statistical-method-design
Design statistical methods, baselines, diagnostics, variants, and ablations that directly address a formal problem formulation.
Source facts
- Repository
- aiming-lab/AutoResearchClaw
- Last source activity
- May 20, 2026 at 04:39
- Detected SKILL.md language
- English
- Stars
- 14,678
- Forks
- 1,709
Install options
The review-first prompt is selected by default. You can switch to a direct command or download a local copy.
Review the source files
Read SKILL.md and any companion files shown by SkillsMP before deciding whether to install.
Showing SKILL.md
SKILL.md
Source instructions · Read-only preview- name
- statistical-method-design
- description
- Design statistical methods, baselines, diagnostics, variants, and ablations that directly address a formal problem formulation.
- metadata
- {"category":"domain","trigger-keywords":"method proposal,estimator,algorithm,baseline,ablation,diagnostic,statistical method","applicable-stages":"3,4,5,6,7,8","priority":"1"}
# 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"
```
View on GitHub