sumit11402/knights-ae-02/statistical-experimental-evaluation

Claude Code and agent skill by sumit11402/knights-ae-02

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Design and run statistical experiments that test the formal problem, proposed methods, theoretical predictions, baselines, and ablations.

Install

npx vskill@latest install sumit11402/knights-ae-02/statistical-experimental-evaluation

SKILL.md

The same SKILL.md was published earlier as aiming-lab/autoresearchclaw/statistical-experimental-evaluation.

Statistical Experimental Evaluation

Overview

Use this skill after formulation, method proposal, and theory. Experiments should test specific claims and theoretical predictions.

Experiment Plan

Define:

  • Conditions or data-generating processes
  • Real data source or synthetic data generator
  • Sample sizes, folds, repetitions, seeds, or resamples
  • Proposed method
  • Baselines
  • Ablations
  • Diagnostics
  • Metrics
  • Failure accounting

Required Artifacts

text
experiments/<TOPIC_ID>/config.yaml
experiments/<TOPIC_ID>/src/
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

Evidence Schema

Use a row-oriented metric format:

json
{
  "topic_id": "TXX",
  "metric_rows": [
    {
      "claim_id": "C1",
      "method": "proposed_method",
      "baseline": "standard_method",
      "condition": "stress_condition",
      "metric": "risk",
      "value": 0.12,
      "status": "ok"
    }
  ]
}

Claim verdicts should connect theory and experiments:

json
[
  {
    "claim_id": "C1",
    "verdict": "supported",
    "theory_support": "Proposition 1 under A1-A3",
    "experimental_support": "Proposed method has lower risk in conditions X-Y",
    "comparison": "Outperforms baseline B on metric M",
    "limitations": "Finite sample only; assumption A2 not tested"
  }
]

Evidence Rules

  • A metric must map to a formulated claim.
  • A comparison must use the same data conditions across methods.
  • Failed runs must be counted.
  • Runtime reductions must be recorded.
  • Results must be interpreted against theoretical predictions.

Security

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  • Model reviewPassed

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Quality evaluation

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Source

Popularity

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2
7-day trend score
5

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