Statistical Method Design logo

Statistical Method Design

OrganizationPopular
aiming-lab
statistical-method-design

Design statistical methods, baselines, diagnostics, variants, and ablations that directly address a formal problem formulation.

Overview

Publisheraiming-lab
RepositoryAutoResearchClaw
Skill namestatistical-method-design
Stars
14.4K
Forks
1.7K
Bundled files
Instructions only
LicenseMIT
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 aiming-lab on GitHub. Read the source before you install it.

Installation

Install the Statistical Method Design 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/aiming-lab/AutoResearchClaw.git /tmp/AutoResearchClaw
mkdir -p .claude/skills
cp -r /tmp/AutoResearchClaw/external/agents/stat_research_agent/skills/statistical-method-design .claude/skills/statistical-method-design
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Statistical Method Design 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 Method Design 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 Method Design 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 Method Design

Overview

Use this skill after formal problem formulation. The method should be a response to the formal target and assumptions, not a generic collection of techniques.

Required Method Proposal

For each method:

  • Name
  • Problem it solves
  • Formula or algorithm
  • Inputs and outputs
  • Tuning parameters
  • Required assumptions
  • Diagnostics
  • Expected failure modes
  • Computational cost
  • Relation to baselines

Baselines and Ablations

Always define meaningful baselines:

  • Classical or standard method
  • Naive or unadjusted method
  • Oracle or idealized reference when available
  • Robust variant
  • Ablation removing the key design feature

Method-to-Claim Map

Every method must connect to at least one claim:

yaml
method_to_claim_map:
  proposed_method:
    claims: [C1, C2]
    expected_evidence: "lower risk under stress condition"
    theory_target: "consistency under assumptions A1-A3"

Frequently asked questions

What does the Statistical Method Design AI skill do?

Design statistical methods, baselines, diagnostics, variants, and ablations that directly address a formal problem formulation.

Why use Statistical Method Design on TypingMind?

Because you install it once and use it with any model. Statistical Method Design 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 Method Design 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-method-design. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Statistical Method Design?

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 Method Design?

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

Is the Statistical Method Design 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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