Subagent Fanout logo

Subagent Fanout

Community
adamentwistle
subagent-fanout

Delegate breadth — multi-file searches, independent investigations, noisy command output — to subagents with precise briefs, then verify their claims before acting on them. Use when locating an answer means reading more than ~4 files, when several unrelated investigations could run in parallel, or when raw output would flood the context needed for synthesis and edits. Not for a single known file or symbol lookup.

Overview

Publisheradamentwistle
Repositoryfable-skills
Skill namesubagent-fanout
Stars
98
Forks
11
Bundled files
Instructions only
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.

  • Self-contained

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

  • Open source

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

Installation

Install the Subagent Fanout 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/adamentwistle/fable-skills/tree/main/subagent-fanout
  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/adamentwistle/fable-skills.git /tmp/fable-skills
mkdir -p .claude/skills
cp -r /tmp/fable-skills/subagent-fanout .claude/skills/subagent-fanout
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Subagent Fanout 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 Subagent Fanout 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 Subagent Fanout 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.

subagent-fanout

Weak delegation looks like: reading twelve files inline to answer one "where is X handled" question, or sending an agent "look into auth" and getting back an essay you can't act on. Strong delegation spends subagent context on search and keeps main context for synthesis and edits.

When to fan out

  1. Breadth, not depth: "where is auth handled", "find every caller of X", "which configs mention Y". If locating the answer would mean reading more than ~4 files, delegate.
  2. Independent investigations in parallel: three unrelated questions = three agents launched in one message, not a sequence.
  3. Noisy output: a test run, log grep, or build whose 2,000 lines you'd skim for three — have the agent run it and return the three.

When not to

  1. The single lookup. If you know the file or symbol, one Grep/Read beats an agent round-trip.
  2. Work that needs your context: judgment calls that depend on what the user said, or edits to code you've already reasoned about. Delegate finding; keep deciding.
  3. Already delegated. Once an agent is searching, don't run the same search inline while waiting.

The brief

A subagent has none of your context. Every brief states:

  • Goal — the one question, phrased so it has a definite answer.
  • Scope — directories or globs to search, and what to skip (generated code, vendored deps, tests unless asked).
  • Constraints — read-only unless told otherwise: no edits, commits, or installs.
  • Return format — e.g. "list of file:line + one-line role each, then a two-sentence conclusion; say 'not found' rather than guessing". Cap the length.
  • What you already know, so it doesn't rediscover it.

If you can't write the return format, you don't yet know what you're asking.

After it returns

  1. Verify at the point of use. Before editing based on an agent's claim about a signature, call site, or behavior, Read the cited lines yourself (see workmanship). Agents can invent paths and line numbers; a cited file:line that doesn't exist invalidates that finding, not the whole report.
  2. Keep the conclusion, drop the transcript. Record what you carry forward in one or two lines; don't paste the agent's full output into your reasoning.
  3. Attribute in the report. A finding that came from a subagent and wasn't independently confirmed is labelled as such.

Frequently asked questions

What does the Subagent Fanout AI skill do?

Delegate breadth — multi-file searches, independent investigations, noisy command output — to subagents with precise briefs, then verify their claims before acting on them. Use when locating an answer means reading more than ~4 files, when several unrelated investigations could run in parallel, or when raw output would flood the context needed for synthesis and edits. Not for a single known file or symbol lookup.

Why use Subagent Fanout on TypingMind?

Because you install it once and use it with any model. Subagent Fanout 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 Subagent Fanout in TypingMind?

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

Which AI models can use Subagent Fanout?

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 Subagent Fanout?

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

Is the Subagent Fanout AI skill free?

Yes. It is published on GitHub by adamentwistle 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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