Statistical Method Design
Design statistical methods, baselines, diagnostics, variants, and ablations that...
Install
npx skills add aiming-lab/AutoResearchClaw --skill statistical-method-design -g -a claude-code -y- Repository
- aiming-lab/AutoResearchClaw14.8k
- Category
- Design
- Updated
- 3mo ago
- License
- MIT
- Context cost
- Lightabout 236 tokens
- Runs scripts
- No, instructions only
What it does
Design statistical methods, baselines, diagnostics, variants, and ablations that directly address a formal problem formulation.
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:
method_to_claim_map:
proposed_method:
claims: [C1, C2]
expected_evidence: "lower risk under stress condition"
theory_target: "consistency under assumptions A1-A3"
