statistical-method-design
Design statistical methods, baselines, diagnostics, variants, and ablations that directly address a formal problem formulation.
57
66%
Does it follow best practices?
Run evals on this skill
Adds up to 20 points to the overall score
View guide
Passed
No findings from the security scan
Fix and improve this skill with Tessl
tessl review fix ./external/agents/stat_research_agent/skills/statistical-method-design/SKILL.mdStatistical 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"- Repository
- aiming-lab/AutoResearchClaw
- Commit
e2e23c9
- Last updated
- First committed
Is this your skill?
If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.