Statistical Theory Analysis
Analyze the theoretical properties of statistical methods: bias, variance, consistency, coverage, and more.
- Repository
- aiming-lab/AutoResearchClaw
- Stars
- 14,802
- Licence
- MIT
- Files
- 1 file
What it does
This skill tells an AI agent to reason about statistical methods in a formal, mathematical way. When asked about a method, the agent looks at properties such as identifiability, bias and variance, consistency, asymptotic behaviour, coverage, error bounds, robustness and limitations. It can then lay out a structured proposition with assumptions, a proof sketch, an interpretation and the scope where the result does not hold. It also suggests experiments that would test each theoretical claim.
Great for
- Researchers who need a structured theoretical analysis of a proposed statistical method before running experiments
- Students checking the assumptions and properties behind an estimator or test
- Authors preparing the theory section of a simulation or methods paper
Skip it if
- Quick practical data analysis, since it focuses on formal theory rather than running code on datasets (it is not a substitute for checking proofs by a qualified statistician)
- Situations where the agent's mathematical claims will be published or relied on without expert review, because the agent may produce plausible but incorrect proofs
Try asking
- “Analyze the consistency and asymptotic normality of this estimator under the stated assumptions.”
- “What are the bias and variance trade-offs of this regularised method, and where does coverage fail?”
- “Draft a proposition and proof sketch for the robustness of our proposed estimator to outliers.”
- “Suggest empirical predictions that would test the theoretical claims about our method.”
Install in 3 steps
- 1
Download the skill
The ZIP contains the 1 original files, the MIT licence and an attribution note.
statistical-theory-analysis.zip - 2
Add it to your agent
Claude Code (all projects)
mkdir -p ~/.claude/skills && unzip statistical-theory-analysis.zip -d ~/.claude/skills/Claude Code / Agent SDK (this project only)
mkdir -p .claude/skills && unzip statistical-theory-analysis.zip -d .claude/skills/Claude apps (web/desktop): Settings → Capabilities → Skills → Upload skill, and choose the ZIP.
- 3
Ask for the task
No special command needed — the agent reads the skill's description and loads it when your request matches. Try one of the prompts above.
Get better results
- State your model, assumptions and estimator precisely so the agent reasons from concrete definitions.
- Ask for the explicit assumptions and limitations section, and check each assumption against your setting.
- Treat the proof sketch as a starting point and verify key steps independently or with a statistician.
What's inside
- SKILL.md
Redistributed unmodified from aiming-lab/AutoResearchClaw under the MIT licence. The assessment was drafted with AI from the skill's files on 2026-10-29. Skills can instruct your agent to run code — read SKILL.md and any scripts before installing.