Codebase Simplification Campaign logo

Codebase Simplification Campaign

Community
Sahil-SS9
codebase-simplification-campaign

Use when refactoring a module or codebase while preserving behaviour.

Overview

PublisherSahil-SS9
Repositoryagent-personas
Skill namecodebase-simplification-campaign
Stars
67
Forks
4
Bundled files
4
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.

  • 4 bundled files

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

  • Open source

    Published by Sahil-SS9 on GitHub. Read the source before you install it.

Installation

Install the Codebase Simplification Campaign 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/Sahil-SS9/agent-personas/tree/main/skills/codebase-simplification-campaign
  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/Sahil-SS9/agent-personas.git /tmp/agent-personas
mkdir -p .claude/skills
cp -r /tmp/agent-personas/skills/codebase-simplification-campaign .claude/skills/codebase-simplification-campaign
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Codebase Simplification Campaign 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 Codebase Simplification Campaign 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 Codebase Simplification Campaign 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.

Codebase Simplification Campaign

Operational method; evidence is limited to the scenarios actually exercised.

Preparation gate

Applies to module-level refactors as well as campaigns. BEFORE the first structural source edit, follow refactor preparation: inspect and execute relevant coverage; add and run missing boundary characterization against unchanged source. A smoke pass alone does not cover absent edge assertions. Reuse adequate existing coverage without duplicate tests. For structural-plus-feature work, verify a behaviour-preserving structural slice before adding the feature; separate edits/test receipts suffice, not mandatory separate commits. If a small test seam is necessary first, disclose and verify that prerequisite instead of pretending the source was untouched. For trivial local work, use the short path in the guide; the repository-wide LOC census and campaign machinery below apply only when a repository-wide campaign or numerical reduction is requested.

  1. Freeze baseline revision, tracked file universe, language-aware LOC tool/version and exclusions before changes. Report overall repository and production-source counts separately, with tests/docs/generated/vendor categories. Include new and moved files consistently.

  2. A requested 30% reduction is an aggressive target, not a licence to remove features, tests, security guards, observability or compatibility. Never minify, reclassify or move code outside the denominator to claim success. Report target shortfalls honestly.

  3. Map entrypoints, dependencies, high-churn modules, large files and duplicate behaviours. Read callers and history before declaring code dead; reflection, registration and import side effects can evade text search.

  4. First add characterisation tests at observable boundaries, including error paths, ordering and side effects. Keep existing defects distinguishable from intended fixes; behaviour-changing repairs are separate scope, not disguised refactoring.

  5. Rank seams by measured duplication, change coupling and verification cost. Break god files along coherent responsibilities and ownership, not line thresholds. Consolidate helpers only when semantics and reasons for change match; keep accidental similarity separate.

  6. Flatten routing only after recording precedence, fallthrough, defaults and failure semantics. Compare guard clauses, explicit state transitions and table dispatch; reject clever indirection that makes the caller harder to understand.

  7. Apply small reversible slices; after each slice run focused and integration tests. Use simplify-swarm-candidate for local improvements and hermaguard-candidate for adversarial verification. Reviewer agreement never lowers change risk.

  8. Prefer independently green stacked PRs. When changing published interfaces or independently deployed consumers, apply expand/migrate/contract gates, including mixed-state and retirement evidence. Use one atomic PR only where splitting creates broken intermediate states. Continue routine approved choices without asking, but do not invent missing authority or conceal blockers.

  9. Report baseline/current counts, reduction, duplication/coupling changes, remaining hotspots, tests and actual PR handles if authorised. Only undo owned changes; preserve unrelated dirty work. A smaller tree is not enough: prove unchanged contracts and explain representative execution paths.

Provenance and limits

Read source synthesis when assessing origin, conflicting guidance or evidence maturity.

Source-grounded decision rules

Separate mechanism, specialised action and policy when they have different change reasons. A god-file split is successful only if callers need less knowledge and dependencies decrease; never extract solely to meet a function-length threshold (Ousterhout, chapters 6 and section 9.8).

Reduce special cases through representations only after proving distinctions are irrelevant: empty and absent are not interchangeable when callers observe them. Compare explicit branches, data dispatch and polymorphism; preserve unknown-type defaults and error semantics (Ousterhout chapter 6; Fowler catalogue).

Migration ledger: enumerate every consumer, compatibility state, owner, verification and retirement condition. Use independently green shards, prevent new old-API uses, and do not call the migration finished while consumers or temporary adapters remain (Software Engineering at Google, chapter 22).

Branch by abstraction: place current behaviour behind a narrow boundary, migrate callers, introduce and compare the replacement, switch only with authority, then retire old implementation and temporary boundary when no longer needed. Test mixed-version states; do not shadow-write side effects (Fowler, Branch By Abstraction; side-effect prohibition is our safety synthesis).

Freeze the dependency index revision and confirm runtime registration, configuration and dynamic callers separately. Treat graph absence as an investigation lead, never proof of dead code (wiki codeindex plus upstream README).

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 Codebase Simplification Campaign AI skill do?

Use when refactoring a module or codebase while preserving behaviour.

Why use Codebase Simplification Campaign on TypingMind?

Because you install it once and use it with any model. Codebase Simplification Campaign 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 Codebase Simplification Campaign in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Sahil-SS9/agent-personas/tree/main/skills/codebase-simplification-campaign. 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 Codebase Simplification Campaign?

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 Codebase Simplification Campaign?

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

Is the Codebase Simplification Campaign AI skill free?

Yes. It is published on GitHub by Sahil-SS9 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇