Guide · Agent skill
How to use the Statistical Theory Analysis agent skill
Updated · written with AI from the project's own documentation and checked against our catalogue data
The Statistical Theory Analysis skill is a set of instructions that tells an AI agent to reason about a statistical method in a formal, mathematical way. Instead of running code on a dataset, the agent looks at properties such as identifiability, bias and variance, consistency, asymptotic behaviour, coverage and robustness, then writes a structured result with assumptions, a proof sketch, an interpretation and limitations. This guide is for researchers, students and paper authors who want that kind of theoretical analysis, and it explains how to install the skill, trigger it and use its output safely.
Statistical Theory Analysisaiming-lab/AutoResearchClaw · MIT Download the skill Quick start
- 1Download the ZIP. Get the statistical-theory-analysis skill ZIP from the download page on this site.
- 2Unzip into a skills folder. Unzip it into ~/.claude/skills/ for a personal install, or into a project's .claude/skills/ folder for a project install.
- 3Or upload in the Claude apps. In the Claude apps, go to Settings, then Capabilities, then Skills, and upload the skill.
- 4State your method and property. Ask in plain language about the method, its assumptions and a property such as consistency, bias, variance or coverage.
- 5Review the proposition. Check the assumptions, proof sketch and limitations, since the output is a draft rather than a verified proof.
- 6Turn claims into experiments. Use the suggested experimental predictions to design simulations that test each theoretical claim.
What an agent skill is and what this one makes the agent do
An agent skill is a folder containing a SKILL.md file. The file describes when and how an AI agent should work on a task. This skill's SKILL.md is written for theory work: it asks the agent to analyze the theoretical properties of a statistical method under a formal formulation.
When the skill is active, the agent can cover identifiability arguments, bias or variance calculations, consistency statements, asymptotic distributions, coverage or calibration arguments, risk or error bounds, robustness analysis, sensitivity or impossibility results, and counterexamples that show failure outside the assumptions.
- Output is laid out as a Proposition, Proof Sketch, Interpretation and Limitations, following the template in the skill.
- Each theoretical claim should be paired with an experimental prediction where possible, such as the direction of a metric change, the condition under which the method should improve, a stress condition where it should fail, or a baseline it should outperform.
Install the skill: download the ZIP and unzip it
Download the ZIP of the skill from the download page on this site. Then choose one of the following locations, depending on whether you want the skill available everywhere or only in one project.
For a personal install that applies across your projects, unzip the skill into ~/.claude/skills/. For a project-only install, unzip it into the .claude/skills/ folder inside that project. The skill folder should contain SKILL.md.
If you use the Claude apps instead of files on disk, go to Settings, then Capabilities, then Skills, and upload the skill there.
~/.claude/skills/Trigger the skill with natural requests
You do not need a special command. The skill's metadata lists trigger keywords including theory, proof, consistency, asymptotic normality, bias, variance, coverage, error bound, identifiability and robustness. Asking in plain language that uses these words is the most reliable way to activate it.
State the method and the property you care about. The more precisely you name the estimator, test or procedure and its assumptions, the more useful the proposition will be.
- Analyze the consistency and asymptotic normality of this estimator under the stated assumptions.
- What is the bias and variance of this shrinkage estimator, and where does it break down?
- Give a proposition and proof sketch for the coverage of this confidence interval, and list its limitations.
- What identifiability problems could this mixture model have?
- Suggest experiments that would test each theoretical claim in my methods section.
Examples of a good result
A useful response follows the structure in the skill. It states assumptions labelled A1 to Ak, a proposition saying what the method satisfies under them, a numbered proof sketch, an interpretation of what the result predicts, and a limitations section saying what the result does not cover.
Treat the output as a structured draft. The skill's own template asks for a proof sketch, not a complete rigorous proof, so the steps are the place to check the reasoning.
What you need and safety notes
The skill lists no requirements. It runs no scripts and makes no network calls, so it works from its instructions alone and the agent reasons in text.
Because the agent may produce plausible but incorrect proofs, do not publish or rely on its mathematical claims without review by a qualified statistician. The skill is not a substitute for checking proofs.
- Do not use it for quick practical data analysis; it focuses on formal theory rather than running code on datasets.
- Verify every assumption and step before it goes into a paper.
Tips for better results
Give the agent the exact assumptions you are willing to make. Results depend heavily on them, and the skill is built around stating assumptions explicitly.
Ask for the counterexample or the scope where the result fails. This is often the most informative part of the output.
Use the experimental predictions as a plan for simulations, which suits a theory section in a simulation paper.
- Name the stage of your project if relevant; the skill is intended after method proposal and before final experimental comparison.
- Ask for the limitations section every time, not only the proposition.
What people use it for
- Checking the assumptions and consistency properties of an estimator before running experiments.
- Drafting the theory section of a simulation or methods paper.
- Listing the identifiability problems a mixture or latent variable model might have.
- Stating the scope where a confidence interval's coverage claim fails, with a counterexample.
- Generating experimental predictions to test each claim in a methods write-up.
Frequently asked questions
Is the Statistical Theory Analysis skill free?
The skill is published under the MIT licence, as listed on this page. Check the licence file in the source repository for the full terms before reuse.
What do I need to install this skill?
The skill lists no requirements. You download the ZIP, unzip it into ~/.claude/skills/ or a project's .claude/skills/ folder, or upload it under Settings, Capabilities, Skills in the Claude apps.
Does this skill run code or access the internet?
No. The skill runs no scripts and uses no network. It works through the agent's reasoning on the instructions in SKILL.md.
Can I use it to analyze my dataset?
Not well. The skill focuses on formal theory rather than running code on datasets, so use a practical data analysis tool for that work.
Can I trust the proofs it writes?
Not without checking. The agent may produce plausible but incorrect proofs, so have a qualified statistician review any claim before publishing or relying on it.
How is this different from asking a general chat assistant about statistics?
The skill gives the agent a fixed structure: assumptions, a proposition, a proof sketch, interpretation, limitations and experimental predictions. That structure is what makes the output easier to review.
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Our verdict
This skill suits researchers, students and paper authors who want a structured theoretical analysis of a statistical method before running experiments. It is a poor fit for practical data analysis, and anyone publishing its mathematics should have a qualified statistician check the proofs first.
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