stat-result-validator
GitHub用于验证统计研究结果的完整性与一致性,检查从问题定义、方法、理论到实验证据及最终结论的完整链条质量。
Trigger Scenarios
Install
npx skills add aiming-lab/AutoResearchClaw --skill stat-result-validator -g -y
SKILL.md
Frontmatter
{
"name": "stat-result-validator",
"metadata": {
"category": "domain",
"priority": "1",
"trigger-keywords": "validation,audit,formulation,theory,comparison,claims,statistical sanity,quality gate",
"applicable-stages": "10,11,12,13,14,15,16,17,20"
},
"description": "Validate statistical research outputs for formulation quality, method-to- problem alignment, theory presence, experimental evidence, fair comparison, artifact completeness, and final-claim consistency.\n"
}
Stat Result Validator
Overview
Use this skill after formulation, method proposal, theory, experimental evaluation, comparison, and result synthesis. It checks whether the final result is supported by a coherent statistical research chain.
Artifact Checks
Required for all topics:
progress/<TOPIC_ID>/step0_problem_formulation.md
progress/<TOPIC_ID>/step1_method_proposal.md
progress/<TOPIC_ID>/step2_theory_analysis.md
progress/<TOPIC_ID>/step3_experimental_evaluation.md
progress/<TOPIC_ID>/step4_comparison.md
progress/<TOPIC_ID>/step5_result_synthesis.md
progress/<TOPIC_ID>/step6_quality_audit.md
experiments/<TOPIC_ID>/config.yaml
experiments/<TOPIC_ID>/results/metrics.json
experiments/<TOPIC_ID>/results/run_manifest.json
experiments/<TOPIC_ID>/results/comparison_summary.md
experiments/<TOPIC_ID>/results/claim_verdicts.json
experiments/<TOPIC_ID>/report/paper.md
experiments/<TOPIC_ID>/README.md
Analysis-specific source files and raw outputs are determined by the experiment
plan and should live under experiments/<TOPIC_ID>/src/ and
experiments/<TOPIC_ID>/results/.
Formulation Checks
The formulation must define:
- Observed data and sampling regime
- Data model or data source
- Target parameter, decision, prediction, or risk
- Assumptions
- Claims or hypotheses
- Evaluation criteria
- Theory targets
Blocking failures:
- No target or estimand.
- Claims cannot be measured.
- Assumptions are absent or incompatible with the proposed method.
- Evaluation criteria do not answer the research question.
Method Checks
Verify that:
- The proposed method addresses the formulated target.
- Baselines are meaningful.
- Ablations isolate important design choices.
- Diagnostics are specified for likely failure modes.
- Method outputs match the metrics and theory targets.
Theory Checks
Theory may be rigorous or partial, but it must be explicit.
Check for:
- Definitions and assumptions.
- Proposition, theorem, derivation, counterexample, or clearly labeled heuristic analysis.
- Predicted empirical patterns.
- Limitations and regimes not covered.
Blocking failures:
- No theory section at all.
- Theory analyzes a different target than the formulation.
- Final claims are stronger than the theory supports.
Experimental Evidence Checks
Check that:
- Experiments test the formulated claims.
- Conditions are aligned with assumptions and stress tests.
- Proposed method, baselines, and ablations are run on comparable conditions.
- Failure counts are recorded.
- Runtime reductions are recorded in
run_manifest.json. - Metrics are grouped at the right level.
Comparison Checks
Verify that:
- Comparisons include proposed method vs baseline.
- Ablations are interpreted.
- Experimental patterns are compared with theoretical predictions.
- Disagreements between theory and experiments are discussed.
- Claim verdicts cite both theoretical and empirical support when available.
Final Claim Checks
Every final claim must be traceable to:
formulation -> method -> theory -> experiment -> comparison
Use:
PASS: formulation, theory, experiments, and comparisons support the claims.WARN: usable but has limitations that must be disclosed.FAIL: missing formulation, theory, evidence, or fair comparison prevents a valid conclusion.
Version History
- e2e23c9 Current 2026-07-25 07:47


