Decompose Research Question

Organization

yogsoth-ai

decompose-research-question

Decompose a complex RQ into MECE subquestions, map dependencies, and schedule the answering order.

Overview

Publisher	yogsoth-ai
Repository	`de-anthropocentric-research-engine`
Skill name	`decompose-research-question`
Stars	505
Forks	43
Bundled files	Instructions only
License	Apache-2.0
Links	Skill source Repository

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 yogsoth-ai on GitHub. Read the source before you install it.

Installation

Install the Decompose Research Question AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/yogsoth-ai/de-anthropocentric-research-engine/tree/main/skills/decompose-research-question
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/yogsoth-ai/de-anthropocentric-research-engine/tree/main/skills/decompose-research-question

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

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/yogsoth-ai/de-anthropocentric-research-engine.git /tmp/de-anthropocentric-research-engine
mkdir -p .claude/skills
cp -r /tmp/de-anthropocentric-research-engine/skills/decompose-research-question .claude/skills/decompose-research-question

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Decompose Research Question 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 Decompose Research Question 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 Decompose Research Question 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.

decompose-research-question

Purpose

Decompose a complex RQ into MECE subquestions, map dependencies, and schedule the answering order.

Input contract

yaml
required: [research_question, scope_constraints, dependency_evidence]
optional: [assumptions, prior_findings, evidence_updates]
constraints: [consume named scientific objects; preserve provenance; keep unresolved uncertainty visible]

Execution protocol

Do not perform called SOP operations inline; each loaded SOP owns its contract and thresholds.

You MUST load skill generate-subquestions to generate answerable subquestions.
You MUST load skill map-dependencies to map their evidence and logical dependencies.
You MUST load skill sequence-work to sequence the dependent research work.

Deviation: reorder only when a dependency is already satisfied or unavailable; record the reason and confidence effect.

Output contract

yaml
produces: [subquestion_set, dependency_map, answering_sequence]
delta_fields: [open_questions]

Thresholds and quality gates

Each output is traceable to an input object, operation, and evidence reference.
Scope, assumptions, and unresolved alternatives remain explicit.
Retain $\alpha$ 0.05 and power 0.8 wherever the predeclared statistical design requires them.

Failure and counterexamples

Stop synthesis when a required object is absent, a precondition is violated, or a counterexample invalidates the proposed conclusion; return the partial delta with the failure recorded.

Provenance map

resolved: decomposition-formulation
resolved: sub-question-decomposition

Preserved source criteria ledger

source	criterion	treatment
resolved v3 entries above	node-specific criteria	retained and specialized to the v4 object contract
experiment-execution/statistical-testing	$\alpha$ = 0.05	fixed value retained where applicable
experiment-execution/sample-size-estimation	power = 0.8	fixed value retained where applicable

Context checkpoint / Delta notes

Return the node-specific research-state delta and preserve findings, evidence updates, uncertainties, decisions, open questions, and recommended jumps as applicable.

Frequently asked questions

What does the Decompose Research Question AI skill do?

Decompose a complex RQ into MECE subquestions, map dependencies, and schedule the answering order.

Why use Decompose Research Question on TypingMind?

Because you install it once and use it with any model. Decompose Research Question 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 Decompose Research Question in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/yogsoth-ai/de-anthropocentric-research-engine/tree/main/skills/decompose-research-question. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Decompose Research Question?

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 Decompose Research Question?

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

Is the Decompose Research Question AI skill free?

Yes. It is published on GitHub by yogsoth-ai 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

decompose-research-question

Purpose

Input contract

Execution protocol

Output contract

Thresholds and quality gates

Failure and counterexamples

Provenance map

Preserved source criteria ledger

Context checkpoint / Delta notes

Frequently asked questions

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