Agent Skills
› aiming-lab/AutoResearchClaw
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statistical-theory-analysis
GitHub用于在方法提出后、实验前进行统计理论分析，评估可识别性、偏差、一致性等性质，并生成定理模板与实证预测。
Trigger Scenarios
需要对提出的统计方法进行形式化理论分析
需要推导方法的渐近分布或误差界限
Install
npx skills add aiming-lab/AutoResearchClaw --skill statistical-theory-analysis -g -y
SKILL.md
Frontmatter
{
"name": "statistical-theory-analysis",
"metadata": {
"category": "domain",
"priority": "1",
"trigger-keywords": "theory,proof,consistency,asymptotic normality,bias,variance,coverage,error bound,identifiability,robustness",
"applicable-stages": "4,5,6,7,8,9,10"
},
"description": "Analyze theoretical properties of statistical methods under the formal formulation: identifiability, bias, variance, consistency, asymptotics, coverage, error bounds, robustness, and limitations.\n"
}
Statistical Theory Analysis
Overview
Use this skill after method proposal and before final experimental comparison. Theory is required as a stage even if the final output is a simulation paper.
Theory Outputs
Depending on the topic, provide:
- Identifiability argument
- Bias or variance calculation
- Consistency statement
- Asymptotic distribution
- Coverage or calibration argument
- Risk or error bound
- Robustness analysis
- Sensitivity or impossibility result
- Counterexample showing failure outside assumptions
Theorem Template
## Proposition
Under assumptions A1-Ak, method M satisfies ...
## Proof Sketch
1. ...
2. ...
3. ...
## Interpretation
This predicts that ...
## Limitations
The result does not cover ...
Experimental Predictions
Every theoretical claim should produce an empirical prediction when possible:
- Direction of metric change
- Condition under which the method should improve
- Stress condition under which it should fail
- Baseline it should outperform
Version History
- e2e23c9 Current 2026-07-25 07:48
