Statistical Method Design
Researchstatistical-method-designStatistical Method Design is an Agent Skill for research published in the aiming-lab/AutoResearchClaw repository on GitHub: a SKILL.md instruction file an AI agent can load to do this task.
Design statistical methods, baselines, diagnostics, variants, and ablations that directly address a formal problem formulation.
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How to use this skill
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Prompt to paste
Inspect the Agent Skill at https://github.com/aiming-lab/AutoResearchClaw/blob/HEAD/external/agents/stat_research_agent/skills/statistical-method-design/SKILL.md and all supporting files. Report provenance, permissions, scripts, dependencies, and security risks. Propose an exact installation into .agents/skills/statistical-method-design, but wait for my approval before writing files or running scripts.
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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"