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Ce Simplify Code

OrganizationPopular
EveryInc
ce-simplify-code

Simplify settled, recently changed code for clarity, reuse, quality, and efficiency while preserving behavior. Use after implementation and before review; use ce-debug for bugs.

Overview

PublisherEveryInc
Repositorycompound-engineering-plugin
Skill namece-simplify-code
Stars
25.1K
Forks
2.1K
Bundled files
3
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.

  • 3 bundled files

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

  • Open source

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

Installation

Install the Ce Simplify Code 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.
  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/EveryInc/compound-engineering-plugin.git /tmp/compound-engineering-plugin
mkdir -p .claude/skills
cp -r /tmp/compound-engineering-plugin/skills/ce-simplify-code .claude/skills/ce-simplify-code
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ce Simplify Code 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 Ce Simplify Code 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 Ce Simplify Code 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.

Simplify recently changed code for clarity, reuse, quality, and efficiency while preserving exact behavior. Prioritize readable, explicit code over compact code — fewer lines is not the goal.

Step 1: Identify scope

Resolve the simplification scope in this order:

  1. User-named scope is authoritative; do not widen it.
  2. Otherwise, in git, use the current branch versus its base. Without a usable base, use staged and unstaged changes (git diff HEAD).
  3. Outside git or without a diff, use files the user named or that were edited earlier in the conversation.

If none of the above produces a non-empty scope, stop and ask the user what to simplify rather than guessing. Use the host's blocking question tool already in the current tool list (match by capability, not by a host-specific name). Presence in the current tool list is proof the tool exists; never call a user-facing question tool to discover whether it exists. If a matching tool is listed but unloaded, use the host's tool-discovery primitive to load that capability — do not search for another host's tool name. Fall back to numbered options on the host's user-visible chat surface only when no such tool is in the list or a real question call errors. Never silently skip the question.

Preflight. If the scope has no substantive human-authored code — only documentation, generated or vendored files, dependencies or lockfiles, or mechanical churn — report that there is nothing to simplify and stop without reviewers. For mixed scopes, retain only the code. This check is about the kind of change, never its size: explicit small scopes still run, and any size or cost threshold is the caller's to set.

When the platform's task-tracking capability is available, show the review, apply, and verification outcomes without creating one task per reviewer. Otherwise continue without simulating a task list in chat.

Step 2: Launch 3 review agents in parallel

Dispatch three generic subagents — code-reuse, code-quality, and efficiency reviewers — via the platform's subagent primitive (Agent/Task in Claude Code, spawn_agent in Codex) where available; otherwise run the reviews inline or serially. For each reviewer, read its prompt asset from this skill's directory and pass the full file content as the subagent's prompt, together with the resolved scope (the full diff or file set) so it has complete context:

  • references/personas/code-reuse-reviewer.md
  • references/personas/code-quality-reviewer.md
  • references/personas/efficiency-reviewer.md

Do not paraphrase these rubrics from memory. Read each file and pass it verbatim, or the reviewer loses the rules that keep the pass behavior-preserving.

Bounded dispatch. Queue the three reviewers and launch only as many as the harness accepts at once. A concurrency or active-agent-limit error means the harness is full, not that the reviewer failed: leave the reviewer queued and retry after a slot frees. When a dispatch cannot recover through active work, supported release, or a corrected invocation, run that pass inline using the same prompt asset and disclose the substitution.

Agent lifecycle. Collect terminal outcomes, including failures, before cleanup. Close or release review-owned agents when the harness provides caller-owned cleanup, before refilling slots, advancing stages, or returning. Do not message completed agents with no remaining work. Do not infer released capacity from completion or interruption, or invent cleanup operations.

Model selection. Use the platform's balanced mid-tier model for these reviewers when the current harness exposes a known override. In Claude Code this is the Sonnet class. In Codex, apply this tier only when the active dispatch primitive exposes an explicit model or custom-agent selector; task wording alone does not select a different model. Otherwise omit the override and inherit the parent model -- a working pass on the parent model beats a broken dispatch.

Permission mode. Omit the mode parameter on the dispatch call so the user's configured permission settings apply.

Step 3: Fix issues

Proceed only after all three review outcomes are complete, whether returned by subagents or produced inline. Apply worthwhile findings directly; record false positives and low-value findings as skipped without asking the user.

Inspect beyond the resolved scope when needed to evaluate a finding, but edit only that scope and the import/export lines it needs. For a user-named file or directory scope, those import/export lines must also be inside it; skip any fix that would edit outside the mutation boundary.

Each fix must preserve outputs, errors, side effects, and ordering. If that cannot be established, skip it.

An interface or data shape that existed only in an earlier iteration of the current unshipped scope is not protected behavior once you verify it has no deployed, persisted, public, external, dependent-branch, or in-repo caller outside the resolved scope. Remove that compatibility path only when every required caller update fits the existing mutation boundary; otherwise preserve it.

Never simplify away a safety check. Preserve trust-boundary validation, data-loss protection, security checks, and accessibility affordances. Skip any finding that would thin or remove one.

Honor caller-passed structure pins. A plan path passed with the structure-pin constraint is context, not scope. Preserve its session-settled: Key Technical Decisions, including deliberate duplication or separation.

Step 4: Verify behavior is preserved

Run project-wide typecheck and lint. Run tests matched to blast radius: scoped tests for local changes, broader tests for shared or wide-reach changes, and the full suite when the runner cannot scope tests.

Report failures with the check name and relevant output. Fix simplification-caused failures or revert the responsible change; never relax assertions, weaken types, or skip tests.

If no test suite, lint, or typecheck is configured, state that explicitly in the summary; do not silently skip verification.

Step 5: Summarize

Summarize what was already sound and what improved. Report applied counts by reuse, quality, and efficiency; skipped count; and check outcomes. If nothing changed, say so. Do not use net lines removed as the success metric.

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 Ce Simplify Code AI skill do?

Simplify settled, recently changed code for clarity, reuse, quality, and efficiency while preserving behavior. Use after implementation and before review; use ce-debug for bugs.

Why use Ce Simplify Code on TypingMind?

Because you install it once and use it with any model. Ce Simplify Code 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 Ce Simplify Code in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/EveryInc/compound-engineering-plugin/tree/main/skills/ce-simplify-code. 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 Ce Simplify Code?

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 Ce Simplify Code?

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

Is the Ce Simplify Code AI skill free?

Yes. It is published on GitHub by EveryInc 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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