Guide · Agent skill
How to use the Statistical Method Design agent skill
Updated · written with AI from the project's own documentation and checked against our catalogue data
The Statistical Method Design skill is a set of written instructions that tells an AI agent how to plan a statistical method for a research project. Once it is installed, the agent proposes a named method for a clearly defined problem and describes its inputs, assumptions, tuning settings, failure modes and cost. This guide shows you how to install it, how to ask for help in a way that triggers it, and what to check in the output.
Statistical Method Designaiming-lab/AutoResearchClaw · MIT Download the skill Quick start
- 1Download the ZIP. Download the skill ZIP from this page and unzip it so that SKILL.md is available.
- 2Place the skill folder. Put the unzipped folder in ~/.claude/skills/ for all projects, or in the project's .claude/skills/ folder for one project.
- 3Or upload it in the Claude apps. In the Claude apps, go to Settings → Capabilities → Skills and upload the skill.
- 4Formalise your problem. Write the target quantity, your data and your assumptions before asking for a method.
- 5Ask in natural language. Request a method proposal using terms like estimator, baseline, ablation or diagnostic, and mention your claims.
- 6Review the proposal. Check the named method, assumptions, tuning parameters, failure modes and cost against your problem.
What the skill makes the agent do
The skill is a single instruction file, SKILL.md. It is meant to be used after you have formally specified your research problem. The agent then designs a method that responds to that formal target and its assumptions, rather than listing generic techniques.
- Proposes a named method with the problem it solves, its formula or algorithm, and its inputs and outputs.
- Lists tuning parameters, required assumptions, diagnostics, expected failure modes and computational cost.
- Defines baselines such as a classical method, a naive unadjusted method, an oracle reference when available, a robust variant, and an ablation that removes the key design feature.
- Links each method to at least one research claim in a method-to-claim map, with expected evidence and a theory target.
Install the skill from the ZIP download
Download the ZIP for the skill from this page and unzip it. The skill folder contains SKILL.md. You can place it in one of two locations depending on how you want to use it.
For a personal setup available to all your projects, unzip it into ~/.claude/skills/. For a single project, unzip it into the .claude/skills/ folder inside that project.
In the Claude apps, you can instead upload the skill under Settings → Capabilities → Skills.
~/.claude/skills/Trigger the skill with a natural request
You do not need a special command. The skill's description covers designing statistical methods, baselines, diagnostics, variants and ablations for a formal problem formulation. Ask in those terms and mention the problem you have defined.
The skill's metadata lists trigger keywords including method proposal, estimator, algorithm, baseline, ablation, diagnostic and statistical method. Using these words in your request makes it more likely the agent picks up the skill.
- Design an estimator for my formally specified problem, with a naive baseline and two ablations.
- Write a method proposal for this problem statement, including assumptions, failure modes and computational cost.
- Propose diagnostics and a robust variant for my estimator, and map each method to my claims C1 and C2.
Give the agent a formal problem first
The skill expects a formal problem formulation before it designs anything. If you skip this, the output may be generic. Before you ask for a method, write down the target quantity, the data you have, and the assumptions you are willing to make.
Once you have the proposal, check each item against your problem. A method that does not name its assumptions or failure modes is incomplete and should be revised before you rely on it.
Use the method-to-claim map
Every method in the skill's output must connect to at least one claim. The map records which claims the method supports, the evidence you would expect to see, and the theoretical property you are targeting.
method_to_claim_map:
proposed_method:
claims: [C1, C2]
expected_evidence: "lower risk under stress condition"
theory_target: "consistency under assumptions A1-A3"Safety notes: scripts, network and review
The skill's files are SKILL.md only. According to its listing, it runs no scripts and uses no network access, and it lists no required dependencies.
The skill is for designing methods, not running analyses. Do not treat the agent's proposed method as final for high-stakes medical, legal or financial decisions without review by a qualified statistician.
- Runs scripts: no
- Uses network: no
- Requires: nothing listed
Tips for better proposals
Specify the problem formally and include your assumptions. The more concrete the formulation, the more specific the method. Ask for the baselines and ablations explicitly if you want them, since the skill's guidance is to always define meaningful ones.
Treat the proposal as a draft for your methods section. Check the tuning parameters, the failure modes and the claim links against your own understanding before you run anything.
- Name the claims you want to support so the method-to-claim map is useful.
- Ask for diagnostics alongside the method, not as an afterthought.
- Review each proposed assumption and decide whether your data can plausibly satisfy it.
What people use it for
- Drafting the methods section of a statistics or machine learning paper with defined baselines.
- Planning a student analysis that includes a naive baseline and an ablation of the key design feature.
- Producing structured method proposals inside the AutoResearchClaw research pipeline.
- Linking each proposed estimator to the research claims it is meant to support.
Frequently asked questions
How do I install the statistical-method-design skill?
Download the ZIP from this page and unzip it. Place the folder in ~/.claude/skills/ for all projects, or in a project's .claude/skills/ folder for one project. In the Claude apps you can also upload it under Settings → Capabilities → Skills.
Does the statistical-method-design skill require anything else to run?
The skill lists no requirements. It runs no scripts and uses no network access, and its only file is SKILL.md.
Is the statistical-method-design skill free?
The source lists the licence as MIT. Check the repository's licence file for the full terms before reuse.
Can this skill run my statistical tests on my data?
No. The skill is for designing methods, baselines and ablations. Running an existing statistical test on data is outside its scope.
Why does the agent give generic output?
The skill expects a formal problem formulation first. If the problem is not yet specified, the proposal may be generic. Write the target quantity, data and assumptions before asking.
How is this different from asking an agent for a statistical method without a skill?
The skill gives the agent a fixed structure: a named method, assumptions, diagnostics, failure modes, cost, baselines, ablations and a link to claims. Without it, the answer may not follow this format or include the claim map.
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Our verdict
This skill suits researchers drafting methods sections, students planning analyses with proper baselines and ablations, and teams in the AutoResearchClaw pipeline. It is not the right choice if you only need to run an existing test, or if your problem is not yet formally defined. For medical, legal or financial decisions, have a qualified statistician review any proposed method before you act on it.
Download the skill