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Decompose Research Goal

Organization
yogsoth-ai
decompose-research-goal

Turn a research direction into an AND/OR goal DAG with testable leaves, dependency structure, and feasibility annotations.

Overview

Publisheryogsoth-ai
Repositoryde-anthropocentric-research-engine
Skill namedecompose-research-goal
Stars
505
Forks
43
Bundled files
Instructions only
LicenseApache-2.0
Links
Skill sourceRepository
  • 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 Goal 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/yogsoth-ai/de-anthropocentric-research-engine/tree/main/skills/decompose-research-goal
  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/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-goal .claude/skills/decompose-research-goal
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Decompose Research Goal 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 Goal 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 Goal 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-goal

Purpose

Turn a research direction into an AND/OR goal DAG with testable leaves, dependencies, and feasibility annotations.

Input contract

yaml
required: [research_direction, constraints]
optional: [actor_profile, obstacle_report, timeline, north_star]
constraints: [goal wording, constraints, and source context must be explicit]

Execution protocol

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

  1. You MUST load skill formulate-top-goal to formalize the top goal and desired effect.
  2. You MUST load skill decompose-and-or-goal to recursively construct AND/OR branches and executable leaves.
  3. You MUST load skill validate-goal-tree to check leaf specificity, testability, coverage, and dependency consistency.
  4. You MUST load skill assess-goal-feasibility to annotate feasibility and alternatives.
  5. You MUST load skill map-dependencies to map the goal dependencies. You MUST load skill crystallize-north-star to compress the result into a North Star.

Deviation: Omit feasibility annotations when no actor, resource, or timeline constraints are supplied; mark the tree provisional rather than inventing constraints. Re-run only the affected branch after a decision changes its parent goal.

Output contract

yaml
produces: [goal_tree_dag, executable_leaves, dependency_map, feasibility_annotations, research_north_star]
delta_fields: [findings, hypothesis_updates, decisions, uncertainties, open_questions]

Thresholds and quality gates

  • Every leaf must be testable or executable and trace to a parent goal.
  • AND/OR semantics must be explicit; unresolved feasibility remains an uncertainty.
  • Completion requires coverage of the declared top goal, not a fixed leaf count.

Failure and counterexamples

Reject trees with circular dependencies, vague leaves, or branches that cannot be tied to the declared direction. Do not treat a polished sentence as evidence that decomposition is complete.

Provenance map

  • resolved: goal-decomposition
  • resolved: north-star-crystallization-formulate-top-goal
  • resolved: north-star-crystallization-and-or-decompose
  • resolved: north-star-crystallization-validate-leaves
  • resolved: north-star-crystallization-feasibility-check
  • resolved: north-star-crystallization-ask-decomposition-validation

Preserved source criteria ledger

sourcesource linekindsource criterion
none retained--No source numeric/textual criterion retained after normalization.

Context checkpoint / Delta notes

Append the goal DAG, leaf validation, feasibility annotations, dependency changes, and unresolved validation questions.

Frequently asked questions

What does the Decompose Research Goal AI skill do?

Turn a research direction into an AND/OR goal DAG with testable leaves, dependency structure, and feasibility annotations.

Why use Decompose Research Goal on TypingMind?

Because you install it once and use it with any model. Decompose Research Goal 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 Goal 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-goal. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Decompose Research Goal?

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 Goal?

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

Is the Decompose Research Goal 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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