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validating-models-and-claims

Validate trained models and analytical claims against their intended decision, independent evidence, and human-reviewed ground truth. Use when reviewing model performance, analysis conclusions, launch claims, or evaluation reports; use failure diagnosis instead when the main task is locating a known defect.

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  • SKILL.md4.6 KB
  • agents/openai.yaml261 B
  • assets/validation-report.md3.0 KB
  • references/validation-protocol.md4.9 KB

SKILL.md(原文)

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Validating Models and Claims

Decide what the available evidence actually supports. Treat a metric, chart, or successful run as evidence for a bounded claim, not as proof that the full system works.

Start with the claim

Write each material claim in a falsifiable form before selecting evidence:

  • the decision or action the claim will influence;
  • the population, unit of analysis, prediction horizon, and operating conditions;
  • the comparator and practical improvement threshold;
  • the cost of false positives, false negatives, abstention, and delay;
  • the evidence that would refute or narrow the claim.

If these are unresolved and materially change the evaluation, surface the choice instead of silently selecting a convenient definition.

Build an evidence contract

For every claim, map the claim's scope to evidence of equal scope. A component test cannot establish end-to-end readiness; aggregate accuracy cannot establish subgroup safety; retrospective fit cannot establish future performance.

Separate at least these evidence sources when they exist:

  1. development evidence used to choose features, thresholds, or models;
  2. untouched or independently collected validation evidence;
  3. human-reviewed ground truth and its adjudication record;
  4. operational evidence produced under the real inference path.

Do not describe reused tuning data as independent validation. Record every contact with the evaluation set, including manual error review that influenced a subsequent model choice.

When label correctness depends on expertise or judgment, preserve the human ground-truth process: reviewer qualifications, instructions, blinding, disagreement rate, adjudication, unresolved cases, and sampling frame. Model agreement with noisy labels is not accuracy against reality.

Validate in layers

Use the smallest set of checks that covers the actual claim:

  • Semantic validity: target, timestamps, joins, exclusions, and units mean what the claim assumes.
  • Evaluation integrity: split boundaries, leakage controls, comparator, threshold selection, and metric implementation are valid.
  • Performance: report uncertainty and practical effect size, not only a point estimate.
  • Slices and errors: inspect decision-relevant subgroups, rare cases, temporal drift, missingness patterns, and high-cost errors.
  • Operational fidelity: when the claim includes deployment or operational behavior, exercise the same preprocessing, feature availability, threshold, and output interpretation used in practice. Otherwise record why this layer is not applicable.
  • Claim calibration: narrow the final language to what all completed evidence supports.

Predefine acceptance criteria when possible. If criteria are chosen after observing results, label them exploratory.

Preserve independence

Prefer a validator who did not construct the evaluated artifact. When that is not possible, create procedural independence: freeze the artifact and protocol, record hashes and versions, run the validation from a clean entry point, and keep exceptions visible.

Never overwrite failed or contradictory results. Explain discrepancies or carry them into the decision as unresolved risk.

Produce a decision record

Use assets/validation-report.md for a reviewable deliverable. Include:

  • a claim-to-evidence matrix with status supported, partially supported, unsupported, or not tested;
  • the provenance and independence of each evidence source;
  • uncertainty, slice results, and representative error cases;
  • human ground-truth quality and unresolved disagreements;
  • limitations, rejected interpretations, and the exact supported wording;
  • a decision recommendation with explicit conditions and owner.

For detailed protocol design, human-label review, and scope-matched proof rules, read references/validation-protocol.md.

Stop conditions

Do not declare validation complete while a material claim lacks scope-matched evidence, evaluation independence is unknown, the ground truth is unreviewed where it matters, or a claimed operational path has not been exercised. Report missing proof or a justified not-applicable layer rather than converting absence of evidence into a pass.

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概要と使いどころ

Audit datasets, joins, labels, and ground truth before analysis or modeling. Use when data meaning, row grain, time semantics, source-of-truth reliability, or label construction may invalidate conclusions; not for general model evaluation after the evidence base is already trusted.

日本語の概要は準備中です。原文の説明を表示しています。

aiopshwang/data-analysis-ml-agent-skills122026年8月27日 更新

Design leakage-safe machine learning experiments that mirror real deployment and support fair model comparisons. Use when defining prediction timing, feature eligibility, train-validation-test splits, baselines, metrics, or controlled model iterations; not for auditing whether raw labels are trustworthy.

日本語の概要は準備中です。原文の説明を表示しています。

aiopshwang/data-analysis-ml-agent-skills122026年8月27日 更新

Isolate the root cause of ML performance drops, inconsistent evaluations, prediction errors, and training-serving mismatches across data, labels, splits, pipelines, models, metrics, and runtime behavior. Use when investigating a reproducible failure or regression, not routine model selection or general performance validation.

日本語の概要は準備中です。原文の説明を表示しています。

aiopshwang/data-analysis-ml-agent-skills122026年8月27日 更新

Orchestrate an end-to-end data analysis or machine learning project from decision framing through reproducible handoff. Use when a request spans multiple lifecycle stages or an ambiguous modeling request must become a decision-ready result; use narrower audit or experiment-design skills for isolated reviews.

日本語の概要は準備中です。原文の説明を表示しています。

aiopshwang/data-analysis-ml-agent-skills122026年8月27日 更新

Package completed data analysis and ML work so an independent recipient can reproduce the claimed results, verify artifact lineage, and operate the handoff within its stated scope. Use when finalizing a project, study, model package, or review bundle; not for deploying to a live system.

日本語の概要は準備中です。原文の説明を表示しています。

aiopshwang/data-analysis-ml-agent-skills122026年8月27日 更新

Route data analysis and machine learning work to the right skill in this suite. Use when starting any analysis, modeling, validation, or reproducibility task and the matching specialized skill is not yet clear; not needed when one specific skill already clearly applies.

日本語の概要は準備中です。原文の説明を表示しています。

aiopshwang/data-analysis-ml-agent-skills122026年8月27日 更新

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