what step 1 and 2 are for.
  • Renaming during a move. Move, commit, then rename. A diff that does both is unreviewable and hides accidental behaviour changes.
  • Treating traffic as universal proof. Traffic/canary proof is meaningful for a live served boundary, not an offline or sealed module. Match the consumer or integration receipt to the boundary, while retaining its deletion authority and migration check.

Troubleshooting

SymptomCauseFix
Tests broke and you cannot tell whyBehaviour change smuggled into a structural commitRevert; redo as two commits
Extracted function needs six parametersThe cut is in the wrong placeCut along the data, not along the line count
Circular import after the splitTwo new modules both own part of one conceptExtract the shared concept into a third
Race appears only after the moveState and its lock ended up in different modulesMove them together; re-state the invariant
New offline path has no trafficThe module is not a live served boundaryIdentify its consumers and use the applicable focused contract/integration receipt before authorized deletion

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Refactoring Safely AI skill do?

Change the structure of code that already exists without changing what it does: smells as triggers, the named transformations (extract/inline, move feature, organise data, simplify conditionals), and above all the workflow that makes it safe — characterization tests first, one transformation at a time, green between every step. Use when a file or function is already too large; when asked to "split this module", "extract this", "break up main.py", "clean up this legacy code", "reduce coupling here"; when a shape advisory fires on a grown file; or before any restructuring of code that has use...

Why use Refactoring Safely on TypingMind?

Because you install it once and use it with any model. Refactoring Safely 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 Refactoring Safely in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/AnastasiyaW/codex-claude-code-config/tree/main/skills/development/refactoring-safely. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Refactoring Safely?

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 Refactoring Safely?

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

Is the Refactoring Safely AI skill free?

Yes. It is published on GitHub by AnastasiyaW under the MIT license. 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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