Statistical Problem Formulation

OrganizationPopular

aiming-lab

statistical-problem-formulation

Formulate statistical research problems with formal notation, target parameters, assumptions, hypotheses, evaluation criteria, and theory targets.

Overview

Publisher	aiming-lab
Repository	`AutoResearchClaw`
Skill name	`statistical-problem-formulation`
Stars	14.4K
Forks	1.7K
Bundled files	Instructions only
License	MIT
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 aiming-lab on GitHub. Read the source before you install it.

Installation

Install the Statistical Problem Formulation 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.
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/aiming-lab/AutoResearchClaw/tree/main/external/agents/stat_research_agent/skills/statistical-problem-formulation

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/aiming-lab/AutoResearchClaw.git /tmp/AutoResearchClaw
mkdir -p .claude/skills
cp -r /tmp/AutoResearchClaw/external/agents/stat_research_agent/skills/statistical-problem-formulation .claude/skills/statistical-problem-formulation

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

Use it in TypingMind

Enable Statistical Problem Formulation 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 Statistical Problem Formulation 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 Statistical Problem Formulation 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.

Statistical Problem Formulation

Overview

Use this skill before any method design, theory, experiment, or report writing. The goal is to transform a broad topic into a precise statistical problem.

Required Formulation Elements

Element	Questions
Observed data	What is observed? What is the sample size? Are samples iid, dependent, clustered, censored, or selected?
Data model	What family of distributions or data-generating processes is considered?
Target	What parameter, decision, prediction, or risk is the object of study?
Assumptions	What must hold for the target to be identifiable or the method to work?
Hypotheses	What claims should be supported, refuted, or made inconclusive?
Criteria	What metrics define success or failure?
Theory target	What property should be derived: bias, variance, consistency, rate, coverage, error bound, robustness, or impossibility?

Handoff Schema

The problem formulation should be precise enough to support this structured handoff:

yaml
topic_id: TXX
title: ""
research_question: ""
observed_data:
  notation: ""
  sampling: iid | dependent | clustered | time_series | selected | unknown
data_model:
  notation: ""
  family: ""
target:
  name: ""
  notation: ""
  type: estimand | decision | prediction | risk | descriptive_quantity
  truth_source: analytic | simulation | oracle | empirical_reference | not_applicable
assumptions:
  structural: []
  sampling: []
  regularity: []
  identifiability: []
claims:
  - id: C1
    statement: ""
    formal_statement: ""
evaluation_criteria:
  - name: ""
    direction: ""
theory_targets:
  - identifiability
  - bias
  - consistency
blocking_ambiguities: []

Template

markdown
# Problem Formulation

## Research Question
...

## Observed Data
Let ...

## Data-Generating Model
Assume ...

## Target / Estimand
Define ...

## Candidate Procedure Class
We consider procedures ...

## Assumptions
1. ...

## Claims / Hypotheses
- ...

## Evaluation Criteria
- ...

## Theoretical Questions
- ...

## Experimental Questions
- ...

Quality Bar

A formulation passes only if another researcher could implement or analyze the problem without guessing the target, assumptions, or success criteria.

Frequently asked questions

What does the Statistical Problem Formulation AI skill do?

Formulate statistical research problems with formal notation, target parameters, assumptions, hypotheses, evaluation criteria, and theory targets.

Why use Statistical Problem Formulation on TypingMind?

Because you install it once and use it with any model. Statistical Problem Formulation 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 Statistical Problem Formulation in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/aiming-lab/AutoResearchClaw/tree/main/external/agents/stat_research_agent/skills/statistical-problem-formulation. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Statistical Problem Formulation?

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 Statistical Problem Formulation?

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

Is the Statistical Problem Formulation AI skill free?

Yes. It is published on GitHub by aiming-lab 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Statistical Problem Formulation

Overview

Required Formulation Elements

Handoff Schema

Template

Quality Bar

Frequently asked questions

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