Control Flow logo

Control Flow

OrganizationPopular
EpicenterHQ
control-flow

Linearize complex control flow with early returns, guard clauses, nested ifs, and natural-language conditions. Use when flattening nested conditions, linearizing try/catch logic, or separating mixed throw-and-return handlers, not for a broad "simplify this" pass over a diff or package, which belongs to collapse-pass.

Overview

PublisherEpicenterHQ
Repositoryepicenter
Skill namecontrol-flow
Stars
4.8K
Forks
386
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 EpicenterHQ on GitHub. Read the source before you install it.

Installation

Install the Control Flow 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/EpicenterHQ/epicenter/tree/main/.agents/skills/control-flow
  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/EpicenterHQ/epicenter.git /tmp/epicenter
mkdir -p .claude/skills
cp -r /tmp/epicenter/.agents/skills/control-flow .claude/skills/control-flow
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Control Flow 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 Control Flow 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 Control Flow 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.

Human-Readable Control Flow

When refactoring complex control flow, mirror natural human reasoning patterns:

Related Skills: See refactoring methods for branch collapse and caller evidence.

Core Pattern

  1. Ask the human question first: "Can I use what I already have?" -> early return for happy path
  2. Assess the situation: "What's my current state and what do I need to do?" -> clear, mutually exclusive conditions
  3. Take action: "Get what I need" -> consolidated logic at the end
  4. Use natural language variables: isUsingNavigator, isUsingLocalTranscription, needsOldFileCleanup: names that read like thoughts
  5. Avoid artificial constructs: No nested conditions that don't match how humans actually think through problems

Transform this: nested conditionals with duplicated logic Into this: linear flow that mirrors human decision-making

Example: Early Returns with Natural Language Variables

typescript
// From apps/whispering/src/routes/(app)/_layout-utils/check-ffmpeg.ts

export async function checkFfmpegRecordingMethodCompatibility() {
	if (!window.__TAURI_INTERNALS__) return;

	// Only check if FFmpeg recording method is selected
	if (settings.value['recording.method'] !== 'ffmpeg') return;

	const { data: ffmpegInstalled } =
		await rpc.ffmpeg.checkFfmpegInstalled.ensure();
	if (ffmpegInstalled) return; // FFmpeg is installed, all good

	// FFmpeg recording method selected but not installed
	toast.warning('FFmpeg Required for FFmpeg Recording Method', {
		// ... toast content
	});
}

Example: Natural Language Booleans

typescript
// From apps/whispering/src/routes/(app)/_layout-utils/check-ffmpeg.ts

const isUsingNavigator = settings.value['recording.method'] === 'navigator';
const isUsingLocalTranscription =
	settings.value['transcription.selectedTranscriptionService'] ===
		'whispercpp' ||
	settings.value['transcription.selectedTranscriptionService'] === 'parakeet';

return isUsingNavigator && isUsingLocalTranscription && !isFFmpegInstalled;

Example: Cleanup Check with Comment

typescript
// From packages/epicenter/src/indexes/markdown/markdown-index.ts

/**
 * This is checking if there's an old filename AND if it's different
 * from the new one. It's essentially checking: "has the filename
 * changed?" and "do we need to clean up the old file?"
 */
const needsOldFileCleanup = oldFilename && oldFilename !== filename;
if (needsOldFileCleanup) {
	const oldFilePath = path.join(tableConfig.directory, oldFilename);
	await deleteMarkdownFile({ filePath: oldFilePath });
	tracking[table.name]!.deleteByFilename({ filename: oldFilename });
}

Example: Linearizing try-catch into Guard + Happy Path

try-catch blocks create a nested, two-branch structure: the try body and the catch body. When only one call inside the try can actually throw, replace the try-catch with a guarded call + early return so the code reads top-to-bottom.

Before (nested, mixed throw/return):

typescript
async ({ body, status }) => {
	const adapter = createAdapter(body.provider);

	try {
		const stream = chat({ adapter, messages: body.messages });
		return toServerSentEventsResponse(stream);
	} catch (error) {
		if (error instanceof Error && error.name === 'AbortError') {
			throw status(499, 'Client closed request');
		}
		const message = error instanceof Error ? error.message : 'Unknown error';
		throw status('Bad Gateway', `Provider error: ${message}`);
	}
};

After (linear, consistent returns):

typescript
async ({ body, status }) => {
	const adapter = createAdapter(body.provider);

	const { data: stream, error: chatError } = trySync({
		try: () => chat({ adapter, messages: body.messages }),
		catch: (e) => Err(e instanceof Error ? e : new Error(String(e))),
	});

	if (chatError) {
		if (chatError.name === 'AbortError') {
			return status(499, 'Client closed request');
		}
		return status('Bad Gateway', `Provider error: ${chatError.message}`);
	}

	return toServerSentEventsResponse(stream);
};

The transformation follows the same human reasoning pattern:

  1. Try the risky thing: wrap only what can fail
  2. Check if it failed: early return with the appropriate error
  3. Continue with the happy path: the rest of the function assumes success

This eliminates the nesting, makes return vs throw consistent, and separates the error boundary from the safe code that follows it.

Example: Sequential Guards in a Handler

When a handler has multiple failure points, each guard follows the same pattern: do the thing, check the result, return early or continue.

typescript
async ({ body, status }) => {
	// Guard 1: validate input
	if (!isSupportedProvider(body.provider)) {
		return status('Bad Request', `Unsupported provider: ${body.provider}`);
	}

	// Guard 2: resolve dependency
	const apiKey = resolveApiKey(body.provider, headers['x-api-key']);
	if (!apiKey) {
		return status('Unauthorized', 'Missing API key');
	}

	// Guard 3: risky operation
	const { data: stream, error } = trySync({
		try: () => chat({ adapter: createAdapter(body.provider, apiKey) }),
		catch: (e) => Err(e instanceof Error ? e : new Error(String(e))),
	});
	if (error) return status('Bad Gateway', error.message);

	// Happy path: all guards passed
	return toServerSentEventsResponse(stream);
};

Every guard has the same shape: check → return early on failure. The happy path accumulates at the bottom. Reading top-to-bottom, you see every way the function can fail before you see the success case.

Frequently asked questions

What does the Control Flow AI skill do?

Linearize complex control flow with early returns, guard clauses, nested ifs, and natural-language conditions. Use when flattening nested conditions, linearizing try/catch logic, or separating mixed throw-and-return handlers, not for a broad "simplify this" pass over a diff or package, which belongs to collapse-pass.

Why use Control Flow on TypingMind?

Because you install it once and use it with any model. Control Flow 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 Control Flow in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/EpicenterHQ/epicenter/tree/main/.agents/skills/control-flow. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Control Flow?

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 Control Flow?

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

Is the Control Flow AI skill free?

It is published on GitHub by EpicenterHQ. 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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