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Fanout Analysis

OrganizationPopular
trpc-group
fanout-analysis

Use this workflow recipe when a question benefits from several independent perspectives followed by a single evidence-based synthesis.

Overview

Publishertrpc-group
Repositorytrpc-agent-go
Skill namefanout-analysis
Stars
1.8K
Forks
309
Bundled files
Instructions only
LicenseApache-2.0
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 trpc-group on GitHub. Read the source before you install it.

Installation

Install the Fanout Analysis 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/trpc-group/trpc-agent-go.git /tmp/trpc-agent-go
mkdir -p .claude/skills
cp -r /tmp/trpc-agent-go/examples/dynamicworkflow/skills/skills/fanout-analysis .claude/skills/fanout-analysis
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Fanout Analysis 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 Fanout Analysis 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 Fanout Analysis 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.

Parallel Analysis and Synthesis

Turn the user's request into a temporary fan-out/fan-in workflow. Keep the number and focus of branches appropriate to the request; do not create roles just to make the workflow look larger.

Process

  1. Extract the decision or question, the relevant constraints, and the output format from the user's request.
  2. Choose two to four independent analysis angles that cover different evidence or reasoning needs. Give every branch the same core question and only the context it needs. Do not let one branch depend on another branch's unfinished answer.
  3. Run those branches in parallel. Each branch should return concise findings, assumptions, and unresolved uncertainty rather than a polished final answer.
  4. Pass the ordered branch results, the original request, and the explicit decision criteria to a separate synthesis role. The synthesizer must distinguish agreement, disagreement, and missing evidence; it must not silently turn an unsupported claim into a fact.
  5. If the request requires a decision, have the synthesizer return a small structured object with recommendation, reasons, and uncertainties. Keep branch content as text unless a later control-flow decision genuinely needs typed fields.
  6. Return the synthesis and the key evidence trail. If a branch fails, keep that missing evidence explicit and follow the application's bounded failure policy; do not silently treat it as support or retry indefinitely.

Compilation Rules

  • Express independent branches with parallel([...]); preserve the input order when passing results to the synthesizer.
  • Use separate workflow-local Agent instances for each branch and for the synthesizer. Do not ask the synthesizer to redo every branch from memory.
  • Pass the original question, constraints, and branch outputs explicitly as inputs. A later stage must not depend on context that was only present in a previous Agent's prompt.
  • parallel returns None for a failed independent branch. Handle that value explicitly, and let the workflow or its caller decide whether a single, bounded rerun is appropriate; do not rely on exception-catching syntax or unbounded retries.
  • Keep the glue code small: create roles, pass JSON-compatible values, fan out, fan in, and return the result. Delegate substantive analysis to Agents.
  • Use tools=[] for roles that only reason over supplied inputs. Select a declared tool only for a branch whose task genuinely needs it, and keep mutating tools out of parallel branches unless their independence is clear.
  • If a structured synthesis is requested, read the Agent result's explicit structured object. Do not ask for JSON-looking text and parse it in the workflow.

Frequently asked questions

What does the Fanout Analysis AI skill do?

Use this workflow recipe when a question benefits from several independent perspectives followed by a single evidence-based synthesis.

Why use Fanout Analysis on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/trpc-group/trpc-agent-go/tree/main/examples/dynamicworkflow/skills/skills/fanout-analysis. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Fanout Analysis?

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 Fanout Analysis?

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

Is the Fanout Analysis AI skill free?

Yes. It is published on GitHub by trpc-group under the Apache-2.0 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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